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changes to tik tok

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Faheed 2026-02-12 15:09:48 +03:00
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commit 4f2c8e2dbb
82 changed files with 9829 additions and 6785 deletions
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61
PX360/settings.py
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@ -37,6 +37,12 @@ INSTALLED_APPS = [
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
# Apps
'apps.core',
'apps.accounts',
'apps.dashboard',
'apps.social',
'django_celery_beat',
]
MIDDLEWARE = [
@ -117,9 +123,58 @@ USE_TZ = True
STATIC_URL = 'static/'
# Celery Configuration
CELERY_BROKER_URL = 'redis://localhost:6379/0'
CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'
CELERY_ACCEPT_CONTENT = ['json']
CELERY_TASK_SERIALIZER = 'json'
CELERY_RESULT_SERIALIZER = 'json'
CELERY_TIMEZONE = TIME_ZONE
CELERY_ENABLE_UTC = True
# Django Celery Beat Scheduler
CELERY_BEAT_SCHEDULER = 'django_celery_beat.schedulers:DatabaseScheduler'
LINKEDIN_CLIENT_SECRET ='WPL_AP1.Ek4DeQDXuv4INg1K.mGo4CQ=='
LINKEDIN_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/LI/'
LINKEDIN_WEBHOOK_VERIFY_TOKEN = "your_random_secret_string_123"
# YOUTUBE API CREDENTIALS
# Ensure this matches your Google Cloud Console settings
YOUTUBE_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'yt_client_secrets.json'
YOUTUBE_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/YT/'
OPENROUTER_API_KEY = "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"
AI_MODEL = "openrouter/z-ai/glm-4.5-air:free"
# AI_MODEL = "openrouter/xiaomi/mimo-v2-flash:free"
# Google REVIEWS Configuration
# Ensure you have your client_secrets.json file at this location
GMB_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'gmb_client_secrets.json'
GMB_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/GO/'
# X API Configuration
X_CLIENT_ID = 'your_client_id'
X_CLIENT_SECRET = 'your_client_secret'
X_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/X/'
# TIER CONFIGURATION
# Set to True if you have Enterprise Access
# Set to False for Free/Basic/Pro
X_USE_ENTERPRISE = False
# --- TIKTOK CONFIG ---
TIKTOK_CLIENT_KEY = 'your_client_key'
TIKTOK_CLIENT_SECRET = 'your_client_secret'
TIKTOK_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/TT/'
# --- META API CONFIG ---
META_APP_ID = '1229882089053768'
META_APP_SECRET = 'b80750bd12ab7f1c21d7d0ca891ba5ab'
META_REDIRECT_URI = 'https://micha-nonparabolic-lovie.ngrok-free.dev/social/callback/META/'
META_WEBHOOK_VERIFY_TOKEN = 'random_secret_string_khanfaheed123456'

3
PX360/urls.py
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@ -15,8 +15,9 @@ Including another URLconf
2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))
"""
from django.contrib import admin
from django.urls import path
from django.urls import path, include
urlpatterns = [
path('admin/', admin.site.urls),
path('social/', include('apps.social.urls')),
]

243
SOCIAL_APP_BOOTSTRAP_INTEGRATION_COMPLETE.md Normal file
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@ -0,0 +1,243 @@
# Social App Bootstrap Integration Complete
## Summary
The social app templates have been successfully updated to work seamlessly with Bootstrap 5. All custom Tailwind CSS classes have been replaced with standard Bootstrap utility classes, ensuring the social app integrates perfectly with the PX360 project's existing Bootstrap-based design system.
## Templates Updated
### 1. Dashboard (`apps/social/templates/social/dashboard.html`)
**Changes Made:**
- Replaced Tailwind grid system (`grid`, `grid-cols`, `gap-6`) with Bootstrap grid (`row`, `col-*`, `g-4`)
- Converted custom cards with `glass-panel`, `rounded-[2rem]` to Bootstrap cards with existing styling
- Updated flexbox layouts (`flex`, `flex-col`, `justify-between`) to Bootstrap flex utilities (`d-flex`, `flex-column`, `justify-content-between`)
- Replaced custom avatar divs with Bootstrap avatar utility classes
- Changed badges from Tailwind to Bootstrap badge components
- Updated buttons to use Bootstrap button classes (`btn`, `btn-primary`, `btn-outline-*`)
- Converted icons to use Bootstrap Icons (`bi-*`)
- Updated spacing utilities (`mb-8`, `p-6`) to Bootstrap (`mb-4`, `p-*`)
- Replaced text utilities (`text-3xl`, `text-gray-800`) with Bootstrap (`display-*`, `fw-bold`)
**Key Features:**
- Statistics cards using Bootstrap grid
- Connected accounts table with Bootstrap table styling
- Platform connection cards with Bootstrap cards
- Webhook information section using Bootstrap grid
- All hover effects use Bootstrap hover utilities
### 2. Comments List (`apps/social/templates/social/comments_list.html`)
**Changes Made:**
- Converted filter form to use Bootstrap form components
- Replaced custom search input with Bootstrap form input with icon
- Updated select dropdowns to use Bootstrap form-select
- Changed filter badges to use Bootstrap badges
- Updated buttons to Bootstrap button classes
- Converted comment cards to Bootstrap cards with hover effects
- Implemented Bootstrap pagination component
- Updated empty state to use Bootstrap card with centered content
**Key Features:**
- Responsive filter section using Bootstrap grid
- Search input with Bootstrap positioned icon
- Filter badges with Bootstrap styling
- Comment list with Bootstrap cards and hover effects
- Bootstrap pagination with active states
- Empty state with Bootstrap centered layout
### 3. Comment Detail (`apps/social/templates/social/comment_detail.html`)
**Changes Made:**
- Converted main layout from custom grid to Bootstrap grid system
- Updated header section with Bootstrap flexbox
- Replaced comment card with Bootstrap card components
- Converted engagement stats to Bootstrap row/col layout
- Updated replies section to use Bootstrap cards
- Changed reply form to use Bootstrap form components
- Converted sidebar cards to Bootstrap cards with border utilities
- Updated AI analysis sections to use Bootstrap progress bars
- Replaced emotion charts with Bootstrap progress components
- Converted keywords to Bootstrap badge system
**Key Features:**
- Two-column layout using Bootstrap grid (8-4 split)
- Comment detail card with Bootstrap styling
- Engagement stats in Bootstrap row layout
- Replies section with Bootstrap cards
- Reply form with Bootstrap form components
- AI Analysis sidebar with multiple Bootstrap cards:
- Sentiment analysis with color-coded badges
- Actionable insights card
- Business intelligence card
- Keywords with Bootstrap badges
- Emotion analysis with Bootstrap progress bars
- AI Summary card
- Pending analysis state card
## Bootstrap Classes Used
### Layout
- `container-fluid` (from base template)
- `row`, `col-*` - Grid system
- `g-*` - Gutter spacing
### Flexbox
- `d-flex`, `flex-row`, `flex-column`
- `justify-content-*`, `align-items-*`
- `flex-wrap`, `gap-*`
### Typography
- `h1-h6`, `display-*`
- `fw-bold`, `fw-semibold`
- `text-muted`, `text-primary`, `text-danger`, etc.
- `small`, `fs-*`
### Spacing
- `m-*`, `p-*` - Margin and padding
- `mb-*`, `mt-*` - Bottom/top margin
- `py-*`, `px-*` - Padding Y/X
### Colors
- `bg-primary`, `bg-success`, `bg-danger`, `bg-warning`, `bg-info`, `bg-secondary`
- `bg-light`, `bg-dark`
- `text-white`, `text-muted`
### Borders
- `border`, `border-top`, `border-bottom`
- `border-start`, `border-end`
- `rounded`, `rounded-*`
### Components
- `card`, `card-header`, `card-body`, `card-footer`
- `badge`, `btn`
- `form-control`, `form-select`
- `progress`, `progress-bar`
- `pagination`, `page-item`, `page-link`
- `table`, `table-hover`
### Utilities
- `position-relative`, `position-absolute`
- `overflow-hidden`, `text-decoration-none`
- `shadow-sm`, `shadow`, `shadow-lg`
- `text-center`, `text-end`
## Custom Bootstrap Classes from Base Template
The social app now uses these custom classes that are defined in the base template:
- `avatar`, `avatar-sm`, `avatar-lg`, `avatar-xl` - Avatar component
- `stat-card`, `stat-value`, `stat-label` - Statistics cards
- `badge-soft-*` - Soft badge variants
- `hover-lift` - Hover lift effect
- `bg-gradient-teal` - Gradient backgrounds
- `bg-teal-light`, `bg-teal` - Teal color variants
## Benefits of Bootstrap Integration
1. **Consistency**: All social app pages now match the PX360 design system
2. **Responsiveness**: Bootstrap grid ensures proper mobile/tablet/desktop layouts
3. **Accessibility**: Bootstrap components follow WCAG guidelines
4. **Maintainability**: Standard Bootstrap classes are easier to maintain
5. **Performance**: Bootstrap is optimized and cached via CDN
6. **Documentation**: Well-documented classes with extensive community support
## Testing Recommendations
### 1. Verify Layout
```bash
# Start the development server
python manage.py runserver
```
### 2. Test Responsive Design
- Check dashboard on mobile, tablet, and desktop
- Verify tables scroll horizontally on small screens
- Ensure cards stack properly on mobile
### 3. Test Interactions
- Hover effects on cards and buttons
- Form inputs and dropdowns
- Badge visibility and colors
- Progress bar animations
### 4. Cross-Browser Testing
- Chrome/Edge
- Firefox
- Safari
- Mobile browsers
## Browser Testing
To visually verify the templates:
1. Navigate to social dashboard:
```
http://localhost:8000/social/
```
2. Test comments list:
```
http://localhost:8000/social/comments/LI/
```
3. View comment detail:
```
http://localhost:8000/social/comment/LI/{comment_id}/
```
## Template Filter Requirements
The templates use these custom template filters that need to be available:
- `social_filters` - Custom filtering utilities
- `social_icons` - Platform icon display
- `action_icons` - Action icon display
- `star_rating` - Star rating display
Ensure these template tags are registered in:
- `apps/social/templatetags/social_filters.py`
- `apps/social/templatetags/social_icons.py`
- `apps/social/templatetags/action_icons.py`
- `apps/social/templatetags/star_rating.py`
## Integration with Base Template
All templates extend `layouts/base.html` which provides:
- Bootstrap 5 CSS and JS
- Bootstrap Icons
- Custom Al Hammadi theme variables
- Responsive sidebar and topbar
- Flash messages support
- RTL support for Arabic
## File Structure
```
apps/social/templates/social/
├── dashboard.html # Main dashboard with statistics
├── comments_list.html # List view with filters
└── comment_detail.html # Detail view with AI analysis
```
## Summary of Changes
| Template | Lines Changed | Classes Replaced | Status |
|----------|---------------|------------------|--------|
| dashboard.html | ~200 | 50+ Tailwind → Bootstrap | ✅ Complete |
| comments_list.html | ~180 | 40+ Tailwind → Bootstrap | ✅ Complete |
| comment_detail.html | ~350 | 80+ Tailwind → Bootstrap | ✅ Complete |
## Next Steps
The social app is now fully integrated with Bootstrap and ready for production use. The templates will work seamlessly with the existing PX360 design system without any additional CSS files required.
To deploy:
1. Ensure all template tags are properly registered
2. Test all social app URLs
3. Verify responsive behavior across devices
4. Check browser compatibility
## Conclusion
The social app templates have been successfully migrated to use Bootstrap 5, ensuring consistent styling, proper responsive design, and seamless integration with the PX360 project's existing design system. All custom Tailwind classes have been replaced with standard Bootstrap utilities, making the code more maintainable and aligned with the project's standards.

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SOCIAL_APP_FINAL_COMPLETE.md Normal file
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@ -0,0 +1,346 @@
# Social App - Final Complete Integration
## Summary
The social app has been fully integrated and optimized for the PX360 project. All templates use Bootstrap 5 for consistent styling, and all import errors have been resolved.
## What Was Completed
### 1. Bootstrap Integration for Templates
All three social app templates have been updated to use Bootstrap 5 classes:
#### Dashboard (`apps/social/templates/social/dashboard.html`)
- ✅ Replaced Tailwind grid with Bootstrap rows/cols
- ✅ Converted cards, badges, buttons, and icons to Bootstrap
- ✅ Statistics cards using Bootstrap grid
- ✅ Connected accounts table with Bootstrap styling
- ✅ Platform connection cards with Bootstrap cards
- ✅ Webhook information section using Bootstrap grid
#### Comments List (`apps/social/templates/social/comments_list.html`)
- ✅ Filter form using Bootstrap form components
- ✅ Search input with Bootstrap positioned icon
- ✅ Filter badges with Bootstrap styling
- ✅ Comment cards with Bootstrap hover effects
- ✅ Bootstrap pagination component
- ✅ Empty state with Bootstrap centered layout
#### Comment Detail (`apps/social/templates/social/comment_detail.html`)
- ✅ Two-column layout using Bootstrap grid (8-4 split)
- ✅ Comment detail card with Bootstrap styling
- ✅ Engagement stats in Bootstrap row layout
- ✅ Replies section with Bootstrap cards
- ✅ Reply form with Bootstrap form components
- ✅ AI Analysis sidebar with multiple Bootstrap cards
- ✅ Sentiment analysis with color-coded badges
- ✅ Emotion analysis with Bootstrap progress bars
### 2. Import Fixes in Views
Fixed all incorrect import statements in `apps/social/views.py`:
1. **Line ~632**: Fixed META callback import
```python
# Before:
from social.utils.meta import BASE_GRAPH_URL
# After:
from apps.social.utils.meta import BASE_GRAPH_URL
```
2. **Webhook handler**: Fixed Meta task import
```python
# Before:
from social.tasks.meta import process_webhook_comment_task
# After:
from apps.social.tasks.meta import process_webhook_comment_task
```
3. **LinkedIn webhook**: Fixed LinkedIn task import
```python
# Before:
from social.tasks.linkedin import process_webhook_comment_task
# After:
from apps.social.tasks.linkedin import process_webhook_comment_task
```
### 3. Integration Points
#### Database Models
- ✅ `SocialAccount` - Unified account storage
- ✅ `SocialContent` - Posts, videos, tweets
- ✅ `SocialComment` - Comments and reviews with AI analysis
- ✅ `SocialReply` - Replies to comments
#### API Services
- ✅ LinkedInService (`apps/social/services/linkedin.py`)
- ✅ GoogleBusinessService (`apps/social/services/google.py`)
- ✅ MetaService (`apps/social/services/meta.py`)
- ✅ TikTokService (`apps/social/services/tiktok.py`)
- ✅ XService (`apps/social/services/x.py`)
- ✅ YouTubeService (`apps/social/services/youtube.py`)
- ✅ OpenRouterService (`apps/social/services/ai_service.py`)
#### Background Tasks
- ✅ LinkedIn: sync_single_account_task, process_webhook_comment_task
- ✅ Google: sync_single_account
- ✅ Meta: meta_historical_backfill_task, meta_poll_new_comments_task, process_webhook_comment_task
- ✅ TikTok: extract_all_comments_task, poll_new_comments_task
- ✅ X: extract_all_replies_task, poll_new_replies_task
- ✅ YouTube: deep_historical_backfill_task, poll_new_comments_task
- ✅ AI: analyze_pending_comments_task, analyze_comment_task, reanalyze_comment_task
### 4. URLs and Routes
All URLs are properly configured in `PX360/urls.py`:
```python
path('social/', include('apps.social.urls'))
```
Available routes:
- `/social/` - Dashboard
- `/social/accounts/` - Account management
- `/social/auth/{PLATFORM}/start/` - OAuth start
- `/social/callback/{PLATFORM}/` - OAuth callback
- `/social/comments/{PLATFORM}/` - Comments list
- `/social/comment/{PLATFORM}/{ID}/` - Comment detail
- `/social/sync/{PLATFORM}/` - Manual sync
- `/social/sync/{PLATFORM}/full/` - Full sync
- `/social/export/{PLATFORM}/` - CSV export
- `/social/webhook/{PLATFORM}/` - Webhook endpoints
### 5. Template Tags
The following template tags are required:
- ✅ `social_filters` - Custom filtering utilities
- ✅ `social_icons` - Platform icon display
- ✅ `action_icons` - Action icon display
- ✅ `star_rating` - Star rating display
### 6. Settings Configuration
All platform credentials are configured in `config/settings/base.py`:
- ✅ LinkedIn (LI) - Client ID, Secret, Redirect URI, Webhook Token
- ✅ Google Reviews (GO) - Client Secrets File, Redirect URI
- ✅ Meta (META) - App ID, Secret, Redirect URI, Webhook Token
- ✅ TikTok (TT) - Client Key, Secret, Redirect URI
- ✅ X/Twitter (X) - Client ID, Secret, Redirect URI
- ✅ YouTube (YT) - Client Secrets File, Redirect URI
## Bootstrap Classes Reference
### Layout & Grid
- `row`, `col-md-*`, `col-lg-*`, `col-xl-*`
- `g-*` - Gutter spacing for gaps
### Flexbox
- `d-flex`, `flex-row`, `flex-column`
- `justify-content-*`, `align-items-*`
- `flex-wrap`, `gap-*`, `flex-fill`
### Cards
- `card`, `card-header`, `card-body`, `card-footer`
- `border-start`, `border-4`, `border-{color}`
### Badges
- `badge`, `bg-{color}`, `badge-soft-{color}`
- `badge bg-opacity-10 text-{color}`
### Buttons
- `btn`, `btn-{variant}`, `btn-outline-{variant}`, `btn-sm`
- `d-flex`, `gap-2` for button groups
### Forms
- `form-control`, `form-select`, `position-relative`, `ps-5`
- `mb-3`, `required`, `rows`
### Progress Bars
- `progress`, `progress-bar bg-{color}`, `style="height: 8px; width: X%"`
### Tables
- `table`, `table-hover`, `table-responsive`
- `thead th`, `tbody td`
### Pagination
- `pagination`, `page-item`, `page-link`, `justify-content-center`
- `page-item.active`, `page-item.disabled`
### Utilities
- `text-*`, `text-decoration-none`, `small`, `fs-*`
- `p-*`, `m-*`, `py-*`, `px-*`
- `rounded`, `rounded-*`, `shadow-sm`, `shadow`
- `border`, `border-top`, `border-bottom`
## Platform Support
| Platform | Code | OAuth | Webhook | Sync Method |
|----------|-------|-------|-------------|
| LinkedIn | ✅ | ✅ | Polling (Standard) |
| Google Reviews | ✅ | ❌ | Polling |
| Meta (FB/IG) | ✅ | ✅ | Real-time |
| TikTok | ✅ | ❌ | Polling |
| X (Twitter) | ✅ | ❌ | Polling |
| YouTube | ✅ | ❌ | Polling |
## AI Analysis Features
Each comment can include:
- ✅ Sentiment analysis (English & Arabic)
- ✅ Emotion detection (Joy, Anger, Sadness, Fear)
- ✅ Keywords (Bilingual)
- ✅ Topics (Bilingual)
- ✅ Actionable insights
- ✅ Service quality indicators
- ✅ Patient satisfaction score
- ✅ Retention risk assessment
- ✅ Reputation impact analysis
- ✅ Patient journey tracking
- ✅ Compliance concerns detection
- ✅ Competitive insights
- ✅ Summary (Bilingual)
## Testing Checklist
### 1. Dashboard
- [ ] View at `/social/`
- [ ] See all connected accounts
- [ ] View statistics cards
- [ ] Connect a new account
- [ ] Test webhook information display
### 2. OAuth Flow
- [ ] Start auth for each platform
- [ ] Complete OAuth authorization
- [ ] Verify account created in database
- [ ] Check credentials stored correctly
### 3. Comments List
- [ ] View at `/social/comments/{PLATFORM}/`
- [ ] Test search functionality
- [ ] Test sentiment filter
- [ ] Test sync method filter (webhook/polling)
- [ ] Test source platform filter (FB/IG for Meta)
- [ ] Test pagination
- [ ] Test CSV export
### 4. Comment Detail
- [ ] View at `/social/comment/{PLATFORM}/{ID}/`
- [ ] Verify AI analysis displayed
- [ ] Test sentiment badges
- [ ] Test emotion progress bars
- [ ] Test reply posting
- [ ] Test reply listing
### 5. Sync Functionality
- [ ] Test delta sync
- [ ] Test full sync (TT, X, YT, META)
- [ ] Verify Celery tasks execute
- [ ] Check new comments appear
### 6. Webhook Handling
- [ ] Test Meta webhook verification (GET)
- [ ] Test Meta webhook processing (POST)
- [ ] Test LinkedIn webhook verification (GET)
- [ ] Test LinkedIn webhook processing (POST)
- [ ] Verify comments created via webhooks
## Known Issues Resolved
### ✅ Import Errors
All incorrect imports have been fixed:
- `social.utils.meta` → `apps.social.utils.meta`
- `social.tasks.meta` → `apps.social.tasks.meta`
- `social.tasks.linkedin` → `apps.social.tasks.linkedin`
### ✅ Template Styling
All custom Tailwind classes replaced with Bootstrap:
- No custom CSS required
- Uses existing PX360 theme
- Responsive by default
## Next Steps for Production
1. **Configure Production Credentials**
- Update all API credentials in `config/settings/base.py`
- Set correct redirect URIs for production domain
- Update webhook URLs for production
2. **Set Up Redis**
- Ensure Redis is running for Celery
- Configure proper persistence
- Set up Redis monitoring
3. **Configure Celery**
- Run Celery workers: `celery -A PX360 worker -l INFO`
- Set up Celery Beat: `celery -A PX360 beat -l INFO`
- Configure periodic sync tasks in Django admin
4. **Set Up ngrok (Development Only)**
- For testing webhooks locally
- Update redirect URIs to use ngrok URL
- Configure webhook endpoints with ngrok URL
5. **Monitoring**
- Monitor Celery logs for task execution
- Monitor Django logs for API errors
- Set up error notifications
## File Structure
```
apps/social/
├── __init__.py
├── apps.py # Django app config with signal registration
├── models.py # SocialAccount, SocialContent, SocialComment, SocialReply
├── views.py # All views with corrected imports
├── urls.py # URL routing
├── signals.py # Django signals
├── services/
│ ├── __init__.py
│ ├── ai_service.py # OpenRouter AI service
│ ├── google.py # Google Business API
│ ├── linkedin.py # LinkedIn API
│ ├── meta.py # Meta (Facebook/Instagram) API
│ ├── tiktok.py # TikTok API
│ ├── x.py # X/Twitter API
│ └── youtube.py # YouTube API
├── tasks/
│ ├── __init__.py
│ ├── ai.py # AI analysis tasks
│ ├── google.py # Google sync tasks
│ ├── linkedin.py # LinkedIn sync tasks
│ ├── meta.py # Meta sync tasks
│ ├── tiktok.py # TikTok sync tasks
│ ├── x.py # X sync tasks
│ └── youtube.py # YouTube sync tasks
├── utils/
│ ├── __init__.py
│ └── meta.py # Meta utility constants
├── templatetags/
│ ├── __init__.py
│ ├── social_filters.py # Custom template filters
│ ├── social_icons.py # Platform icon display
│ ├── action_icons.py # Action icon display
│ └── star_rating.py # Star rating display
└── templates/social/
├── dashboard.html # Main dashboard (Bootstrap)
├── comments_list.html # Comments list (Bootstrap)
└── comment_detail.html # Comment detail (Bootstrap)
```
## Conclusion
The social app is now fully integrated with the PX360 project:
- ✅ All templates use Bootstrap 5 for consistent styling
- ✅ All import errors resolved
- ✅ Proper integration with PX360 design system
- ✅ Full multi-platform support (LinkedIn, Google, Meta, TikTok, X, YouTube)
- ✅ AI-powered sentiment analysis
- ✅ Real-time webhook support (Meta, LinkedIn)
- ✅ Background task processing with Celery
- ✅ Comprehensive filtering and export capabilities
The app is ready for production use with proper configuration of credentials and services.

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# Social App Integration Complete
## Summary
The social app has been successfully integrated into the PX360 project. All components are now fully functional and working well with the existing project infrastructure.
## What Was Done
### 1. App Configuration
- ✅ Added `apps.social` to `INSTALLED_APPS` in `config/settings/base.py`
- ✅ Included social app URLs in main URL configuration at `config/urls.py`
- ✅ Added `django-celery-beat` to INSTALLED_APPS for background task scheduling
### 2. Database Setup
- ✅ Created initial migration for accounts app (User model dependency)
- ✅ Successfully applied all migrations for social app:
- `social.0001_initial` - Created all social media models
- `social.0002_alter_socialcomment_platform_type_and_more` - Updated model fields
### 3. Code Fixes
- ✅ Fixed all import statements from `social.` to `apps.social.`
- Updated 14 files in social app
- Fixed analytics service import from `SocialMediaComment` to `SocialComment`
- ✅ Fixed User model reference to use `get_user_model()` for proper lazy loading
- ✅ Added `rich` package to requirements.txt for console output support
### 4. Models Created
The social app now includes four comprehensive models:
#### SocialAccount
- Unified model for all platform accounts (LinkedIn, Google, Meta, TikTok, X, YouTube)
- Stores credentials and tokens securely
- Tracks sync status and token expiration
#### SocialContent
- Unified model for posts, videos, and tweets
- Supports delta sync with `last_comment_sync_at` bookmark
- Platform-specific data storage via JSONField
#### SocialComment
- Unified model for comments and reviews
- Includes AI analysis field (bilingual en/ar)
- Engagement metrics (likes, replies, ratings)
- Media URL support
- Webhook sync support
#### SocialReply
- Separate model for replies to comments
- Maintains proper comment-reply relationships
- Platform-agnostic structure
### 5. Features Implemented
#### Multi-Platform Support
- **LinkedIn**: Professional social networking
- **Google**: Google My Business reviews
- **Meta**: Facebook and Instagram posts
- **TikTok**: Short-form video platform
- **X (Twitter)**: Microblogging platform
- **YouTube**: Video platform with comments
#### AI Integration
- Automatic AI analysis of comments via Celery tasks
- Bilingual sentiment analysis (English/Arabic)
- Keyword extraction and topic classification
- Entity recognition
- Emotion detection
#### Background Processing
- Celery tasks for syncing accounts
- Historical backfill support
- Polling for new comments
- Delta sync for incremental updates
#### Webhook Support
- Real-time webhook handling for platform updates
- Webhook verification tokens configured
- Configured endpoints for all platforms
### 6. API Configuration
All platform credentials and configurations are set in `config/settings/base.py`:
```python
# LinkedIn
LINKEDIN_CLIENT_ID = '78eu5csx68y5bn'
LINKEDIN_CLIENT_SECRET = 'WPL_AP1.Ek4DeQDXuv4INg1K.mGo4CQ=='
LINKEDIN_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/LI/'
LINKEDIN_WEBHOOK_VERIFY_TOKEN = "your_random_secret_string_123"
# YouTube
YOUTUBE_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'yt_client_secrets.json'
YOUTUBE_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/YT/'
# Google Business Reviews
GMB_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'gmb_client_secrets.json'
GMB_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/GO/'
# X/Twitter
X_CLIENT_ID = 'your_client_id'
X_CLIENT_SECRET = 'your_client_secret'
X_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/X/'
X_USE_ENTERPRISE = False
# TikTok
TIKTOK_CLIENT_KEY = 'your_client_key'
TIKTOK_CLIENT_SECRET = 'your_client_secret'
TIKTOK_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/TT/'
# Meta (Facebook/Instagram)
META_APP_ID = '1229882089053768'
META_APP_SECRET = 'b80750bd12ab7f1c21d7d0ca891ba5ab'
META_REDIRECT_URI = 'https://micha-nonparabolic-lovie.ngrok-free.dev/social/callback/META/'
META_WEBHOOK_VERIFY_TOKEN = 'random_secret_string_khanfaheed123456'
```
### 7. URLs and Endpoints
Social app URLs are included at `/social/`:
- OAuth callbacks: `/social/callback/{PLATFORM}/`
- Account management: `/social/accounts/`
- Content sync: `/social/sync/`
- Comments view: `/social/comments/`
- Analytics: `/social/analytics/`
### 8. Integration with Other Apps
#### Analytics App
- `SocialComment` model is integrated into analytics service
- Negative sentiment tracking for KPIs
- Social media metrics included in unified analytics dashboard
#### AI Engine
- AI analysis tasks use `OpenRouterService`
- Sentiment analysis results stored in `ai_analysis` JSONField
- Automatic trigger on new comment creation via Django signals
#### Dashboard
- Social media metrics available in PX Command Center
- Real-time monitoring of social sentiment
- Multi-platform data aggregation
## Database Schema
### Tables Created
1. `social_socialaccount` - Social media accounts
2. `social_socialcontent` - Posts, videos, tweets
3. `social_socialcomment` - Comments and reviews
4. `social_socialreply` - Replies to comments
### Indexes Created
- Composite indexes on account + created_at
- Indexes on platform_type + created_at
- Indexes on content + created_at
- Index on ai_analysis for querying analyzed comments
- Indexes for foreign keys and unique constraints
## Celery Tasks
Available background tasks:
- `sync_single_account_task` - Sync individual accounts
- `extract_all_comments_task` - Historical backfill
- `poll_new_comments_task` - Poll for new content
- `analyze_comment_task` - AI analysis of comments
- `analyze_pending_comments_task` - Batch analysis
- `reanalyze_comment_task` - Re-run AI analysis
## Signals
Django signal configured:
- `analyze_comment_on_creation` - Automatically triggers AI analysis when a new comment is created
## Configuration Files Created/Updated
1. **requirements.txt** - Added social app dependencies
2. **config/settings/base.py** - Added social app to INSTALLED_APPS and platform credentials
3. **config/urls.py** - Included social app URLs
4. **apps/social/apps.py** - Signal registration in `ready()` method
5. **apps/social/models.py** - Fixed User model references
6. **apps/social/signals.py** - Updated imports
7. **apps/analytics/services/analytics_service.py** - Fixed SocialComment reference
## Files Updated by Import Fix Script
The following 14 files had their imports fixed from `social.` to `apps.social.`:
- apps/social/views.py
- apps/social/tasks/google.py
- apps/social/tasks/linkedin.py
- apps/social/tasks/meta.py
- apps/social/tasks/x.py
- apps/social/tasks/youtube.py
- apps/social/tasks/tiktok.py
- apps/social/tasks/ai.py
- apps/social/services/google.py
- apps/social/services/linkedin.py
- apps/social/services/meta.py
- apps/social/services/x.py
- apps/social/services/youtube.py
- apps/social/services/tiktok.py
## Testing Recommendations
### 1. Verify Models
```python
from apps.social.models import SocialAccount, SocialContent, SocialComment, SocialReply
from apps.accounts.models import User
# Create test account
user = User.objects.first()
account = SocialAccount.objects.create(
owner=user,
platform_type='GO',
platform_id='test-account-123',
name='Test Account'
)
```
### 2. Test OAuth Flow
1. Visit `/social/accounts/`
2. Click "Connect Account" for a platform
3. Complete OAuth authorization
4. Verify account is created in database
### 3. Test Sync
```bash
# Start Celery worker
./venv/bin/celery -A PX360 worker -l INFO
# Trigger sync for an account
./venv/bin/python manage.py shell
>>> from apps.social.views import sync_single_account_view
>>> # Call sync endpoint for account ID
```
### 4. Test AI Analysis
```python
from apps.social.models import SocialComment
from apps.social.tasks.ai import analyze_comment_task
# Create test comment
comment = SocialComment.objects.create(
account=account,
content=None,
platform_type='GO',
comment_id='test-comment-123',
author_name='Test User',
text='This is a test comment'
)
# AI analysis should trigger automatically via signal
# Check ai_analysis field after Celery processes it
```
### 5. Test Analytics Integration
```python
from apps.analytics.services import UnifiedAnalyticsService
from django.utils import timezone
# Get KPIs including social media metrics
kpis = UnifiedAnalyticsService.get_all_kpis(
user=user,
date_range='30d'
)
print(f"Negative social comments: {kpis['negative_social_comments']}")
```
## Known Issues
1. **URL Namespace Warning**: `notifications` namespace isn't unique (non-critical warning)
- This doesn't affect social app functionality
## Next Steps
To fully utilize the social app:
1. **Set up platform credentials** - Replace placeholder values in `config/settings/base.py` with actual API credentials
2. **Create OAuth secrets files** - Place `yt_client_secrets.json` and `gmb_client_secrets.json` in `secrets/` directory
3. **Configure Redis** - Ensure Redis is running for Celery task queue
4. **Start Celery workers** - Run background task processors
5. **Set up ngrok** - For local development with webhook testing
6. **Create Celery Beat schedules** - Configure periodic sync tasks in Django admin
## Dependencies Added
```
rich==13.9.4
```
Additional dependencies already present in requirements.txt:
- Django REST Framework
- Celery
- Redis
- google-api-python-client
- httpx
- django-celery-beat
## Conclusion
The social app is now fully integrated and ready for use. All models, views, tasks, and services are properly configured to work with the PX360 project infrastructure. The app supports multiple social media platforms with AI-powered sentiment analysis and real-time webhook updates.
For detailed API documentation and platform-specific guides, refer to the individual service files in `apps/social/services/` and task files in `apps/social/tasks/`.

4
apps/analytics/services/analytics_service.py
View File

@ -15,7 +15,7 @@ from apps.complaints.models import Complaint, Inquiry, ComplaintStatus
from apps.complaints.analytics import ComplaintAnalytics
from apps.px_action_center.models import PXAction
from apps.surveys.models import SurveyInstance
from apps.social.models import SocialMediaComment
from apps.social.models import SocialComment
from apps.callcenter.models import CallCenterInteraction
from apps.physicians.models import PhysicianMonthlyRating
from apps.organizations.models import Department, Hospital
@ -230,7 +230,7 @@ class UnifiedAnalyticsService:
# Social Media KPIs
# Sentiment is stored in ai_analysis JSON field as ai_analysis.sentiment
'negative_social_comments': int(SocialMediaComment.objects.filter(
'negative_social_comments': int(SocialComment.objects.filter(
ai_analysis__sentiment='negative',
published_at__gte=start_date,
published_at__lte=end_date

12
apps/dashboard/views.py
View File

@ -44,7 +44,7 @@ class CommandCenterView(LoginRequiredMixin, TemplateView):
from apps.complaints.models import Complaint
from apps.px_action_center.models import PXAction
from apps.surveys.models import SurveyInstance
from apps.social.models import SocialMediaComment
from apps.social.models import SocialComment
from apps.callcenter.models import CallCenterInteraction
from apps.integrations.models import InboundEvent
from apps.physicians.models import PhysicianMonthlyRating
@ -63,25 +63,25 @@ class CommandCenterView(LoginRequiredMixin, TemplateView):
complaints_qs = Complaint.objects.filter(hospital=hospital) if hospital else Complaint.objects.none()
actions_qs = PXAction.objects.filter(hospital=hospital) if hospital else PXAction.objects.none()
surveys_qs = SurveyInstance.objects.all() # Surveys can be viewed across hospitals
social_qs = SocialMediaComment.objects.all() # Social media is organization-wide, not hospital-specific
social_qs = SocialComment.objects.all() # Social media is organization-wide, not hospital-specific
calls_qs = CallCenterInteraction.objects.filter(hospital=hospital) if hospital else CallCenterInteraction.objects.none()
elif user.is_hospital_admin() and user.hospital:
complaints_qs = Complaint.objects.filter(hospital=user.hospital)
actions_qs = PXAction.objects.filter(hospital=user.hospital)
surveys_qs = SurveyInstance.objects.filter(survey_template__hospital=user.hospital)
social_qs = SocialMediaComment.objects.all() # Social media is organization-wide, not hospital-specific
social_qs = SocialComment.objects.all() # Social media is organization-wide, not hospital-specific
calls_qs = CallCenterInteraction.objects.filter(hospital=user.hospital)
elif user.is_department_manager() and user.department:
complaints_qs = Complaint.objects.filter(department=user.department)
actions_qs = PXAction.objects.filter(department=user.department)
surveys_qs = SurveyInstance.objects.filter(journey_stage_instance__department=user.department)
social_qs = SocialMediaComment.objects.all() # Social media is organization-wide, not department-specific
social_qs = SocialComment.objects.all() # Social media is organization-wide, not department-specific
calls_qs = CallCenterInteraction.objects.filter(department=user.department)
else:
complaints_qs = Complaint.objects.none()
actions_qs = PXAction.objects.none()
surveys_qs = SurveyInstance.objects.none()
social_qs = SocialMediaComment.objects.all() # Show all social media comments
social_qs = SocialComment.objects.all() # Show all social media comments
calls_qs = CallCenterInteraction.objects.none()
# Top KPI Stats
@ -119,7 +119,7 @@ class CommandCenterView(LoginRequiredMixin, TemplateView):
{
'label': _('Negative Social Mentions'),
'value': sum(
1 for comment in social_qs.filter(published_at__gte=last_7d)
1 for comment in social_qs.filter(created_at__gte=last_7d)
if comment.ai_analysis and
comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en') == 'negative'
),

251
apps/social/BILINGUAL_AI_ANALYSIS_IMPLEMENTATION.md
View File

@ -1,251 +0,0 @@
# Bilingual AI Analysis Implementation - Complete Summary
## Overview
Successfully implemented a comprehensive bilingual (English/Arabic) AI analysis system for social media comments, replacing the previous single-language sentiment analysis with a unified bilingual structure.
## What Was Implemented
### 1. **New Unified AI Analysis Structure**
#### Model Updates (`apps/social/models.py`)
- Added new `ai_analysis` JSONField to store complete bilingual analysis
- Marked existing fields as `[LEGACY]` for backward compatibility
- Updated `is_analyzed` property to check new structure
- Added `is_analyzed_legacy` for backward compatibility
**New JSON Structure:**
```json
{
"sentiment": {
"classification": {"en": "positive", "ar": "إيجابي"},
"score": 0.85,
"confidence": 0.92
},
"summaries": {
"en": "The customer is very satisfied with the excellent service...",
"ar": "العميل راضٍ جداً عن الخدمة الممتازة..."
},
"keywords": {
"en": ["excellent service", "fast delivery", ...],
"ar": ["خدمة ممتازة", "تسليم سريع", ...]
},
"topics": {
"en": ["customer service", "delivery speed", ...],
"ar": ["خدمة العملاء", "سرعة التسليم", ...]
},
"entities": [
{
"text": {"en": "Amazon", "ar": "أمازون"},
"type": {"en": "ORGANIZATION", "ar": "منظمة"}
}
],
"emotions": {
"joy": 0.9,
"anger": 0.05,
"sadness": 0.0,
"fear": 0.0,
"surprise": 0.15,
"disgust": 0.0,
"labels": {
"joy": {"en": "Joy/Happiness", "ar": "فرح/سعادة"},
...
}
},
"metadata": {
"model": "anthropic/claude-3-haiku",
"analyzed_at": "2026-01-07T12:00:00Z",
...
}
}
```
### 2. **OpenRouter Service Updates (`apps/social/services/openrouter_service.py`)**
Updated the analysis prompt to generate bilingual output:
- **Sentiment Classification**: Provided in both English and Arabic
- **Summaries**: 2-3 sentence summaries in both languages
- **Keywords**: 5-7 keywords in each language
- **Topics**: 3-5 topics in each language
- **Entities**: Bilingual entity recognition with type labels
- **Emotions**: 6 emotion scores with bilingual labels
- **Metadata**: Analysis timing, model info, token usage
### 3. **Analysis Service Updates (`apps/social/services/analysis_service.py`)**
Updated to populate the new bilingual structure:
- `analyze_pending_comments()` - Now populates bilingual analysis
- `reanalyze_comment()` - Single comment re-analysis with bilingual support
- Maintains backward compatibility by updating legacy fields alongside new structure
### 4. **Bilingual UI Component (`templates/social/partials/ai_analysis_bilingual.html`)**
Created a beautiful, interactive bilingual analysis display:
**Features:**
- 🇬🇧/🇸🇦 Language toggle buttons
- **Sentiment Section**:
- Color-coded badge with emoji
- Score and confidence progress bars
- **Summary Section**:
- Bilingual text display
- Copy-to-clipboard functionality
- RTL support for Arabic
- **Keywords & Topics**:
- Tag-based display
- Hover effects
- **Entities**:
- Card-based layout
- Type badges
- **Emotions**:
- 6 emotion types with progress bars
- Icons for each emotion
- **Metadata**:
- Model name and analysis timestamp
**UX Highlights:**
- Smooth transitions between languages
- Responsive design
- Professional color scheme
- Interactive elements (copy, hover effects)
- Accessible and user-friendly
### 5. **Template Filters (`apps/social/templatetags/social_filters.py`)**
Added helper filters:
- `multiply` - For calculating progress bar widths
- `add` - For score adjustments
- `get_sentiment_emoji` - Maps sentiment to emoji
### 6. **Database Migration**
Created and applied migration `0004_socialmediacomment_ai_analysis_and_more.py`:
- Added `ai_analysis` field
- Marked existing fields as legacy
## Design Decisions
### Bilingual Strategy
1. **Dual Storage**: All analysis stored in both English and Arabic
2. **User Choice**: UI toggle lets users switch between languages
3. **Quality AI**: AI provides accurate, culturally appropriate translations
4. **Complete Coverage**: Every field available in both languages
### Backward Compatibility
- Kept legacy fields for existing code
- Populate both structures during analysis
- Allows gradual migration
- No breaking changes
### UI/UX Approach
1. **Logical Organization**: Group related analysis sections
2. **Visual Hierarchy**: Clear sections with icons
3. **Interactive**: Language toggle, copy buttons, hover effects
4. **Professional**: Clean, modern design consistent with project
5. **Accessible**: Clear labels, color coding, progress bars
## Benefits
### For Users
- ✅ View analysis in preferred language (English/Arabic)
- ✅ Better understanding of Arabic comments
- ✅ Improved decision-making with bilingual insights
- ✅ Enhanced cultural context
### For Developers
- ✅ Unified data structure
- ✅ Reusable UI component
- ✅ Easy to extend with new languages
- ✅ Backward compatible
### For Business
- ✅ Better serve Saudi/Arabic market
- ✅ More accurate sentiment analysis
- ✅ Deeper insights from comments
- ✅ Competitive advantage in bilingual support
## Usage
### Analyzing Comments
```python
from apps.social.services.analysis_service import AnalysisService
service = AnalysisService()
result = service.analyze_pending_comments(limit=100)
```
### Displaying in Templates
```django
{% include "social/partials/ai_analysis_bilingual.html" %}
```
### Accessing Bilingual Data
```python
comment = SocialMediaComment.objects.first()
# English sentiment
sentiment_en = comment.ai_analysis['sentiment']['classification']['en']
# Arabic summary
summary_ar = comment.ai_analysis['summaries']['ar']
# Keywords in both languages
keywords_en = comment.ai_analysis['keywords']['en']
keywords_ar = comment.ai_analysis['keywords']['ar']
```
## Files Modified
1. `apps/social/models.py` - Added ai_analysis field
2. `apps/social/services/openrouter_service.py` - Updated for bilingual output
3. `apps/social/services/analysis_service.py` - Updated to populate new structure
4. `apps/social/templatetags/social_filters.py` - Added helper filters
5. `templates/social/partials/ai_analysis_bilingual.html` - NEW bilingual UI component
## Database Changes
**Migration**: `0004_socialmediacomment_ai_analysis_and_more.py`
- Added `ai_analysis` JSONField
- Updated field help texts for legacy fields
## Testing Recommendations
1. Test comment analysis with English comments
2. Test comment analysis with Arabic comments
3. Test language toggle in UI
4. Verify backward compatibility with existing code
5. Test emotion detection and display
6. Test copy-to-clipboard functionality
7. Test RTL layout for Arabic content
## Next Steps
1. Integrate the new bilingual component into detail pages
2. Add bilingual filtering in analytics views
3. Create bilingual reports
4. Add more languages if needed (expand structure)
5. Optimize AI prompts for better results
6. Add A/B testing for language preferences
## Technical Notes
- **AI Model**: Uses OpenRouter (Claude 3 Haiku by default)
- **Token Usage**: Bilingual analysis requires more tokens but provides comprehensive insights
- **Performance**: Analysis time similar to previous implementation
- **Storage**: JSONField efficient for bilingual data
- **Scalability**: Structure supports adding more languages
## Success Metrics
- ✅ Bilingual analysis structure implemented
- ✅ Backward compatibility maintained
- ✅ Beautiful, functional UI component created
- ✅ Template filters added for UI
- ✅ Database migration applied successfully
- ✅ No breaking changes introduced
- ✅ Comprehensive documentation provided
---
**Implementation Date**: January 7, 2026
**Status**: ✅ COMPLETE
**Ready for Production**: ✅ YES (after testing)

91
apps/social/FIXES_APPLIED.md
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@ -1,91 +0,0 @@
# Social App Fixes Applied
## Summary
Fixed all issues related to the Social Media app, including template filter errors, migration state mismatches, and cleanup of unused legacy code.
## Issues Fixed
### 1. Template Filter Error (`lookup` filter not found)
**Problem:** The template `social_comment_list.html` was trying to use a non-existent `lookup` filter to access platform-specific statistics.
**Solution:**
- Created custom template filter module: `apps/social/templatetags/social_filters.py`
- Implemented `lookup` filter to safely access dictionary keys
- Updated template to load and use the custom filter
**Files Modified:**
- `apps/social/templatetags/__init__.py` (created)
- `apps/social/templatetags/social_filters.py` (created)
- `templates/social/social_comment_list.html` (updated)
### 2. Missing Platform Statistics
**Problem:** The `social_comment_list` view only provided global statistics, but the template needed platform-specific counts for each platform card.
**Solution:**
- Updated `apps/social/ui_views.py` to add platform-specific counts to the stats dictionary
- Added loop to count comments for each platform (Facebook, Instagram, YouTube, etc.)
- Statistics now include: `stats.facebook`, `stats.instagram`, `stats.youtube`, etc.
**Files Modified:**
- `apps/social/ui_views.py` (updated)
### 3. Migration State Mismatch
**Problem:** Django migration showed as applied but the `social_socialmediacomment` table didn't exist in the database, causing "no such table" errors.
**Solution:**
- Unapplied the migration using `--fake` flag
- Ran the migration to create the table
- The table was successfully created and migration marked as applied
**Commands Executed:**
```bash
python manage.py migrate social zero --fake
python manage.py migrate social
python manage.py migrate social 0001 --fake
```
### 4. Legacy Template Cleanup
**Problem:** Two template files referenced a non-existent `SocialMention` model and were not being used by any URLs.
**Solution:**
- Removed unused templates:
- `templates/social/mention_list.html`
- `templates/social/mention_detail.html`
**Files Removed:**
- `templates/social/mention_list.html` (deleted)
- `templates/social/mention_detail.html` (deleted)
## Active Templates
The following templates are currently in use and properly configured:
1. **`social_comment_list.html`** - Main list view with platform cards, statistics, and filters
2. **`social_comment_detail.html`** - Individual comment detail view
3. **`social_platform.html`** - Platform-specific filtered view
4. **`social_analytics.html`** - Analytics dashboard with charts
## Active Model
**`SocialMediaComment`** - The only model in use for the social app
- Defined in: `apps/social/models.py`
- Fields: platform, comment_id, comments, author, sentiment, keywords, topics, entities, etc.
- Migration: `apps/social/migrations/0001_initial.py`
## Verification
All fixes have been verified:
- ✅ Django system check passes
- ✅ No template filter errors
- ✅ Database table exists
- ✅ Migration state is consistent
- ✅ All templates use the correct model
## Remaining Warning (Non-Critical)
There is a pre-existing warning about URL namespace 'accounts' not being unique:
```
?: (urls.W005) URL namespace 'accounts' isn't unique. You may not be able to reverse all URLs in this namespace
```
This is not related to the social app fixes and is a project-wide URL configuration issue.

172
apps/social/GOOGLE_REVIEWS_INTEGRATION.md
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@ -1,172 +0,0 @@
# Google Reviews Integration Implementation
## Summary
Successfully integrated Google Reviews platform into the social media monitoring system with full support for star ratings display.
## Changes Made
### 1. Model Updates (`apps/social/models.py`)
- Added `GOOGLE = 'google', 'Google Reviews'` to `SocialPlatform` enum
- Added `rating` field to `SocialMediaComment` model:
- Type: `IntegerField`
- Nullable: Yes (for platforms without ratings)
- Indexed: Yes
- Range: 1-5 stars
- Purpose: Store star ratings from review platforms
### 2. Database Migration
- Created migration: `0002_socialmediacomment_rating_and_more`
- Successfully applied to database
- New field added without data loss for existing records
### 3. UI Views Update (`apps/social/ui_views.py`)
- Added Google brand color `#4285F4` to `platform_colors` dictionary
- Ensures consistent branding across all Google Reviews pages
### 4. Template Filter (`apps/social/templatetags/star_rating.py`)
Created custom template filter for displaying star ratings:
- `{{ comment.rating|star_rating }}`
- Displays filled stars (★) and empty stars (☆)
- Example: Rating 3 → ★★★☆☆, Rating 5 → ★★★★★
- Handles invalid values gracefully
### 5. Template Updates
#### Comment Detail Template (`templates/social/social_comment_detail.html`)
- Added star rating display badge next to platform badge
- Shows rating as "★★★☆☆ 3/5"
- Only displays when rating is present
#### Comment List Template (`templates/social/social_comment_list.html`)
- Added star rating display in comment cards
- Integrated with existing platform badges
- Added Google platform color to JavaScript platform colors
- Added CSS styling for Google platform icon
#### Platform Template (`templates/social/social_platform.html`)
- Added star rating display for platform-specific views
- Maintains consistent styling with other templates
## Features Implemented
### Star Rating Display
- Visual star representation (★ for filled, ☆ for empty)
- Numeric display alongside stars (e.g., "★★★★☆ 4/5")
- Conditional rendering (only shows when rating exists)
- Responsive and accessible design
### Platform Support
- Google Reviews now available as a selectable platform
- Full integration with existing social media monitoring features
- Platform-specific filtering and analytics
- Consistent branding with Google's brand color (#4285F4)
### Data Structure
```python
class SocialMediaComment(models.Model):
# ... existing fields ...
rating = models.IntegerField(
null=True,
blank=True,
db_index=True,
help_text="Star rating (1-5) for review platforms like Google Reviews"
)
```
## Usage Examples
### Displaying Ratings in Templates
```django
{% load star_rating %}
<!-- Display rating if present -->
{% if comment.rating %}
<span class="badge bg-warning text-dark">
{{ comment.rating|star_rating }} {{ comment.rating }}/5
</span>
{% endif %}
```
### Filtering by Rating (Future Enhancement)
```python
# Filter reviews by rating
high_rated_reviews = SocialMediaComment.objects.filter(
platform='google',
rating__gte=4
)
```
### Analytics with Ratings
```python
# Calculate average rating
avg_rating = SocialMediaComment.objects.filter(
platform='google'
).aggregate(avg=Avg('rating'))['avg']
```
## Testing Checklist
- [x] Model changes applied
- [x] Database migration created and applied
- [x] Template filter created and functional
- [x] All templates updated to display ratings
- [x] Platform colors configured
- [x] JavaScript styling updated
- [x] No errors on social media pages
- [x] Server running and responding
## Benefits
1. **Enhanced Review Monitoring**: Google Reviews can now be monitored alongside other social media platforms
2. **Visual Clarity**: Star ratings provide immediate visual feedback on review quality
3. **Consistent Experience**: Google Reviews follow the same UI patterns as other platforms
4. **Future-Ready**: Data structure supports additional review platforms (Yelp, TripAdvisor, etc.)
5. **Analytics Ready**: Rating data indexed for efficient filtering and analysis
## Compatibility
- **Django**: Compatible with current Django version
- **Database**: SQLite (production ready for PostgreSQL, MySQL)
- **Browser**: All modern browsers with Unicode support
- **Mobile**: Fully responsive design
## Future Enhancements
Potential features that could be added:
1. Rating distribution charts in analytics
2. Filter by rating range in UI
3. Rating trend analysis over time
4. Export ratings in CSV/Excel
5. Integration with Google Places API for automatic scraping
6. Support for fractional ratings (e.g., 4.5 stars)
7. Rating-based sentiment correlation analysis
## Files Modified
1. `apps/social/models.py` - Added Google platform and rating field
2. `apps/social/ui_views.py` - Added Google brand color
3. `apps/social/templatetags/star_rating.py` - New file for star display
4. `templates/social/social_comment_detail.html` - Display ratings
5. `templates/social/social_comment_list.html` - Display ratings + Google color
6. `templates/social/social_platform.html` - Display ratings
7. `apps/social/migrations/0002_socialmediacomment_rating_and_more.py` - Database migration
## Deployment Notes
1. Run migrations on production: `python manage.py migrate social`
2. No data migration needed (field is nullable)
3. No breaking changes to existing functionality
4. Safe to deploy without downtime
## Support
For issues or questions:
- Check Django logs for template errors
- Verify star_rating.py is in templatetags directory
- Ensure `{% load star_rating %}` is in templates using the filter
- Confirm database migration was applied successfully
---
**Implementation Date**: January 7, 2026
**Status**: ✅ Complete and Deployed

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# Social Media App - Implementation Summary
## Overview
The Social Media app has been fully implemented with a complete UI that monitors and analyzes social media comments across multiple platforms (Facebook, Instagram, YouTube, Twitter, LinkedIn, TikTok).
## Implementation Date
January 6, 2026
## Components Implemented
### 1. Backend Components
#### models.py
- `SocialMediaComment` model with comprehensive fields:
- Platform selection (Facebook, Instagram, YouTube, Twitter, LinkedIn, TikTok, Other)
- Comment metadata (comment_id, post_id, author, comments)
- Engagement metrics (like_count, reply_count, share_count)
- AI analysis fields (sentiment, sentiment_score, confidence, keywords, topics, entities)
- Timestamps (published_at, scraped_at)
- Raw data storage
#### serializers.py
- `SocialMediaCommentSerializer` - Full serializer for all fields
- `SocialMediaCommentListSerializer` - Lightweight serializer for list views
- `SocialMediaCommentCreateSerializer` - Serializer for creating comments
- `SocialMediaCommentUpdateSerializer` - Serializer for updating comments
#### views.py
- `SocialMediaCommentViewSet` - DRF ViewSet with:
- Standard CRUD operations
- Advanced filtering (platform, sentiment, date range, keywords, topics)
- Search functionality
- Ordering options
- Custom actions: `analyze_sentiment`, `scrape_platform`, `export_data`
#### ui_views.py
Complete UI views with server-side rendering:
- `social_comment_list` - Main dashboard with all comments
- `social_comment_detail` - Individual comment detail view
- `social_platform` - Platform-specific filtered view
- `social_analytics` - Analytics dashboard with charts
- `social_scrape_now` - Manual scraping trigger
- `social_export_csv` - CSV export functionality
- `social_export_excel` - Excel export functionality
#### urls.py
- UI routes for all template views
- API routes for DRF ViewSet
- Export endpoints (CSV, Excel)
### 2. Frontend Components (Templates)
#### social_comment_list.html
**Main Dashboard Features:**
- Platform cards with quick navigation
- Real-time statistics (total, positive, neutral, negative)
- Advanced filter panel (collapsible)
- Platform filter
- Sentiment filter
- Date range filter
- Comment feed with pagination
- Platform badges with color coding
- Sentiment indicators
- Engagement metrics (likes, replies)
- Quick action buttons
- Export buttons (CSV, Excel)
- Responsive design with Bootstrap 5
#### social_platform.html
**Platform-Specific View Features:**
- Breadcrumb navigation
- Platform-specific branding and colors
- Platform statistics:
- Total comments
- Sentiment breakdown
- Average sentiment score
- Total engagement
- Time-based filters (all time, today, week, month)
- Search functionality
- Comment cards with platform color theming
- Pagination
#### social_comment_detail.html
**Detail View Features:**
- Full comment display with metadata
- Engagement metrics (likes, replies)
- AI Analysis section:
- Sentiment score with color coding
- Confidence score
- Keywords badges
- Topics badges
- Entities list
- Raw data viewer (collapsible)
- Comment info sidebar
- Action buttons:
- Create PX Action
- Mark as Reviewed
- Flag for Follow-up
- Delete Comment
#### social_analytics.html
**Analytics Dashboard Features:**
- Overview cards:
- Total comments
- Positive count
- Negative count
- Average engagement
- Interactive charts (Chart.js):
- Sentiment distribution (doughnut chart)
- Platform distribution (bar chart)
- Daily trends (line chart)
- Top keywords with progress bars
- Top topics list
- Platform breakdown table with:
- Comment counts
- Average sentiment
- Total likes/replies
- Quick navigation links
- Top entities cards
- Date range selector (7, 30, 90 days)
## Navigation Flow
```
Main Dashboard (/social/)
├── Platform Cards (clickable)
│ └── Platform-specific views (/social/facebook/, /social/instagram/, etc.)
│ └── Comment Cards (clickable)
│ └── Comment Detail View (/social/123/)
├── Analytics Button
│ └── Analytics Dashboard (/social/analytics/)
└── Comment Cards (clickable)
└── Comment Detail View (/social/123/)
Platform-specific views also have:
├── Analytics Button → Platform-filtered analytics
└── All Platforms Button → Back to main dashboard
Comment Detail View has:
├── View Similar → Filtered list by sentiment
└── Back to Platform → Platform-specific view
```
## Key Features
### 1. Creative Solution to Model/Template Mismatch
**Problem:** Original template was for a single feed, but model supports multiple platforms.
**Solution:**
- Created platform-specific view (`social_platform`)
- Added platform cards to main dashboard for quick navigation
- Implemented platform color theming throughout
- Each platform has its own filtered view with statistics
### 2. Advanced Filtering System
- Multi-level filtering (platform, sentiment, date range, keywords, topics)
- Time-based views (today, week, month)
- Search across comment text, author, and IDs
- Preserves filters across pagination
### 3. Comprehensive Analytics
- Real-time sentiment distribution
- Platform comparison metrics
- Daily trend analysis
- Keyword and topic extraction
- Entity recognition
- Engagement tracking
### 4. Export Functionality
- CSV export with all comment data
- Excel export with formatting
- Respects current filters
- Timestamp-based filenames
### 5. Responsive Design
- Mobile-friendly layout
- Bootstrap 5 components
- Color-coded sentiment indicators
- Platform-specific theming
- Collapsible sections for better UX
## Technology Stack
### Backend
- Django 4.x
- Django REST Framework
- Celery (for async tasks)
- PostgreSQL
### Frontend
- Bootstrap 5
- Bootstrap Icons
- Chart.js (for analytics)
- Django Templates
- Jinja2
## Integration Points
### With PX360 System
- PX Actions integration (buttons for creating actions)
- AI Engine integration (sentiment analysis)
- Analytics app integration (charts and metrics)
### External Services (to be implemented)
- Social Media APIs (Facebook Graph API, Instagram Basic Display API, YouTube Data API, Twitter API, LinkedIn API, TikTok API)
- Sentiment Analysis API (AI Engine)
## Future Enhancements
1. **Real-time Updates**
- WebSocket integration for live comment feed
- Auto-refresh functionality
2. **Advanced Analytics**
- Heat maps for engagement
- Sentiment trends over time
- Influencer identification
- Viral content detection
3. **Automation**
- Auto-create PX actions for negative sentiment
- Scheduled reporting
- Alert thresholds
4. **Integration**
- Connect to actual social media APIs
- Implement AI-powered sentiment analysis
- Add social listening capabilities
5. **User Experience**
- Dark mode support
- Customizable dashboards
- Saved filters and views
- Advanced search with boolean operators
## File Structure
```
apps/social/
├── __init__.py
├── admin.py
├── apps.py
├── models.py # Complete model with all fields
├── serializers.py # DRF serializers (4 types)
├── views.py # DRF ViewSet with custom actions
├── ui_views.py # UI views (7 views)
├── urls.py # URL configuration
├── tasks.py # Celery tasks (to be implemented)
├── services.py # Business logic (to be implemented)
└── migrations/ # Database migrations
templates/social/
├── social_comment_list.html # Main dashboard
├── social_platform.html # Platform-specific view
├── social_comment_detail.html # Detail view
└── social_analytics.html # Analytics dashboard
```
## Testing Checklist
- [x] All models created with proper fields
- [x] All serializers implemented
- [x] All DRF views implemented
- [x] All UI views implemented
- [x] All templates created
- [x] URL configuration complete
- [x] App registered in settings
- [x] Navigation flow complete
- [ ] Test with actual data
- [ ] Test filtering functionality
- [ ] Test pagination
- [ ] Test export functionality
- [ ] Test analytics charts
- [ ] Connect to social media APIs
- [ ] Implement Celery tasks
## Notes
1. **No Signals Required:** Unlike other apps, the social app doesn't need signals as comments are imported from external APIs.
2. **Celery Tasks:** Tasks for scraping and analysis should be implemented as Celery tasks for async processing.
3. **Data Import:** Comments should be imported via management commands or Celery tasks from social media APIs.
4. **AI Analysis:** Sentiment analysis, keyword extraction, topic modeling, and entity recognition should be handled by the AI Engine.
5. **Performance:** For large datasets, consider implementing database indexing and query optimization.
6. **Security:** Ensure proper authentication and authorization for all views and API endpoints.
## Conclusion
The Social Media app is now fully implemented with a complete, professional UI that provides comprehensive monitoring and analysis of social media comments across multiple platforms. The implementation follows Django best practices and integrates seamlessly with the PX360 system architecture.

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# Social App Model Field Corrections
## Summary
This document details the corrections made to ensure the social app code correctly uses all model fields.
## Issues Found and Fixed
### 1. **Critical: Broken Field Reference in tasks.py** (Line 264)
**File:** `apps/social/tasks.py`
**Issue:** Referenced non-existent `sentiment__isnull` field
**Fix:** Changed to use correct `ai_analysis__isnull` and `ai_analysis={}` filtering
**Before:**
```python
pending_count = SocialMediaComment.objects.filter(
sentiment__isnull=True
).count()
```
**After:**
```python
pending_count = SocialMediaComment.objects.filter(
ai_analysis__isnull=True
).count() + SocialMediaComment.objects.filter(
ai_analysis={}
).count()
```
### 2. **Missing `rating` Field in Serializers**
**File:** `apps/social/serializers.py`
**Issue:** Both serializers were missing the `rating` field (important for Google Reviews 1-5 star ratings)
**Fixed:**
- Added `rating` to `SocialMediaCommentSerializer` fields list
- Added `rating` to `SocialMediaCommentListSerializer` fields list
### 3. **Missing `rating` Field in Google Reviews Scraper**
**File:** `apps/social/scrapers/google_reviews.py`
**Issue:** Google Reviews scraper was not populating the `rating` field from scraped data
**Before:**
```python
# Add rating to raw_data for filtering
if star_rating:
review_dict['raw_data']['rating'] = star_rating
```
**After:**
```python
# Add rating field for Google Reviews (1-5 stars)
if star_rating:
review_dict['rating'] = int(star_rating)
```
### 4. **Missing `rating` Field in Comment Service**
**File:** `apps/social/services/comment_service.py`
**Issue:** `_save_comments` method was not handling the `rating` field
**Fixed:**
- Added `'rating': comment_data.get('rating')` to defaults dictionary
- Added `comment.rating = defaults['rating']` in the update section
### 5. **Missing `rating` Field in Admin Interface**
**File:** `apps/social/admin.py`
**Issue:** Admin interface was not displaying the rating field
**Added:**
- `rating_display` method to show star ratings with visual representation (★☆)
- Added `rating` to list_display
- Added `rating` to Engagement Metrics fieldset
## Field Coverage Verification
| Field | Model | Serializer | Admin | Views | Services | Status |
|-------|-------|-----------|-------|-------|----------|---------|
| id | ✓ | ✓ | - | ✓ | ✓ | ✓ Complete |
| platform | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| comment_id | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| comments | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| author | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| raw_data | ✓ | ✓ | ✓ | - | ✓ | ✓ Complete |
| post_id | ✓ | ✓ | ✓ | - | ✓ | ✓ Complete |
| media_url | ✓ | ✓ | ✓ | - | ✓ | ✓ Complete |
| like_count | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| reply_count | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| **rating** | ✓ | ✓ | ✓ | - | ✓ | ✓ **Fixed** |
| published_at | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| scraped_at | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
| ai_analysis | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ Complete |
## Impact of Changes
### Benefits:
1. **Google Reviews Data Integrity**: Star ratings (1-5) are now properly captured and stored
2. **Admin Usability**: Admin interface now shows star ratings with visual representation
3. **API Completeness**: Serializers now expose all model fields
4. **Bug Prevention**: Fixed critical field reference error that would cause runtime failures
5. **Data Accuracy**: Comment service now properly saves and updates rating data
### No Breaking Changes:
- All changes are additive (no field removals)
- Backward compatible with existing data
- No API contract changes
## Testing Recommendations
1. **Test Google Reviews Scraping**: Verify that star ratings are correctly scraped and saved
2. **Test Admin Interface**: Check that ratings display correctly with star icons
3. **Test API Endpoints**: Verify that serializers return the rating field
4. **Test Celery Tasks**: Ensure the analyze_pending_comments task works correctly with the fixed field reference
5. **Test Comment Updates**: Verify that updating existing comments preserves rating data
## Files Modified
1. `apps/social/tasks.py` - Fixed field reference
2. `apps/social/serializers.py` - Added rating field to both serializers
3. `apps/social/scrapers/google_reviews.py` - Fixed rating field population
4. `apps/social/services/comment_service.py` - Added rating field handling
5. `apps/social/admin.py` - Added rating display and field support
## Additional Fixes Applied After Initial Review
### 6. **Dashboard View Sentiment Filtering** (Critical)
**File:** `apps/dashboard/views.py`
**Issue:** Line 106 referenced non-existent `sentiment` field in filter
**Fix:** Changed to proper Python-based filtering using `ai_analysis` JSONField
**Before:**
```python
social_qs.filter(sentiment='negative', published_at__gte=last_7d).count()
```
**After:**
```python
sum(
1 for comment in social_qs.filter(published_at__gte=last_7d)
if comment.ai_analysis and
comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en') == 'negative'
)
```
### 7. **Template Filter Error in Analytics Dashboard** (Critical)
**File:** `templates/social/social_analytics.html` and `apps/social/templatetags/social_filters.py`
**Issue:** Template used `get_item` filter incorrectly - data structure was a list of dicts, not nested dict
**Root Cause:**
- `sentiment_distribution` is a list: `[{'sentiment': 'positive', 'count': 10}, ...]`
- Template tried: `{{ sentiment_distribution|get_item:positive|get_item:count }}`
- This implied nested dict: `{'positive': {'count': 10}}` which didn't exist
**Fix:**
1. Created new `get_sentiment_count` filter in `social_filters.py`:
```python
@register.filter
def get_sentiment_count(sentiment_list, sentiment_type):
"""Get count for a specific sentiment from a list of sentiment dictionaries."""
if not sentiment_list:
return 0
for item in sentiment_list:
if isinstance(item, dict) and item.get('sentiment') == sentiment_type:
return item.get('count', 0)
return 0
```
2. Updated template usage:
```django
{{ sentiment_distribution|get_sentiment_count:'positive' }}
```
## Complete Summary of All Fixes
### Files Modified (12 total):
1. `apps/social/tasks.py` - Fixed field reference bug (sentiment → ai_analysis)
2. `apps/social/serializers.py` - Added rating field
3. `apps/social/scrapers/google_reviews.py` - Fixed rating field population
4. `apps/social/services/comment_service.py` - Added rating field handling
5. `apps/social/admin.py` - Added rating display
6. `apps/dashboard/views.py` - Fixed sentiment filtering (sentiment → ai_analysis)
7. `templates/social/social_analytics.html` - Fixed template filter usage and added {% load social_filters %}
8. `apps/social/templatetags/social_filters.py` - Added get_sentiment_count filter
9. `apps/social/services/analysis_service.py` - Fixed queryset for SQLite compatibility
10. `apps/social/tests/test_analysis.py` - Fixed all sentiment field references
11. `apps/social/ui_views.py` - Fixed duplicate Sum import causing UnboundLocalError
### Issues Resolved:
- ✅ 4 Critical FieldError/OperationalError/UnboundLocalError bugs (tasks.py, dashboard views, ui_views.py, analysis_service.py)
- ✅ 1 TemplateSyntaxError in analytics dashboard (missing load tag)
- ✅ Missing rating field integration across 4 components
- ✅ All 13 model fields properly referenced throughout codebase
- ✅ SQLite compatibility issues resolved in querysets
- ✅ All test files updated to use correct field structure
- ✅ Template tag loading issues resolved
### Impact:
- **Immediate Fixes:** All reported errors now resolved
- **Data Integrity:** Google Reviews star ratings properly captured
- **Admin Usability:** Visual star rating display
- **API Completeness:** All model fields exposed via serializers
- **Template Reliability:** Proper data structure handling
## Additional Critical Fixes Applied
### 8. **SQLite Compatibility in Analysis Service** (Critical)
**File:** `apps/social/services/analysis_service.py`
**Issue:** Queryset using union operator `|` caused SQLite compatibility issues
**Fix:** Changed to use Q() objects for OR conditions
**Before:**
```python
queryset = SocialMediaComment.objects.filter(
ai_analysis__isnull=True
) | SocialMediaComment.objects.filter(
ai_analysis={}
)
```
**After:**
```python
from django.db.models import Q
queryset = SocialMediaComment.objects.filter(
Q(ai_analysis__isnull=True) | Q(ai_analysis={})
)
```
### 9. **Test File Field References** (Critical)
**File:** `apps/social/tests/test_analysis.py`
**Issue:** Test functions referenced non-existent `sentiment` and `sentiment_analyzed_at` fields
**Fix:** Updated all test queries to use `ai_analysis` JSONField and proper field access
## Root Cause Analysis
The social app went through a migration from individual fields (`sentiment`, `confidence`, `sentiment_analyzed_at`) to a unified `ai_analysis` JSONField. However, several files still referenced the old field structure, causing `OperationalError: no such column` errors in SQLite.
**Migration Impact:**
- Old structure: Separate columns for `sentiment`, `confidence`, `sentiment_analyzed_at`
- New structure: Single `ai_analysis` JSONField containing all analysis data
- Problem: Codebase wasn't fully updated to match new structure
## Conclusion
All model fields are now properly referenced and used throughout the social app codebase. Four critical bugs have been fixed:
1. **Field reference errors** in tasks.py, dashboard views, and analysis_service.py
2. **Template filter error** in analytics dashboard
3. **Missing rating field** integration throughout the data pipeline
4. **SQLite compatibility issues** with queryset unions
The social app code is now correct based on the model fields and should function without errors. All field references use the proper `ai_analysis` JSONField structure.

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apps/social/__init__.py
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@ -0,0 +1,3 @@
# Social app - Unified social media platform integration
default_app_config = 'social.apps.SocialConfig'

176
apps/social/admin.py
View File

@ -1,176 +0,0 @@
from django.contrib import admin
from django.utils.html import format_html
from .models import SocialMediaComment
from .services.analysis_service import AnalysisService
@admin.register(SocialMediaComment)
class SocialMediaCommentAdmin(admin.ModelAdmin):
"""
Admin interface for SocialMediaComment model with bilingual AI analysis features.
"""
list_display = [
'platform',
'author',
'comments_preview',
'rating_display',
'sentiment_badge',
'confidence_display',
'like_count',
'is_analyzed',
'published_at',
'scraped_at'
]
list_filter = [
'platform',
'published_at',
'scraped_at'
]
search_fields = ['author', 'comments', 'comment_id', 'post_id']
readonly_fields = [
'scraped_at',
'is_analyzed',
'ai_analysis_display',
'raw_data'
]
date_hierarchy = 'published_at'
actions = ['trigger_analysis']
fieldsets = (
('Basic Information', {
'fields': ('platform', 'comment_id', 'post_id', 'media_url')
}),
('Content', {
'fields': ('comments', 'author')
}),
('Engagement Metrics', {
'fields': ('like_count', 'reply_count', 'rating')
}),
('AI Bilingual Analysis', {
'fields': ('is_analyzed', 'ai_analysis_display'),
'classes': ('collapse',)
}),
('Timestamps', {
'fields': ('published_at', 'scraped_at')
}),
('Technical Data', {
'fields': ('raw_data',),
'classes': ('collapse',)
}),
)
def comments_preview(self, obj):
"""
Display a preview of the comment text.
"""
return obj.comments[:100] + '...' if len(obj.comments) > 100 else obj.comments
comments_preview.short_description = 'Comment Preview'
def rating_display(self, obj):
"""
Display star rating (for Google Reviews).
"""
if obj.rating is None:
return '-'
stars = '★' * obj.rating + '☆' * (5 - obj.rating)
return format_html('<span title="{} stars">{}</span>', obj.rating, stars)
rating_display.short_description = 'Rating'
def sentiment_badge(self, obj):
"""
Display sentiment as a colored badge from ai_analysis.
"""
if not obj.ai_analysis:
return format_html('<span style="color: gray;">Not analyzed</span>')
sentiment = obj.ai_analysis.get('sentiment', {}).get('classification', {}).get('en', 'neutral')
colors = {
'positive': 'green',
'negative': 'red',
'neutral': 'blue'
}
color = colors.get(sentiment, 'gray')
return format_html(
'<span style="color: {}; font-weight: bold;">{}</span>',
color,
sentiment.capitalize()
)
sentiment_badge.short_description = 'Sentiment'
def confidence_display(self, obj):
"""
Display confidence score from ai_analysis.
"""
if not obj.ai_analysis:
return '-'
confidence = obj.ai_analysis.get('sentiment', {}).get('confidence', 0)
return format_html('{:.2f}', confidence)
confidence_display.short_description = 'Confidence'
def ai_analysis_display(self, obj):
"""
Display formatted AI analysis data.
"""
if not obj.ai_analysis:
return format_html('<p>No AI analysis available</p>')
sentiment = obj.ai_analysis.get('sentiment', {})
summary_en = obj.ai_analysis.get('summaries', {}).get('en', '')
summary_ar = obj.ai_analysis.get('summaries', {}).get('ar', '')
keywords = obj.ai_analysis.get('keywords', {}).get('en', [])
html = format_html('<h4>Sentiment Analysis</h4>')
html += format_html('<p><strong>Classification:</strong> {} ({})</p>',
sentiment.get('classification', {}).get('en', 'N/A'),
sentiment.get('classification', {}).get('ar', 'N/A')
)
html += format_html('<p><strong>Score:</strong> {}</p>',
sentiment.get('score', 0)
)
html += format_html('<p><strong>Confidence:</strong> {}</p>',
sentiment.get('confidence', 0)
)
if summary_en:
html += format_html('<h4>Summary (English)</h4><p>{}</p>', summary_en)
if summary_ar:
html += format_html('<h4>الملخص (Arabic)</h4><p dir="rtl">{}</p>', summary_ar)
if keywords:
html += format_html('<h4>Keywords</h4><p>{}</p>', ', '.join(keywords))
return html
ai_analysis_display.short_description = 'AI Analysis'
def is_analyzed(self, obj):
"""
Display whether comment has been analyzed.
"""
return bool(obj.ai_analysis)
is_analyzed.boolean = True
is_analyzed.short_description = 'Analyzed'
def trigger_analysis(self, request, queryset):
"""
Admin action to trigger AI analysis for selected comments.
"""
service = AnalysisService()
analyzed = 0
failed = 0
for comment in queryset:
if not comment.ai_analysis: # Only analyze unanalyzed comments
result = service.reanalyze_comment(comment.id)
if result.get('success'):
analyzed += 1
else:
failed += 1
self.message_user(
request,
f'Analysis complete: {analyzed} analyzed, {failed} failed',
level='SUCCESS' if failed == 0 else 'WARNING'
)
trigger_analysis.short_description = 'Analyze selected comments'

9
apps/social/apps.py
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@ -2,6 +2,11 @@ from django.apps import AppConfig
class SocialConfig(AppConfig):
name = 'apps.social'
default_auto_field = 'django.db.models.BigAutoField'
verbose_name = 'Social Media'
name = 'apps.social'
def ready(self):
"""
Import signals when app is ready to ensure they are registered.
"""
import apps.social.signals

262
apps/social/models.py
View File

@ -1,63 +1,143 @@
# social/models.py
from django.db import models
from django.conf import settings
from django.utils import timezone
from django.contrib.auth import get_user_model
# Get the custom User model lazily
User = get_user_model()
# ============================================================================
# MODEL 1: SocialAccount - One model for all platform accounts
# ============================================================================
class SocialAccount(models.Model):
"""Unified account model for all social platforms"""
# FIX: Renamed 'user' to 'owner' to match the logic in views.py
owner = models.ForeignKey(User, on_delete=models.CASCADE, related_name='social_accounts')
PLATFORM_CHOICES = [
('LI', 'LinkedIn'),
('GO', 'Google'),
('META', 'Meta (Facebook/Instagram)'),
('TT', 'TikTok'),
('X', 'X/Twitter'),
('YT', 'YouTube'),
]
platform_type = models.CharField(max_length=4, choices=PLATFORM_CHOICES)
platform_id = models.CharField(max_length=255, help_text="Platform-specific account ID")
name = models.CharField(max_length=255, help_text="Account name or display name")
# Flexible credentials storage
access_token = models.TextField(blank=True, null=True)
refresh_token = models.TextField(blank=True, null=True)
credentials_json = models.JSONField(default=dict, blank=True)
# Token management
expires_at = models.DateTimeField(null=True, blank=True)
is_permanent = models.BooleanField(default=False)
# Sync tracking
is_active = models.BooleanField(default=True)
last_synced_at = models.DateTimeField(null=True, blank=True)
updated_at = models.DateTimeField(auto_now=True)
created_at = models.DateTimeField(auto_now_add=True)
class Meta:
unique_together = [['platform_type', 'platform_id']]
ordering = ['-created_at']
def __str__(self):
return f"{self.get_platform_type_display()}: {self.name}"
def is_token_expired(self):
"""Check if token is expired or needs refresh"""
if self.is_permanent:
return False
if not self.expires_at:
return True
# Consider expired if within 24 hours of expiration
return timezone.now() >= (self.expires_at - timezone.timedelta(hours=24))
class SocialPlatform(models.TextChoices):
"""Social media platform choices"""
FACEBOOK = 'facebook', 'Facebook'
INSTAGRAM = 'instagram', 'Instagram'
YOUTUBE = 'youtube', 'YouTube'
TWITTER = 'twitter', 'Twitter/X'
LINKEDIN = 'linkedin', 'LinkedIn'
TIKTOK = 'tiktok', 'TikTok'
GOOGLE = 'google', 'Google Reviews'
# ============================================================================
# MODEL 2: SocialContent - One model for posts/videos/tweets
# ============================================================================
class SocialMediaComment(models.Model):
"""
Model to store social media comments from various platforms with AI analysis.
Stores scraped comments and AI-powered sentiment, keywords, topics, and entity analysis.
"""
class SocialContent(models.Model):
"""Unified content model for posts, videos, tweets"""
# --- Core ---
id = models.BigAutoField(primary_key=True)
platform = models.CharField(
max_length=50,
choices=SocialPlatform.choices,
db_index=True,
help_text="Social media platform"
)
comment_id = models.CharField(
max_length=255,
db_index=True,
help_text="Unique comment ID from the platform"
)
# --- Content ---
comments = models.TextField(help_text="Comment text content")
author = models.CharField(max_length=255, null=True, blank=True, help_text="Comment author")
# --- Raw Data ---
raw_data = models.JSONField(
default=dict,
help_text="Complete raw data from platform API"
)
# --- Metadata ---
post_id = models.CharField(
max_length=255,
null=True,
account = models.ForeignKey(SocialAccount, on_delete=models.CASCADE, related_name='contents')
platform_type = models.CharField(max_length=4)
source_platform = models.CharField(
max_length=4,
blank=True,
help_text="ID of the post/media"
)
media_url = models.URLField(
max_length=500,
null=True,
blank=True,
help_text="URL to associated media"
null=True,
help_text="Actual source platform for Meta (FB/IG)"
)
# --- Engagement ---
content_id = models.CharField(max_length=255, unique=True, db_index=True, help_text="Platform-specific content ID")
# Content data
title = models.CharField(max_length=255, blank=True, help_text="For videos/titles")
text = models.TextField(blank=True, help_text="For posts/tweets")
# Delta sync bookmark - CRITICAL for incremental updates
last_comment_sync_at = models.DateTimeField(default=timezone.now)
# Sync state
is_syncing = models.BooleanField(default=False, help_text="Is full sync in progress?")
# Platform-specific data
content_data = models.JSONField(default=dict)
# Timestamps
created_at = models.DateTimeField(help_text="Actual content creation time")
added_at = models.DateTimeField(auto_now_add=True)
class Meta:
ordering = ['-created_at']
indexes = [
models.Index(fields=['account', '-created_at']),
models.Index(fields=['platform_type', '-created_at']),
]
def __str__(self):
return f"{self.platform_type} Content: {self.content_id}"
# ============================================================================
# MODEL 3: SocialComment - One model for comments/reviews (original comments only)
# ============================================================================
class SocialComment(models.Model):
"""Unified comment model for comments, reviews (original comments only)"""
account = models.ForeignKey(SocialAccount, on_delete=models.CASCADE, related_name='comments')
content = models.ForeignKey(SocialContent, on_delete=models.CASCADE, related_name='comments')
platform_type = models.CharField(max_length=4)
source_platform = models.CharField(
max_length=4,
blank=True,
null=True,
help_text="Actual source platform for Meta (FB/IG)"
)
comment_id = models.CharField(max_length=255, unique=True, db_index=True, help_text="Platform-specific comment ID")
# Author information
author_name = models.CharField(max_length=255)
author_id = models.CharField(max_length=255, blank=True, null=True)
# Comment data
text = models.TextField()
# Platform-specific data
comment_data = models.JSONField(default=dict)
# --- Engagement Metrics ---
like_count = models.IntegerField(default=0, help_text="Number of likes")
reply_count = models.IntegerField(default=0, help_text="Number of replies")
rating = models.IntegerField(
@ -67,17 +147,12 @@ class SocialMediaComment(models.Model):
help_text="Star rating (1-5) for review platforms like Google Reviews"
)
# --- Timestamps ---
published_at = models.DateTimeField(
null=True,
# --- Media ---
media_url = models.URLField(
max_length=500,
null=True,
blank=True,
db_index=True,
help_text="When the comment was published"
)
scraped_at = models.DateTimeField(
auto_now_add=True,
db_index=True,
help_text="When the comment was scraped"
help_text="URL to associated media (images/videos)"
)
# --- AI Bilingual Analysis ---
@ -88,20 +163,71 @@ class SocialMediaComment(models.Model):
help_text="Complete AI analysis in bilingual format (en/ar) with sentiment, summaries, keywords, topics, entities, and emotions"
)
# Timestamps
created_at = models.DateTimeField(db_index=True)
added_at = models.DateTimeField(auto_now_add=True)
# Webhook support
synced_via_webhook = models.BooleanField(default=False)
class Meta:
ordering = ['-published_at']
unique_together = ['platform', 'comment_id']
ordering = ['-created_at']
indexes = [
models.Index(fields=['platform']),
models.Index(fields=['published_at']),
models.Index(fields=['platform', '-published_at']),
models.Index(fields=['ai_analysis'], name='idx_ai_analysis'),
models.Index(fields=['account', '-created_at']),
models.Index(fields=['content', '-created_at']),
models.Index(fields=['platform_type', '-created_at']),
models.Index(fields=['ai_analysis'], name='idx_comment_ai_analysis'),
]
def __str__(self):
return f"{self.platform} - {self.author or 'Anonymous'}"
return f"{self.platform_type} Comment by {self.author_name}"
@property
def is_analyzed(self):
"""Check if comment has been AI analyzed"""
return bool(self.ai_analysis)
# ============================================================================
# MODEL 4: SocialReply - Separate model for replies to comments
# ============================================================================
class SocialReply(models.Model):
"""Unified reply model for replies to comments"""
account = models.ForeignKey(SocialAccount, on_delete=models.CASCADE, related_name='replies')
comment = models.ForeignKey(SocialComment, on_delete=models.CASCADE, related_name='replies')
platform_type = models.CharField(max_length=4)
source_platform = models.CharField(
max_length=4,
blank=True,
null=True,
help_text="Actual source platform for Meta (FB/IG)"
)
reply_id = models.CharField(max_length=255, unique=True, db_index=True, help_text="Platform-specific reply ID")
# Author information
author_name = models.CharField(max_length=255)
author_id = models.CharField(max_length=255, blank=True, null=True)
# Reply data
text = models.TextField()
# Platform-specific data
reply_data = models.JSONField(default=dict)
# Timestamps
created_at = models.DateTimeField(db_index=True)
added_at = models.DateTimeField(auto_now_add=True)
class Meta:
ordering = ['-created_at']
indexes = [
models.Index(fields=['comment', '-created_at']),
models.Index(fields=['account', '-created_at']),
models.Index(fields=['platform_type', '-created_at']),
]
def __str__(self):
return f"Reply by {self.author_name} to {self.comment}"

13
apps/social/scrapers/__init__.py
View File

@ -1,13 +0,0 @@
"""
Social media scrapers for extracting comments from various platforms.
"""
from .base import BaseScraper
from .youtube import YouTubeScraper
from .facebook import FacebookScraper
from .instagram import InstagramScraper
from .twitter import TwitterScraper
from .linkedin import LinkedInScraper
from .google_reviews import GoogleReviewsScraper
__all__ = ['BaseScraper', 'YouTubeScraper', 'FacebookScraper', 'InstagramScraper', 'TwitterScraper', 'LinkedInScraper', 'GoogleReviewsScraper']

86
apps/social/scrapers/base.py
View File

@ -1,86 +0,0 @@
"""
Base scraper class for social media platforms.
"""
import logging
from abc import ABC, abstractmethod
from typing import List, Dict, Any
from datetime import datetime
class BaseScraper(ABC):
"""
Abstract base class for social media scrapers.
All platform-specific scrapers should inherit from this class.
"""
def __init__(self, config: Dict[str, Any]):
"""
Initialize the scraper with configuration.
Args:
config: Dictionary containing platform-specific configuration
"""
self.config = config
self.logger = logging.getLogger(self.__class__.__name__)
@abstractmethod
def scrape_comments(self, **kwargs) -> List[Dict[str, Any]]:
"""
Scrape comments from the platform.
Returns:
List of dictionaries containing comment data with standardized fields:
- comment_id: Unique comment ID from the platform
- comments: Comment text
- author: Author name/username
- published_at: Publication timestamp (ISO format)
- like_count: Number of likes
- reply_count: Number of replies
- post_id: ID of the post/media
- media_url: URL to associated media (if applicable)
- raw_data: Complete raw data from platform API
"""
pass
def _standardize_comment(self, comment_data: Dict[str, Any]) -> Dict[str, Any]:
"""
Standardize comment data format.
Subclasses can override this method to handle platform-specific formatting.
Args:
comment_data: Raw comment data from platform API
Returns:
Standardized comment dictionary
"""
return comment_data
def _parse_timestamp(self, timestamp_str: str) -> str:
"""
Parse platform timestamp to ISO format.
Args:
timestamp_str: Platform-specific timestamp string
Returns:
ISO formatted timestamp string
"""
try:
# Try common timestamp formats
for fmt in [
'%Y-%m-%dT%H:%M:%S%z',
'%Y-%m-%dT%H:%M:%SZ',
'%Y-%m-%d %H:%M:%S',
'%Y-%m-%d',
]:
try:
dt = datetime.strptime(timestamp_str, fmt)
return dt.isoformat()
except ValueError:
continue
# If no format matches, return as-is
return timestamp_str
except Exception as e:
self.logger.warning(f"Failed to parse timestamp {timestamp_str}: {e}")
return timestamp_str

187
apps/social/scrapers/facebook.py
View File

@ -1,187 +0,0 @@
"""
Facebook comment scraper using Facebook Graph API.
"""
import logging
import requests
from typing import List, Dict, Any
from .base import BaseScraper
class FacebookScraper(BaseScraper):
"""
Scraper for Facebook comments using Facebook Graph API.
Extracts comments from posts.
"""
BASE_URL = "https://graph.facebook.com/v19.0"
def __init__(self, config: Dict[str, Any]):
"""
Initialize Facebook scraper.
Args:
config: Dictionary with 'access_token' and optionally 'page_id'
"""
super().__init__(config)
self.access_token = config.get('access_token')
if not self.access_token:
raise ValueError(
"Facebook access token is required. "
"Set FACEBOOK_ACCESS_TOKEN in your .env file."
)
self.page_id = config.get('page_id')
if not self.page_id:
self.logger.warning(
"Facebook page_id not provided. "
"Set FACEBOOK_PAGE_ID in your .env file to specify which page to scrape."
)
self.logger = logging.getLogger(self.__class__.__name__)
def scrape_comments(self, page_id: str = None, **kwargs) -> List[Dict[str, Any]]:
"""
Scrape comments from all posts on a Facebook page.
Args:
page_id: Facebook page ID to scrape comments from
Returns:
List of standardized comment dictionaries
"""
page_id = page_id or self.page_id
if not page_id:
raise ValueError("Facebook page ID is required")
all_comments = []
self.logger.info(f"Starting Facebook comment extraction for page: {page_id}")
# Get all posts from the page
posts = self._fetch_all_posts(page_id)
self.logger.info(f"Found {len(posts)} posts to process")
# Get comments for each post
for post in posts:
post_id = post['id']
post_comments = self._fetch_post_comments(post_id, post)
all_comments.extend(post_comments)
self.logger.info(f"Fetched {len(post_comments)} comments for post {post_id}")
self.logger.info(f"Completed Facebook scraping. Total comments: {len(all_comments)}")
return all_comments
def _fetch_all_posts(self, page_id: str) -> List[Dict[str, Any]]:
"""
Fetch all posts from a Facebook page.
Args:
page_id: Facebook page ID
Returns:
List of post dictionaries
"""
url = f"{self.BASE_URL}/{page_id}/feed"
params = {
'access_token': self.access_token,
'fields': 'id,message,created_time,permalink_url'
}
all_posts = []
while url:
try:
response = requests.get(url, params=params)
data = response.json()
if 'error' in data:
self.logger.error(f"Facebook API error: {data['error']['message']}")
break
all_posts.extend(data.get('data', []))
# Check for next page
url = data.get('paging', {}).get('next')
params = {} # Next URL already contains params
except Exception as e:
self.logger.error(f"Error fetching posts: {e}")
break
return all_posts
def _fetch_post_comments(self, post_id: str, post_data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Fetch all comments for a specific Facebook post.
Args:
post_id: Facebook post ID
post_data: Post data dictionary
Returns:
List of standardized comment dictionaries
"""
url = f"{self.BASE_URL}/{post_id}/comments"
params = {
'access_token': self.access_token,
'fields': 'id,message,from,created_time,like_count'
}
all_comments = []
while url:
try:
response = requests.get(url, params=params)
data = response.json()
if 'error' in data:
self.logger.error(f"Facebook API error: {data['error']['message']}")
break
# Process comments
for comment_data in data.get('data', []):
comment = self._extract_comment(comment_data, post_id, post_data)
if comment:
all_comments.append(comment)
# Check for next page
url = data.get('paging', {}).get('next')
params = {} # Next URL already contains params
except Exception as e:
self.logger.error(f"Error fetching comments for post {post_id}: {e}")
break
return all_comments
def _extract_comment(self, comment_data: Dict[str, Any], post_id: str, post_data: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract and standardize a Facebook comment.
Args:
comment_data: Facebook API comment data
post_id: Post ID
post_data: Post data dictionary
Returns:
Standardized comment dictionary
"""
try:
from_data = comment_data.get('from', {})
comment = {
'comment_id': comment_data['id'],
'comments': comment_data.get('message', ''),
'author': from_data.get('name', ''),
'published_at': self._parse_timestamp(comment_data.get('created_time')),
'like_count': comment_data.get('like_count', 0),
'reply_count': 0, # Facebook API doesn't provide reply count easily
'post_id': post_id,
'media_url': post_data.get('permalink_url'),
'raw_data': comment_data
}
return self._standardize_comment(comment)
except Exception as e:
self.logger.error(f"Error extracting Facebook comment: {e}")
return None

345
apps/social/scrapers/google_reviews.py
View File

@ -1,345 +0,0 @@
"""
Google Reviews scraper using Google My Business API.
"""
import os
import json
import logging
from typing import List, Dict, Any, Optional
from pathlib import Path
try:
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from googleapiclient.discovery import build
except ImportError:
raise ImportError(
"Google API client libraries not installed. "
"Install with: pip install google-api-python-client google-auth-oauthlib"
)
from .base import BaseScraper
class GoogleReviewsScraper(BaseScraper):
"""
Scraper for Google Reviews using Google My Business API.
Extracts reviews from one or multiple locations.
"""
# OAuth scope for managing Business Profile data
SCOPES = ['https://www.googleapis.com/auth/business.manage']
def __init__(self, config: Dict[str, Any]):
"""
Initialize Google Reviews scraper.
Args:
config: Dictionary with:
- 'credentials_file': Path to client_secret.json (or None)
- 'token_file': Path to token.json (default: 'token.json')
- 'locations': List of location names to scrape (optional)
- 'account_name': Google account name (optional, will be fetched if not provided)
"""
super().__init__(config)
self.credentials_file = config.get('credentials_file', 'client_secret.json')
self.token_file = config.get('token_file', 'token.json')
self.locations = config.get('locations', None) # Specific locations to scrape
self.account_name = config.get('account_name', None)
self.logger = logging.getLogger(self.__class__.__name__)
# Authenticate and build service
self.service = self._get_authenticated_service()
def _get_authenticated_service(self):
"""
Get authenticated Google My Business API service.
Returns:
Authenticated service object
"""
creds = None
# Load existing credentials from token file
if os.path.exists(self.token_file):
creds = Credentials.from_authorized_user_file(self.token_file, self.SCOPES)
# If there are no (valid) credentials available, let the user log in
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
self.logger.info("Refreshing expired credentials...")
creds.refresh(Request())
else:
# Check if credentials file exists
if not os.path.exists(self.credentials_file):
raise FileNotFoundError(
f"Google Reviews requires '{self.credentials_file}' credentials file. "
"This scraper will be disabled. See GOOGLE_REVIEWS_INTEGRATION_GUIDE.md for setup instructions."
)
self.logger.info("Starting OAuth flow...")
flow = InstalledAppFlow.from_client_secrets_file(
self.credentials_file,
self.SCOPES
)
creds = flow.run_local_server(port=0)
# Save the credentials for the next run
with open(self.token_file, 'w') as token:
token.write(creds.to_json())
self.logger.info(f"Credentials saved to {self.token_file}")
# Build the service using the My Business v4 discovery document
service = build('mybusiness', 'v4', credentials=creds)
self.logger.info("Successfully authenticated with Google My Business API")
return service
def _get_account_name(self) -> str:
"""
Get the account ID from Google My Business.
Returns:
Account name (e.g., 'accounts/123456789')
"""
if self.account_name:
return self.account_name
self.logger.info("Fetching account list...")
accounts_resp = self.service.accounts().list().execute()
if not accounts_resp.get('accounts'):
raise ValueError("No Google My Business accounts found. Please ensure you have admin access.")
account_name = accounts_resp['accounts'][0]['name']
self.logger.info(f"Using account: {account_name}")
self.account_name = account_name
return account_name
def _get_locations(self, account_name: str) -> List[Dict[str, Any]]:
"""
Get all locations for the account.
Args:
account_name: Google account name
Returns:
List of location dictionaries
"""
self.logger.info("Fetching location list...")
locations_resp = self.service.accounts().locations().list(parent=account_name).execute()
locations = locations_resp.get('locations', [])
if not locations:
raise ValueError(f"No locations found under account {account_name}")
self.logger.info(f"Found {len(locations)} locations")
# Filter locations if specific locations are requested
if self.locations:
filtered_locations = []
for loc in locations:
# Check if location name matches any of the requested locations
if any(req_loc in loc['name'] for req_loc in self.locations):
filtered_locations.append(loc)
self.logger.info(f"Filtered to {len(filtered_locations)} locations")
return filtered_locations
return locations
def scrape_comments(
self,
location_names: Optional[List[str]] = None,
max_reviews_per_location: int = 100,
**kwargs
) -> List[Dict[str, Any]]:
"""
Scrape Google reviews from specified locations.
Args:
location_names: Optional list of location names to scrape (scrapes all if None)
max_reviews_per_location: Maximum reviews to fetch per location
Returns:
List of standardized review dictionaries
"""
all_reviews = []
try:
# Get account and locations
account_name = self._get_account_name()
locations = self._get_locations(account_name)
# Apply location filter if provided
if location_names:
filtered_locations = []
for loc in locations:
if any(req_loc in loc['name'] for req_loc in location_names):
filtered_locations.append(loc)
locations = filtered_locations
if not locations:
self.logger.warning(f"No matching locations found for: {location_names}")
return []
# Get location resource names for batch fetching
location_resource_names = [loc['name'] for loc in locations]
self.logger.info(f"Extracting reviews for {len(location_resource_names)} locations...")
# Batch fetch reviews for all locations
next_page_token = None
page_num = 0
while True:
page_num += 1
self.logger.info(f"Fetching page {page_num} of reviews...")
batch_body = {
"locationNames": location_resource_names,
"pageSize": max_reviews_per_location,
"pageToken": next_page_token,
"ignoreRatingOnlyReviews": False
}
# Official batchGetReviews call
results = self.service.accounts().locations().batchGetReviews(
name=account_name,
body=batch_body
).execute()
location_reviews = results.get('locationReviews', [])
if not location_reviews:
self.logger.info(f"No more reviews found on page {page_num}")
break
# Process reviews
for loc_review in location_reviews:
review_data = loc_review.get('review', {})
location_name = loc_review.get('name')
standardized = self._extract_review(location_name, review_data)
if standardized:
all_reviews.append(standardized)
self.logger.info(f" - Page {page_num}: {len(location_reviews)} reviews (total: {len(all_reviews)})")
next_page_token = results.get('nextPageToken')
if not next_page_token:
self.logger.info("All reviews fetched")
break
self.logger.info(f"Completed Google Reviews scraping. Total reviews: {len(all_reviews)}")
# Log location distribution
location_stats = {}
for review in all_reviews:
location_id = review.get('raw_data', {}).get('location_name', 'unknown')
location_stats[location_id] = location_stats.get(location_id, 0) + 1
self.logger.info("Reviews by location:")
for location, count in location_stats.items():
self.logger.info(f" - {location}: {count} reviews")
return all_reviews
except Exception as e:
self.logger.error(f"Error scraping Google Reviews: {e}")
raise
def _extract_review(
self,
location_name: str,
review_data: Dict[str, Any]
) -> Optional[Dict[str, Any]]:
"""
Extract and standardize a review from Google My Business API response.
Args:
location_name: Location resource name
review_data: Review object from Google API
Returns:
Standardized review dictionary
"""
try:
# Extract review data
review_id = review_data.get('name', '')
reviewer_info = review_data.get('reviewer', {})
comment = review_data.get('comment', '')
star_rating = review_data.get('starRating')
create_time = review_data.get('createTime')
update_time = review_data.get('updateTime')
# Extract reviewer information
reviewer_name = reviewer_info.get('displayName', 'Anonymous')
reviewer_id = reviewer_info.get('name', '')
# Extract review reply
reply_data = review_data.get('reviewReply', {})
reply_comment = reply_data.get('comment', '')
reply_time = reply_data.get('updateTime', '')
# Extract location details if available
# We'll get the full location info from the location name
try:
location_info = self.service.accounts().locations().get(
name=location_name
).execute()
location_address = location_info.get('address', {})
location_name_display = location_info.get('locationName', '')
location_city = location_address.get('locality', '')
location_country = location_address.get('countryCode', '')
except:
location_info = {}
location_name_display = ''
location_city = ''
location_country = ''
# Build Google Maps URL for the review
# Extract location ID from resource name (e.g., 'accounts/123/locations/456')
location_id = location_name.split('/')[-1]
google_maps_url = f"https://search.google.com/local/writereview?placeid={location_id}"
review_dict = {
'comment_id': review_id,
'comments': comment,
'author': reviewer_name,
'published_at': self._parse_timestamp(create_time) if create_time else None,
'like_count': 0, # Google reviews don't have like counts
'reply_count': 1 if reply_comment else 0,
'post_id': location_name, # Store location name as post_id
'media_url': google_maps_url,
'raw_data': {
'location_name': location_name,
'location_id': location_id,
'location_display_name': location_name_display,
'location_city': location_city,
'location_country': location_country,
'location_info': location_info,
'review_id': review_id,
'reviewer_id': reviewer_id,
'reviewer_name': reviewer_name,
'star_rating': star_rating,
'comment': comment,
'create_time': create_time,
'update_time': update_time,
'reply_comment': reply_comment,
'reply_time': reply_time,
'full_review': review_data
}
}
# Add rating field for Google Reviews (1-5 stars)
if star_rating:
review_dict['rating'] = int(star_rating)
return self._standardize_comment(review_dict)
except Exception as e:
self.logger.error(f"Error extracting Google review: {e}")
return None

187
apps/social/scrapers/instagram.py
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@ -1,187 +0,0 @@
"""
Instagram comment scraper using Instagram Graph API.
"""
import logging
import requests
from typing import List, Dict, Any
from .base import BaseScraper
class InstagramScraper(BaseScraper):
"""
Scraper for Instagram comments using Instagram Graph API.
Extracts comments from media posts.
"""
BASE_URL = "https://graph.facebook.com/v19.0"
def __init__(self, config: Dict[str, Any]):
"""
Initialize Instagram scraper.
Args:
config: Dictionary with 'access_token' and optionally 'account_id'
"""
super().__init__(config)
self.access_token = config.get('access_token')
if not self.access_token:
raise ValueError(
"Instagram access token is required. "
"Set INSTAGRAM_ACCESS_TOKEN in your .env file."
)
self.account_id = config.get('account_id')
if not self.account_id:
self.logger.warning(
"Instagram account_id not provided. "
"Set INSTAGRAM_ACCOUNT_ID in your .env file to specify which account to scrape."
)
self.logger = logging.getLogger(self.__class__.__name__)
def scrape_comments(self, account_id: str = None, **kwargs) -> List[Dict[str, Any]]:
"""
Scrape comments from all media on an Instagram account.
Args:
account_id: Instagram account ID to scrape comments from
Returns:
List of standardized comment dictionaries
"""
account_id = account_id or self.account_id
if not account_id:
raise ValueError("Instagram account ID is required")
all_comments = []
self.logger.info(f"Starting Instagram comment extraction for account: {account_id}")
# Get all media from the account
media_list = self._fetch_all_media(account_id)
self.logger.info(f"Found {len(media_list)} media items to process")
# Get comments for each media
for media in media_list:
media_id = media['id']
media_comments = self._fetch_media_comments(media_id, media)
all_comments.extend(media_comments)
self.logger.info(f"Fetched {len(media_comments)} comments for media {media_id}")
self.logger.info(f"Completed Instagram scraping. Total comments: {len(all_comments)}")
return all_comments
def _fetch_all_media(self, account_id: str) -> List[Dict[str, Any]]:
"""
Fetch all media from an Instagram account.
Args:
account_id: Instagram account ID
Returns:
List of media dictionaries
"""
url = f"{self.BASE_URL}/{account_id}/media"
params = {
'access_token': self.access_token,
'fields': 'id,caption,timestamp,permalink_url,media_type'
}
all_media = []
while url:
try:
response = requests.get(url, params=params)
data = response.json()
if 'error' in data:
self.logger.error(f"Instagram API error: {data['error']['message']}")
break
all_media.extend(data.get('data', []))
# Check for next page
url = data.get('paging', {}).get('next')
params = {} # Next URL already contains params
except Exception as e:
self.logger.error(f"Error fetching media: {e}")
break
return all_media
def _fetch_media_comments(self, media_id: str, media_data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Fetch all comments for a specific Instagram media.
Args:
media_id: Instagram media ID
media_data: Media data dictionary
Returns:
List of standardized comment dictionaries
"""
url = f"{self.BASE_URL}/{media_id}/comments"
params = {
'access_token': self.access_token,
'fields': 'id,text,username,timestamp,like_count'
}
all_comments = []
while url:
try:
response = requests.get(url, params=params)
data = response.json()
if 'error' in data:
self.logger.error(f"Instagram API error: {data['error']['message']}")
break
# Process comments
for comment_data in data.get('data', []):
comment = self._extract_comment(comment_data, media_id, media_data)
if comment:
all_comments.append(comment)
# Check for next page
url = data.get('paging', {}).get('next')
params = {} # Next URL already contains params
except Exception as e:
self.logger.error(f"Error fetching comments for media {media_id}: {e}")
break
return all_comments
def _extract_comment(self, comment_data: Dict[str, Any], media_id: str, media_data: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract and standardize an Instagram comment.
Args:
comment_data: Instagram API comment data
media_id: Media ID
media_data: Media data dictionary
Returns:
Standardized comment dictionary
"""
try:
caption = media_data.get('caption', '')
comment = {
'comment_id': comment_data['id'],
'comments': comment_data.get('text', ''),
'author': comment_data.get('username', ''),
'published_at': self._parse_timestamp(comment_data.get('timestamp')),
'like_count': comment_data.get('like_count', 0),
'reply_count': 0, # Instagram API doesn't provide reply count easily
'post_id': media_id,
'media_url': media_data.get('permalink_url'),
'raw_data': comment_data
}
return self._standardize_comment(comment)
except Exception as e:
self.logger.error(f"Error extracting Instagram comment: {e}")
return None

262
apps/social/scrapers/linkedin.py
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@ -1,262 +0,0 @@
"""
LinkedIn comment scraper using LinkedIn Marketing API.
"""
import logging
from typing import List, Dict, Any
import requests
from .base import BaseScraper
class LinkedInScraper(BaseScraper):
"""
Scraper for LinkedIn comments using LinkedIn Marketing API.
Extracts comments from organization posts.
"""
def __init__(self, config: Dict[str, Any]):
"""
Initialize LinkedIn scraper.
Args:
config: Dictionary with 'access_token' and 'organization_id'
"""
super().__init__(config)
self.access_token = config.get('access_token')
if not self.access_token:
raise ValueError(
"LinkedIn access token is required. "
"Set LINKEDIN_ACCESS_TOKEN in your .env file."
)
self.org_id = config.get('organization_id')
if not self.org_id:
raise ValueError(
"LinkedIn organization ID is required. "
"Set LINKEDIN_ORGANIZATION_ID in your .env file."
)
self.api_version = config.get('api_version', '202401')
self.headers = {
'Authorization': f'Bearer {self.access_token}',
'LinkedIn-Version': self.api_version,
'X-Restli-Protocol-Version': '2.0.0',
'Content-Type': 'application/json'
}
self.base_url = "https://api.linkedin.com/rest"
self.logger = logging.getLogger(self.__class__.__name__)
def scrape_comments(
self,
organization_id: str = None,
max_posts: int = 50,
max_comments_per_post: int = 100,
**kwargs
) -> List[Dict[str, Any]]:
"""
Scrape comments from LinkedIn organization posts.
Args:
organization_id: LinkedIn organization URN (e.g., 'urn:li:organization:1234567')
max_posts: Maximum number of posts to scrape
max_comments_per_post: Maximum comments to fetch per post
Returns:
List of standardized comment dictionaries
"""
organization_id = organization_id or self.org_id
if not organization_id:
raise ValueError("Organization ID is required")
all_comments = []
self.logger.info(f"Starting LinkedIn comment extraction for {organization_id}")
try:
# Get all posts for the organization
posts = self._get_all_page_posts(organization_id)
self.logger.info(f"Found {len(posts)} posts")
# Limit posts if needed
if max_posts and len(posts) > max_posts:
posts = posts[:max_posts]
self.logger.info(f"Limited to {max_posts} posts")
# Extract comments from each post
for i, post_urn in enumerate(posts, 1):
self.logger.info(f"Processing post {i}/{len(posts)}: {post_urn}")
try:
comments = self._get_comments_for_post(
post_urn,
max_comments=max_comments_per_post
)
for comment in comments:
standardized = self._extract_comment(post_urn, comment)
if standardized:
all_comments.append(standardized)
self.logger.info(f" - Found {len(comments)} comments")
except Exception as e:
self.logger.warning(f"Error processing post {post_urn}: {e}")
continue
self.logger.info(f"Completed LinkedIn scraping. Total comments: {len(all_comments)}")
return all_comments
except Exception as e:
self.logger.error(f"Error scraping LinkedIn: {e}")
raise
def _get_all_page_posts(self, org_urn: str, count: int = 50) -> List[str]:
"""
Retrieves all post URNs for the organization.
Args:
org_urn: Organization URN
count: Number of posts per request
Returns:
List of post URNs
"""
posts = []
start = 0
while True:
# Finder query for posts by author
url = f"{self.base_url}/posts?author={org_urn}&q=author&count={count}&start={start}"
try:
response = requests.get(url, headers=self.headers)
response.raise_for_status()
data = response.json()
if 'elements' not in data or not data['elements']:
break
posts.extend([item['id'] for item in data['elements']])
start += count
self.logger.debug(f"Retrieved {len(data['elements'])} posts (total: {len(posts)})")
except requests.exceptions.RequestException as e:
self.logger.error(f"Error fetching posts: {e}")
break
return posts
def _get_comments_for_post(self, post_urn: str, max_comments: int = 100) -> List[Dict[str, Any]]:
"""
Retrieves all comments for a specific post URN.
Args:
post_urn: Post URN
max_comments: Maximum comments to fetch
Returns:
List of comment objects
"""
comments = []
start = 0
count = 100
while True:
# Social Actions API for comments
url = f"{self.base_url}/socialActions/{post_urn}/comments?count={count}&start={start}"
try:
response = requests.get(url, headers=self.headers)
response.raise_for_status()
data = response.json()
if 'elements' not in data or not data['elements']:
break
for comment in data['elements']:
comments.append(comment)
# Check if we've reached the limit
if len(comments) >= max_comments:
return comments[:max_comments]
start += count
# Check if we need to stop
if len(comments) >= max_comments:
return comments[:max_comments]
except requests.exceptions.RequestException as e:
self.logger.warning(f"Error fetching comments for post {post_urn}: {e}")
break
return comments[:max_comments]
def _extract_comment(self, post_urn: str, comment: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract and standardize a comment from LinkedIn API response.
Args:
post_urn: Post URN
comment: Comment object from LinkedIn API
Returns:
Standardized comment dictionary
"""
try:
# Extract comment data
comment_id = comment.get('id', '')
message = comment.get('message', {})
comment_text = message.get('text', '')
actor = comment.get('actor', '')
# Extract author information
author_id = ''
author_name = ''
if isinstance(actor, str):
author_id = actor
elif isinstance(actor, dict):
author_id = actor.get('id', '')
author_name = actor.get('firstName', '') + ' ' + actor.get('lastName', '')
# Extract created time
created_time = comment.get('created', {}).get('time', '')
# Extract social actions (likes)
social_actions = comment.get('socialActions', [])
like_count = 0
for action in social_actions:
if action.get('actionType') == 'LIKE':
like_count = action.get('actorCount', 0)
break
# Build LinkedIn URL
linkedin_url = post_urn.replace('urn:li:activity:', 'https://www.linkedin.com/feed/update/')
comment_data = {
'comment_id': comment_id,
'comments': comment_text,
'author': author_name or author_id,
'published_at': self._parse_timestamp(created_time) if created_time else None,
'like_count': like_count,
'reply_count': 0, # LinkedIn API doesn't provide reply count easily
'post_id': post_urn,
'media_url': linkedin_url,
'raw_data': {
'post_urn': post_urn,
'comment_id': comment_id,
'comment_text': comment_text,
'author_id': author_id,
'author_name': author_name,
'created_time': created_time,
'like_count': like_count,
'full_comment': comment
}
}
return self._standardize_comment(comment_data)
except Exception as e:
self.logger.error(f"Error extracting LinkedIn comment: {e}")
return None

194
apps/social/scrapers/twitter.py
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@ -1,194 +0,0 @@
"""
Twitter/X comment scraper using Twitter API v2 via Tweepy.
"""
import logging
from typing import List, Dict, Any
import tweepy
from .base import BaseScraper
class TwitterScraper(BaseScraper):
"""
Scraper for Twitter/X comments (replies) using Twitter API v2.
Extracts replies to tweets from a specified user.
"""
def __init__(self, config: Dict[str, Any]):
"""
Initialize Twitter scraper.
Args:
config: Dictionary with 'bearer_token' and optionally 'username'
"""
super().__init__(config)
self.bearer_token = config.get('bearer_token')
if not self.bearer_token:
raise ValueError(
"Twitter bearer token is required. "
"Set TWITTER_BEARER_TOKEN in your .env file."
)
self.default_username = config.get('username', 'elonmusk')
if not config.get('username'):
self.logger.warning(
"Twitter username not provided. "
"Set TWITTER_USERNAME in your .env file to specify which account to scrape."
)
self.client = tweepy.Client(
bearer_token=self.bearer_token,
wait_on_rate_limit=True
)
self.logger = logging.getLogger(self.__class__.__name__)
def scrape_comments(
self,
username: str = None,
max_tweets: int = 50,
max_replies_per_tweet: int = 100,
**kwargs
) -> List[Dict[str, Any]]:
"""
Scrape replies (comments) from a Twitter/X user's tweets.
Args:
username: Twitter username to scrape (uses default from config if not provided)
max_tweets: Maximum number of tweets to fetch
max_replies_per_tweet: Maximum replies per tweet
Returns:
List of standardized comment dictionaries
"""
username = username or self.default_username
if not username:
raise ValueError("Username is required")
all_comments = []
self.logger.info(f"Starting Twitter comment extraction for @{username}")
try:
# Get user ID
user = self.client.get_user(username=username)
if not user.data:
self.logger.error(f"User @{username} not found")
return all_comments
user_id = user.data.id
self.logger.info(f"Found user ID: {user_id}")
# Fetch tweets and their replies
tweet_count = 0
for tweet in tweepy.Paginator(
self.client.get_users_tweets,
id=user_id,
max_results=100
).flatten(limit=max_tweets):
tweet_count += 1
self.logger.info(f"Processing tweet {tweet_count}/{max_tweets} (ID: {tweet.id})")
# Search for replies to this tweet
replies = self._get_tweet_replies(tweet.id, max_replies_per_tweet)
for reply in replies:
comment = self._extract_comment(tweet, reply)
if comment:
all_comments.append(comment)
self.logger.info(f" - Found {len(replies)} replies for this tweet")
self.logger.info(f"Completed Twitter scraping. Total comments: {len(all_comments)}")
return all_comments
except tweepy.errors.NotFound:
self.logger.error(f"User @{username} not found or account is private")
return all_comments
except tweepy.errors.Forbidden:
self.logger.error(f"Access forbidden for @{username}. Check API permissions.")
return all_comments
except tweepy.errors.TooManyRequests:
self.logger.error("Twitter API rate limit exceeded")
return all_comments
except Exception as e:
self.logger.error(f"Error scraping Twitter: {e}")
raise
def _get_tweet_replies(self, tweet_id: str, max_replies: int) -> List[Dict[str, Any]]:
"""
Get replies for a specific tweet.
Args:
tweet_id: Original tweet ID
max_replies: Maximum number of replies to fetch
Returns:
List of reply tweet objects
"""
replies = []
# Search for replies using conversation_id
query = f"conversation_id:{tweet_id} is:reply"
try:
for reply in tweepy.Paginator(
self.client.search_recent_tweets,
query=query,
tweet_fields=['author_id', 'created_at', 'text'],
max_results=100
).flatten(limit=max_replies):
replies.append(reply)
except Exception as e:
self.logger.warning(f"Error fetching replies for tweet {tweet_id}: {e}")
return replies
def _extract_comment(self, original_tweet: Dict[str, Any], reply_tweet: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract and standardize a reply (comment) from Twitter API response.
Args:
original_tweet: Original tweet object
reply_tweet: Reply tweet object
Returns:
Standardized comment dictionary
"""
try:
# Extract reply data
reply_id = str(reply_tweet.id)
reply_text = reply_tweet.text
reply_author_id = str(reply_tweet.author_id)
reply_created_at = reply_tweet.created_at
# Extract original tweet data
original_tweet_id = str(original_tweet.id)
# Build Twitter URL
twitter_url = f"https://twitter.com/x/status/{original_tweet_id}"
comment_data = {
'comment_id': reply_id,
'comments': reply_text,
'author': reply_author_id,
'published_at': self._parse_timestamp(reply_created_at.isoformat()),
'like_count': 0, # Twitter API v2 doesn't provide like count for replies in basic query
'reply_count': 0, # Would need additional API call
'post_id': original_tweet_id,
'media_url': twitter_url,
'raw_data': {
'original_tweet_id': original_tweet_id,
'original_tweet_text': original_tweet.text,
'reply_id': reply_id,
'reply_author_id': reply_author_id,
'reply_text': reply_text,
'reply_at': reply_created_at.isoformat()
}
}
return self._standardize_comment(comment_data)
except Exception as e:
self.logger.error(f"Error extracting Twitter comment: {e}")
return None

134
apps/social/scrapers/youtube.py
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@ -1,134 +0,0 @@
"""
YouTube comment scraper using YouTube Data API v3.
"""
import logging
from typing import List, Dict, Any
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from .base import BaseScraper
class YouTubeScraper(BaseScraper):
"""
Scraper for YouTube comments using YouTube Data API v3.
Extracts top-level comments only (no replies).
"""
def __init__(self, config: Dict[str, Any]):
"""
Initialize YouTube scraper.
Args:
config: Dictionary with 'api_key' and optionally 'channel_id'
"""
super().__init__(config)
self.api_key = config.get('api_key')
if not self.api_key:
raise ValueError(
"YouTube API key is required. "
"Set YOUTUBE_API_KEY in your .env file."
)
self.channel_id = config.get('channel_id')
if not self.channel_id:
self.logger.warning(
"YouTube channel_id not provided. "
"Set YOUTUBE_CHANNEL_ID in your .env file to specify which channel to scrape."
)
self.youtube = build('youtube', 'v3', developerKey=self.api_key)
self.logger = logging.getLogger(self.__class__.__name__)
def scrape_comments(self, channel_id: str = None, **kwargs) -> List[Dict[str, Any]]:
"""
Scrape top-level comments from a YouTube channel.
Args:
channel_id: YouTube channel ID to scrape comments from
Returns:
List of standardized comment dictionaries
"""
channel_id = channel_id or self.config.get('channel_id')
if not channel_id:
raise ValueError("Channel ID is required")
all_comments = []
next_page_token = None
self.logger.info(f"Starting YouTube comment extraction for channel: {channel_id}")
while True:
try:
# Get comment threads (top-level comments only)
request = self.youtube.commentThreads().list(
part="snippet",
allThreadsRelatedToChannelId=channel_id,
maxResults=100,
pageToken=next_page_token,
textFormat="plainText"
)
response = request.execute()
# Process each comment thread
for item in response.get('items', []):
comment = self._extract_top_level_comment(item)
if comment:
all_comments.append(comment)
# Check for more pages
next_page_token = response.get('nextPageToken')
if not next_page_token:
break
self.logger.info(f"Fetched {len(all_comments)} comments so far...")
except HttpError as e:
if e.resp.status in [403, 429]:
self.logger.error("YouTube API quota exceeded or access forbidden")
break
else:
self.logger.error(f"YouTube API error: {e}")
break
except Exception as e:
self.logger.error(f"Unexpected error scraping YouTube: {e}")
break
self.logger.info(f"Completed YouTube scraping. Total comments: {len(all_comments)}")
return all_comments
def _extract_top_level_comment(self, item: Dict[str, Any]) -> Dict[str, Any]:
"""
Extract and standardize a top-level comment from YouTube API response.
Args:
item: YouTube API comment thread item
Returns:
Standardized comment dictionary
"""
try:
top_level_comment = item['snippet']['topLevelComment']['snippet']
comment_id = item['snippet']['topLevelComment']['id']
# Get video ID (post_id)
video_id = item['snippet'].get('videoId')
comment_data = {
'comment_id': comment_id,
'comments': top_level_comment.get('textDisplay', ''),
'author': top_level_comment.get('authorDisplayName', ''),
'published_at': self._parse_timestamp(top_level_comment.get('publishedAt')),
'like_count': top_level_comment.get('likeCount', 0),
'reply_count': item['snippet'].get('totalReplyCount', 0),
'post_id': video_id,
'media_url': f"https://www.youtube.com/watch?v={video_id}" if video_id else None,
'raw_data': item
}
return self._standardize_comment(comment_data)
except Exception as e:
self.logger.error(f"Error extracting YouTube comment: {e}")
return None

105
apps/social/serializers.py
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@ -1,105 +0,0 @@
"""
Serializers for Social Media Comments app
"""
from rest_framework import serializers
from .models import SocialMediaComment, SocialPlatform
class SocialMediaCommentSerializer(serializers.ModelSerializer):
"""Serializer for SocialMediaComment model with bilingual AI analysis"""
platform_display = serializers.CharField(source='get_platform_display', read_only=True)
is_analyzed = serializers.ReadOnlyField()
sentiment_classification_en = serializers.SerializerMethodField()
sentiment_classification_ar = serializers.SerializerMethodField()
sentiment_score = serializers.SerializerMethodField()
confidence = serializers.SerializerMethodField()
class Meta:
model = SocialMediaComment
fields = [
'id',
'platform',
'platform_display',
'comment_id',
'comments',
'author',
'raw_data',
'post_id',
'media_url',
'like_count',
'reply_count',
'rating',
'published_at',
'scraped_at',
'ai_analysis',
'is_analyzed',
'sentiment_classification_en',
'sentiment_classification_ar',
'sentiment_score',
'confidence',
]
read_only_fields = [
'scraped_at',
]
def get_sentiment_classification_en(self, obj):
"""Get English sentiment classification"""
if not obj.ai_analysis:
return None
return obj.ai_analysis.get('sentiment', {}).get('classification', {}).get('en')
def get_sentiment_classification_ar(self, obj):
"""Get Arabic sentiment classification"""
if not obj.ai_analysis:
return None
return obj.ai_analysis.get('sentiment', {}).get('classification', {}).get('ar')
def get_sentiment_score(self, obj):
"""Get sentiment score"""
if not obj.ai_analysis:
return None
return obj.ai_analysis.get('sentiment', {}).get('score')
def get_confidence(self, obj):
"""Get confidence score"""
if not obj.ai_analysis:
return None
return obj.ai_analysis.get('sentiment', {}).get('confidence')
def validate_platform(self, value):
"""Validate platform choice"""
if value not in SocialPlatform.values:
raise serializers.ValidationError(f"Invalid platform. Must be one of: {', '.join(SocialPlatform.values)}")
return value
class SocialMediaCommentListSerializer(serializers.ModelSerializer):
"""Lightweight serializer for list views"""
platform_display = serializers.CharField(source='get_platform_display', read_only=True)
is_analyzed = serializers.ReadOnlyField()
sentiment = serializers.SerializerMethodField()
class Meta:
model = SocialMediaComment
fields = [
'id',
'platform',
'platform_display',
'comment_id',
'comments',
'author',
'like_count',
'reply_count',
'rating',
'published_at',
'is_analyzed',
'sentiment',
]
def get_sentiment(self, obj):
"""Get sentiment classification (English)"""
if not obj.ai_analysis:
return None
return obj.ai_analysis.get('sentiment', {}).get('classification', {}).get('en')

21
apps/social/services/__init__.py
View File

@ -1,7 +1,18 @@
"""
Services for managing social media comment scraping and database operations.
"""
# Social Services - All Platform Services
# This module contains service classes for all social platforms
from .comment_service import CommentService
from .linkedin import LinkedInService, LinkedInAPIError
from .google import GoogleBusinessService, GoogleAPIError
from .meta import MetaService, MetaAPIError
from .tiktok import TikTokService, TikTokAPIError
from .x import XService, XAPIError
from .youtube import YouTubeService, YouTubeAPIError, RateLimitError
__all__ = ['CommentService']
__all__ = [
'LinkedInService', 'LinkedInAPIError',
'GoogleBusinessService', 'GoogleAPIError',
'MetaService', 'MetaAPIError',
'TikTokService', 'TikTokAPIError',
'XService', 'XAPIError',
'YouTubeService', 'YouTubeAPIError', 'RateLimitError',
]

447
apps/social/services/ai_service.py Normal file
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@ -0,0 +1,447 @@
"""
OpenRouter API service for AI-powered patient experience comment analysis.
Handles authentication, requests, and response parsing for sentiment analysis,
keyword extraction, topic identification, and entity recognition optimized for healthcare.
"""
import logging
import json
from typing import Dict, List, Any, Optional
import httpx
from django.conf import settings
from django.utils import timezone
logger = logging.getLogger(__name__)
class OpenRouterService:
"""
Service for interacting with OpenRouter API to analyze patient experience comments.
Provides healthcare-focused sentiment analysis, keyword extraction, topic identification,
and entity recognition with actionable business insights.
"""
DEFAULT_MODEL = "anthropic/claude-3-haiku"
DEFAULT_MAX_TOKENS = 2048
DEFAULT_TEMPERATURE = 0.1
def __init__(
self,
api_key: Optional[str] = None,
model: Optional[str] = None,
timeout: int = 30
):
"""
Initialize OpenRouter service.
Args:
api_key: OpenRouter API key (defaults to settings.OPENROUTER_API_KEY)
model: Model to use (defaults to settings.OPENROUTER_MODEL or DEFAULT_MODEL)
timeout: Request timeout in seconds
"""
self.api_key = api_key or getattr(settings, 'OPENROUTER_API_KEY', None)
self.model = model or getattr(settings, 'AI_MODEL', self.DEFAULT_MODEL)
self.timeout = timeout
self.api_url = "https://openrouter.ai/api/v1/chat/completions"
if not self.api_key:
logger.warning(
"OpenRouter API key not configured. "
"Set OPENROUTER_API_KEY in your .env file."
)
logger.info(f"OpenRouter service initialized with model: {self.model}")
def _build_analysis_prompt(self, comments: List[Dict[str, Any]]) -> str:
"""
Build prompt for batch comment analysis with bilingual output.
Note: This method is kept for compatibility but not actively used.
Args:
comments: List of comment dictionaries with 'id' and 'text' keys
Returns:
Formatted prompt string
"""
# Kept for backward compatibility
comments_text = "\n".join([
f"Comment {i+1}: {c['text']}"
for i, c in enumerate(comments)
])
prompt = """You are a bilingual healthcare experience analyst. Analyze patient comments..."""
return prompt
def analyze_comment(self, comment_id: str, text: str) -> Dict[str, Any]:
"""
Analyze a single patient experience comment using OpenRouter API.
Args:
comment_id: Comment ID
text: Comment text
Returns:
Dictionary with success status and analysis results
"""
logger.info("=" * 80)
logger.info("STARTING PATIENT EXPERIENCE ANALYSIS")
logger.info("=" * 80)
if not self.api_key:
logger.error("API KEY NOT CONFIGURED")
return {
'success': False,
'error': 'OpenRouter API key not configured'
}
if not text:
logger.warning("No comment text to analyze")
return {
'success': False,
'error': 'Comment text is empty'
}
try:
logger.info(f"Building prompt for comment {comment_id}...")
# Enhanced healthcare-focused prompt
prompt = f"""You are an expert healthcare patient experience analyst specializing in analyzing patient feedback for hospital quality improvement and business intelligence. Analyze the following patient comment and provide a COMPREHENSIVE bilingual analysis in BOTH English and Arabic that helps hospital management make data-driven decisions.
PATIENT COMMENT:
{text}
CRITICAL REQUIREMENTS:
1. ALL analysis MUST be provided in BOTH English and Arabic
2. Use clear, modern Arabic (فصحى معاصرة) that all Arabic speakers understand
3. Detect the comment's original language and provide accurate translations
4. Maintain cultural sensitivity and medical terminology accuracy
5. Focus on actionable insights for hospital improvement
PROVIDE THE FOLLOWING ANALYSIS:
A. SENTIMENT ANALYSIS (Bilingual)
- classification: {{"en": "positive|neutral|negative|mixed", "ar": "إيجابي|محايد|سلبي|مختلط"}}
- score: number from -1.0 (very negative) to 1.0 (very positive)
- confidence: number from 0.0 to 1.0
- urgency_level: {{"en": "low|medium|high|critical", "ar": "منخفض|متوسط|عالي|حرج"}}
B. DETAILED SUMMARY (Bilingual)
- en: 3-4 sentence English summary covering: main complaint/praise, specific incidents, patient expectations, and emotional tone
- ar: 3-4 sentence Arabic summary (ملخص تفصيلي) with equivalent depth and nuance
C. KEYWORDS (Bilingual - 7-10 each)
Focus on: medical terms, service aspects, staff mentions, facility features, emotional descriptors
- en: ["keyword1", "keyword2", ...]
- ar: ["كلمة1", "كلمة2", ...]
D. HEALTHCARE-SPECIFIC TOPICS (Bilingual - 4-6 each)
Categories: Clinical Care, Nursing Care, Medical Staff, Administrative Services, Facility/Environment,
Wait Times, Communication, Billing/Finance, Food Services, Cleanliness, Privacy, Technology/Equipment
- en: ["topic1", "topic2", ...]
- ar: ["موضوع1", "موضوع2", ...]
E. ENTITIES (Bilingual)
Extract: Doctor names, Department names, Staff roles, Locations, Medical conditions, Treatments, Medications
- For each entity: {{"text": {{"en": "...", "ar": "..."}}, "type": {{"en": "DOCTOR|NURSE|DEPARTMENT|STAFF|LOCATION|CONDITION|TREATMENT|MEDICATION|OTHER", "ar": "طبيب|ممرض|قسم|موظف|موقع|حالة|علاج|دواء|أخرى"}}}}
F. EMOTIONS (Granular Analysis)
- joy: 0.0 to 1.0 (satisfaction, happiness, gratitude)
- anger: 0.0 to 1.0 (frustration, irritation, rage)
- sadness: 0.0 to 1.0 (disappointment, grief, despair)
- fear: 0.0 to 1.0 (anxiety, worry, panic)
- surprise: 0.0 to 1.0 (shock, amazement)
- disgust: 0.0 to 1.0 (revulsion, contempt)
- trust: 0.0 to 1.0 (confidence in care, safety)
- anticipation: 0.0 to 1.0 (hope, expectation)
- labels: {{"emotion": {{"en": "English", "ar": "عربي"}}}}
G. ACTIONABLE INSIGHTS (NEW - Critical for Business)
- primary_concern: {{"en": "Main issue identified", "ar": "المشكلة الرئيسية"}}
- affected_department: {{"en": "Department name", "ar": "اسم القسم"}}
- service_quality_indicators: {{
"clinical_care": 0-10,
"staff_behavior": 0-10,
"facility_condition": 0-10,
"wait_time": 0-10,
"communication": 0-10,
"overall_experience": 0-10
}}
- complaint_type: {{"en": "clinical|service|administrative|facility|staff_behavior|billing|other", "ar": "سريري|خدمة|إداري|منشأة|سلوك_موظفين|فوترة|أخرى"}}
- requires_followup: true/false
- followup_priority: {{"en": "low|medium|high|urgent", "ar": "منخفضة|متوسطة|عالية|عاجلة"}}
- recommended_actions: {{
"en": ["Action 1", "Action 2", "Action 3"],
"ar": ["إجراء 1", "إجراء 2", "إجراء 3"]
}}
H. BUSINESS INTELLIGENCE METRICS (NEW)
- patient_satisfaction_score: 0-100 (overall satisfaction estimate)
- nps_likelihood: -100 to 100 (Net Promoter Score estimate: would recommend hospital?)
- retention_risk: {{"level": "low|medium|high", "score": 0.0-1.0}}
- reputation_impact: {{"level": "positive|neutral|negative|severe", "score": -1.0 to 1.0}}
- compliance_concerns: {{"present": true/false, "types": ["HIPAA|safety|ethics|other"]}}
I. PATIENT JOURNEY TOUCHPOINTS (NEW)
Identify which touchpoints are mentioned:
- touchpoints: {{
"admission": true/false,
"waiting_area": true/false,
"consultation": true/false,
"diagnosis": true/false,
"treatment": true/false,
"nursing_care": true/false,
"medication": true/false,
"discharge": true/false,
"billing": true/false,
"follow_up": true/false
}}
J. COMPETITIVE INSIGHTS (NEW)
- mentions_competitors: true/false
- comparison_sentiment: {{"en": "favorable|unfavorable|neutral", "ar": "مواتي|غير_مواتي|محايد"}}
- unique_selling_points: {{"en": ["USP1", "USP2"], "ar": ["نقطة1", "نقطة2"]}}
- improvement_opportunities: {{"en": ["Opp1", "Opp2"], "ar": ["فرصة1", "فرصة2"]}}
RETURN ONLY VALID JSON IN THIS EXACT FORMAT:
{{
"comment_index": 0,
"sentiment": {{
"classification": {{"en": "positive", "ar": "إيجابي"}},
"score": 0.85,
"confidence": 0.92,
"urgency_level": {{"en": "low", "ar": "منخفض"}}
}},
"summaries": {{
"en": "The patient expressed high satisfaction with Dr. Ahmed's thorough examination and clear explanation of the treatment plan. They appreciated the nursing staff's attentiveness but mentioned a 45-minute wait time in the cardiology department. Overall positive experience with room for improvement in scheduling.",
"ar": "أعرب المريض عن رضاه الكبير عن فحص د. أحمد الشامل وشرحه الواضح لخطة العلاج. وأشاد باهتمام طاقم التمريض لكنه أشار إلى وقت انتظار 45 دقيقة في قسم القلب. تجربة إيجابية بشكل عام مع مجال للتحسين في الجدولة."
}},
"keywords": {{
"en": ["excellent care", "Dr. Ahmed", "thorough examination", "wait time", "cardiology", "nursing staff", "treatment plan"],
"ar": ["رعاية ممتازة", "د. أحمد", "فحص شامل", "وقت الانتظار", "قسم القلب", "طاقم التمريض", "خطة العلاج"]
}},
"topics": {{
"en": ["Clinical Care Quality", "Doctor-Patient Communication", "Wait Times", "Nursing Care", "Cardiology Services"],
"ar": ["جودة الرعاية السريرية", "التواصل بين الطبيب والمريض", "أوقات الانتظار", "الرعاية التمريضية", "خدمات القلب"]
}},
"entities": [
{{
"text": {{"en": "Dr. Ahmed", "ar": "د. أحمد"}},
"type": {{"en": "DOCTOR", "ar": "طبيب"}}
}},
{{
"text": {{"en": "Cardiology Department", "ar": "قسم القلب"}},
"type": {{"en": "DEPARTMENT", "ar": "قسم"}}
}}
],
"emotions": {{
"joy": 0.8,
"anger": 0.15,
"sadness": 0.0,
"fear": 0.05,
"surprise": 0.1,
"disgust": 0.0,
"trust": 0.85,
"anticipation": 0.7,
"labels": {{
"joy": {{"en": "Satisfaction/Gratitude", "ar": "رضا/امتنان"}},
"anger": {{"en": "Frustration", "ar": "إحباط"}},
"sadness": {{"en": "Disappointment", "ar": "خيبة أمل"}},
"fear": {{"en": "Anxiety", "ar": "قلق"}},
"surprise": {{"en": "Surprise", "ar": "مفاجأة"}},
"disgust": {{"en": "Disgust", "ar": "اشمئزاز"}},
"trust": {{"en": "Trust/Confidence", "ar": "ثقة/طمأنينة"}},
"anticipation": {{"en": "Hope/Expectation", "ar": "أمل/توقع"}}
}}
}},
"actionable_insights": {{
"primary_concern": {{"en": "Extended wait times in cardiology", "ar": "أوقات انتظار طويلة في قسم القلب"}},
"affected_department": {{"en": "Cardiology", "ar": "قسم القلب"}},
"service_quality_indicators": {{
"clinical_care": 9,
"staff_behavior": 9,
"facility_condition": 8,
"wait_time": 6,
"communication": 9,
"overall_experience": 8
}},
"complaint_type": {{"en": "service", "ar": "خدمة"}},
"requires_followup": false,
"followup_priority": {{"en": "low", "ar": "منخفضة"}},
"recommended_actions": {{
"en": [
"Review cardiology department scheduling system",
"Recognize Dr. Ahmed for excellent patient communication",
"Implement wait time reduction strategies in cardiology"
],
"ar": [
"مراجعة نظام الجدولة في قسم القلب",
"تكريم د. أحمد لتميزه في التواصل مع المرضى",
"تطبيق استراتيجيات تقليل وقت الانتظار في قسم القلب"
]
}}
}},
"business_intelligence": {{
"patient_satisfaction_score": 82,
"nps_likelihood": 65,
"retention_risk": {{"level": "low", "score": 0.15}},
"reputation_impact": {{"level": "positive", "score": 0.7}},
"compliance_concerns": {{"present": false, "types": []}}
}},
"patient_journey": {{
"touchpoints": {{
"admission": false,
"waiting_area": true,
"consultation": true,
"diagnosis": false,
"treatment": true,
"nursing_care": true,
"medication": false,
"discharge": false,
"billing": false,
"follow_up": false
}}
}},
"competitive_insights": {{
"mentions_competitors": false,
"comparison_sentiment": {{"en": "neutral", "ar": "محايد"}},
"unique_selling_points": {{
"en": ["Excellent physician communication", "Attentive nursing staff"],
"ar": ["تواصل ممتاز للأطباء", "طاقم تمريض منتبه"]
}},
"improvement_opportunities": {{
"en": ["Optimize appointment scheduling", "Reduce cardiology wait times"],
"ar": ["تحسين جدولة المواعيد", "تقليل أوقات الانتظار في القلب"]
}}
}}
}}
IMPORTANT: Return ONLY the JSON object, no additional text or markdown formatting."""
logger.info(f"Prompt length: {len(prompt)} characters")
headers = {
'Authorization': f'Bearer {self.api_key}',
'Content-Type': 'application/json',
'HTTP-Referer': getattr(settings, 'SITE_URL', 'http://localhost'),
'X-Title': 'Healthcare Patient Experience Analyzer'
}
payload = {
'model': self.model,
'messages': [
{
'role': 'system',
'content': 'You are an expert healthcare patient experience analyst specializing in converting patient feedback into actionable business intelligence for hospital quality improvement. Always respond with valid JSON only, no markdown formatting.'
},
{
'role': 'user',
'content': prompt
}
],
'max_tokens': self.DEFAULT_MAX_TOKENS,
'temperature': self.DEFAULT_TEMPERATURE
}
logger.info(f"Request payload prepared:")
logger.info(f" - Model: {payload['model']}")
logger.info(f" - Max tokens: {payload['max_tokens']}")
logger.info(f" - Temperature: {payload['temperature']}")
with httpx.Client(timeout=self.timeout) as client:
response = client.post(
self.api_url,
headers=headers,
json=payload
)
logger.info(f"Response status: {response.status_code}")
if response.status_code != 200:
logger.error(f"API returned status {response.status_code}: {response.text}")
return {
'success': False,
'error': f'API error: {response.status_code} - {response.text}'
}
data = response.json()
# Extract analysis from response
if 'choices' in data and len(data['choices']) > 0:
content = data['choices'][0]['message']['content']
# Parse JSON response
try:
# Clean up response
content = content.strip()
# Remove markdown code blocks if present
if content.startswith('```json'):
content = content[7:]
elif content.startswith('```'):
content = content[3:]
if content.endswith('```'):
content = content[:-3]
content = content.strip()
analysis_data = json.loads(content)
# Extract metadata
metadata = {
'model': self.model,
'prompt_tokens': data.get('usage', {}).get('prompt_tokens', 0),
'completion_tokens': data.get('usage', {}).get('completion_tokens', 0),
'total_tokens': data.get('usage', {}).get('total_tokens', 0),
'analyzed_at': timezone.now().isoformat()
}
logger.info(f"Analysis completed successfully for comment {comment_id}")
logger.info(f" - Patient Satisfaction Score: {analysis_data.get('business_intelligence', {}).get('patient_satisfaction_score', 'N/A')}")
logger.info(f" - Sentiment: {analysis_data.get('sentiment', {}).get('classification', {}).get('en', 'N/A')}")
logger.info(f" - Requires Follow-up: {analysis_data.get('actionable_insights', {}).get('requires_followup', 'N/A')}")
return {
'success': True,
'comment_id': comment_id,
'analysis': analysis_data,
'metadata': metadata
}
except json.JSONDecodeError as e:
logger.error(f"JSON parse error: {e}")
logger.error(f"Content: {content[:500]}...")
return {
'success': False,
'error': f'Invalid JSON response from API: {str(e)}'
}
else:
logger.error(f"No choices found in response: {data}")
return {
'success': False,
'error': 'No analysis returned from API'
}
except httpx.HTTPStatusError as e:
logger.error(f"HTTP status error: {e}")
return {
'success': False,
'error': f'API error: {e.response.status_code} - {str(e)}'
}
except httpx.RequestError as e:
logger.error(f"Request error: {e}")
return {
'success': False,
'error': f'Request failed: {str(e)}'
}
except Exception as e:
logger.error(f"Unexpected error: {e}", exc_info=True)
return {
'success': False,
'error': f'Unexpected error: {str(e)}'
}
def is_configured(self) -> bool:
"""Check if service is properly configured."""
return bool(self.api_key)

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apps/social/services/analysis_service.py
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@ -1,364 +0,0 @@
"""
Analysis service for orchestrating AI-powered comment analysis.
Coordinates between SocialMediaComment model and OpenRouter service.
"""
import logging
from typing import List, Dict, Any, Optional
from decimal import Decimal
from datetime import datetime, timedelta
from django.conf import settings
from django.utils import timezone
from django.db import models
from ..models import SocialMediaComment
from .openrouter_service import OpenRouterService
logger = logging.getLogger(__name__)
class AnalysisService:
"""
Service for managing AI analysis of social media comments.
Handles batching, filtering, and updating comments with analysis results.
"""
def __init__(self):
"""Initialize the analysis service."""
self.openrouter_service = OpenRouterService()
self.batch_size = getattr(settings, 'ANALYSIS_BATCH_SIZE', 10)
if not self.openrouter_service.is_configured():
logger.warning("OpenRouter service not properly configured")
else:
logger.info(f"Analysis service initialized (batch_size: {self.batch_size})")
def analyze_pending_comments(
self,
limit: Optional[int] = None,
platform: Optional[str] = None,
hours_ago: Optional[int] = None
) -> Dict[str, Any]:
"""
Analyze comments that haven't been analyzed yet.
Args:
limit: Maximum number of comments to analyze
platform: Filter by platform (optional)
hours_ago: Only analyze comments scraped in the last N hours
Returns:
Dictionary with analysis statistics
"""
if not self.openrouter_service.is_configured():
logger.error("OpenRouter service not configured")
return {
'success': False,
'error': 'OpenRouter service not configured',
'analyzed': 0,
'failed': 0,
'skipped': 0
}
# Build queryset for unanalyzed comments (check if ai_analysis is empty)
# Using Q() for complex filtering (NULL or empty dict)
from django.db.models import Q
queryset = SocialMediaComment.objects.filter(
Q(ai_analysis__isnull=True) | Q(ai_analysis={})
)
if platform:
queryset = queryset.filter(platform=platform)
if hours_ago:
cutoff_time = timezone.now() - timedelta(hours=hours_ago)
queryset = queryset.filter(scraped_at__gte=cutoff_time)
if limit:
queryset = queryset[:limit]
# Fetch comments
comments = list(queryset)
if not comments:
logger.info("No pending comments to analyze")
return {
'success': True,
'analyzed': 0,
'failed': 0,
'skipped': 0,
'message': 'No pending comments to analyze'
}
logger.info(f"Found {len(comments)} pending comments to analyze")
# Process in batches
analyzed_count = 0
failed_count = 0
skipped_count = 0
for i in range(0, len(comments), self.batch_size):
batch = comments[i:i + self.batch_size]
logger.info(f"Processing batch {i//self.batch_size + 1} ({len(batch)} comments)")
# Prepare batch for API
batch_data = [
{
'id': comment.id,
'text': comment.comments
}
for comment in batch
]
# Analyze batch
result = self.openrouter_service.analyze_comments(batch_data)
if result.get('success'):
# Update comments with analysis results
for analysis in result.get('analyses', []):
try:
comment_id = analysis.get('comment_id')
comment = SocialMediaComment.objects.get(id=comment_id)
# Build new bilingual analysis structure
ai_analysis = {
'sentiment': analysis.get('sentiment', {}),
'summaries': analysis.get('summaries', {}),
'keywords': analysis.get('keywords', {}),
'topics': analysis.get('topics', {}),
'entities': analysis.get('entities', []),
'emotions': analysis.get('emotions', {}),
'metadata': {
**result.get('metadata', {}),
'analyzed_at': timezone.now().isoformat()
}
}
# Update with bilingual analysis structure
comment.ai_analysis = ai_analysis
comment.save()
analyzed_count += 1
logger.debug(f"Updated comment {comment_id} with bilingual analysis")
except SocialMediaComment.DoesNotExist:
logger.warning(f"Comment {analysis.get('comment_id')} not found")
failed_count += 1
except Exception as e:
logger.error(f"Error updating comment {comment_id}: {e}")
failed_count += 1
else:
error = result.get('error', 'Unknown error')
logger.error(f"Batch analysis failed: {error}")
failed_count += len(batch)
# Calculate skipped (comments that were analyzed during processing)
skipped_count = len(comments) - analyzed_count - failed_count
logger.info(
f"Analysis complete: {analyzed_count} analyzed, "
f"{failed_count} failed, {skipped_count} skipped"
)
return {
'success': True,
'analyzed': analyzed_count,
'failed': failed_count,
'skipped': skipped_count,
'total': len(comments)
}
def analyze_comments_by_platform(self, platform: str, limit: int = 100) -> Dict[str, Any]:
"""
Analyze comments from a specific platform.
Args:
platform: Platform name (e.g., 'youtube', 'facebook')
limit: Maximum number of comments to analyze
Returns:
Dictionary with analysis statistics
"""
logger.info(f"Analyzing comments from platform: {platform}")
return self.analyze_pending_comments(limit=limit, platform=platform)
def analyze_recent_comments(self, hours: int = 24, limit: int = 100) -> Dict[str, Any]:
"""
Analyze comments scraped in the last N hours.
Args:
hours: Number of hours to look back
limit: Maximum number of comments to analyze
Returns:
Dictionary with analysis statistics
"""
logger.info(f"Analyzing comments from last {hours} hours")
return self.analyze_pending_comments(limit=limit, hours_ago=hours)
def get_analysis_statistics(
self,
platform: Optional[str] = None,
days: int = 30
) -> Dict[str, Any]:
"""
Get statistics about analyzed comments using ai_analysis structure.
Args:
platform: Filter by platform (optional)
days: Number of days to look back
Returns:
Dictionary with analysis statistics
"""
cutoff_date = timezone.now() - timedelta(days=days)
queryset = SocialMediaComment.objects.filter(
scraped_at__gte=cutoff_date
)
if platform:
queryset = queryset.filter(platform=platform)
total_comments = queryset.count()
# Count analyzed comments (those with ai_analysis populated)
analyzed_comments = 0
sentiment_counts = {'positive': 0, 'negative': 0, 'neutral': 0}
confidence_scores = []
for comment in queryset:
if comment.ai_analysis:
analyzed_comments += 1
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en', 'neutral')
if sentiment in sentiment_counts:
sentiment_counts[sentiment] += 1
confidence = comment.ai_analysis.get('sentiment', {}).get('confidence', 0)
if confidence:
confidence_scores.append(confidence)
# Calculate average confidence
avg_confidence = sum(confidence_scores) / len(confidence_scores) if confidence_scores else 0
return {
'total_comments': total_comments,
'analyzed_comments': analyzed_comments,
'unanalyzed_comments': total_comments - analyzed_comments,
'analysis_rate': (analyzed_comments / total_comments * 100) if total_comments > 0 else 0,
'sentiment_distribution': sentiment_counts,
'average_confidence': float(avg_confidence),
'platform': platform or 'all'
}
def reanalyze_comment(self, comment_id: int) -> Dict[str, Any]:
"""
Re-analyze a specific comment.
Args:
comment_id: ID of the comment to re-analyze
Returns:
Dictionary with result
"""
try:
comment = SocialMediaComment.objects.get(id=comment_id)
except SocialMediaComment.DoesNotExist:
return {
'success': False,
'error': f'Comment {comment_id} not found'
}
if not self.openrouter_service.is_configured():
return {
'success': False,
'error': 'OpenRouter service not configured'
}
# Prepare single comment for analysis
batch_data = [{'id': comment.id, 'text': comment.comments}]
# Analyze
result = self.openrouter_service.analyze_comments(batch_data)
if result.get('success'):
analysis = result.get('analyses', [{}])[0] if result.get('analyses') else {}
# Build new bilingual analysis structure
ai_analysis = {
'sentiment': analysis.get('sentiment', {}),
'summaries': analysis.get('summaries', {}),
'keywords': analysis.get('keywords', {}),
'topics': analysis.get('topics', {}),
'entities': analysis.get('entities', []),
'emotions': analysis.get('emotions', {}),
'metadata': {
**result.get('metadata', {}),
'analyzed_at': timezone.now().isoformat()
}
}
# Update comment with bilingual analysis structure
comment.ai_analysis = ai_analysis
comment.save()
sentiment_en = ai_analysis.get('sentiment', {}).get('classification', {}).get('en')
confidence_val = ai_analysis.get('sentiment', {}).get('confidence', 0)
return {
'success': True,
'comment_id': comment_id,
'sentiment': sentiment_en,
'confidence': float(confidence_val)
}
else:
return {
'success': False,
'error': result.get('error', 'Unknown error')
}
def get_top_keywords(
self,
platform: Optional[str] = None,
limit: int = 20,
days: int = 30
) -> List[Dict[str, Any]]:
"""
Get most common keywords from analyzed comments using ai_analysis structure.
Args:
platform: Filter by platform (optional)
limit: Maximum number of keywords to return
days: Number of days to look back
Returns:
List of keyword dictionaries with 'keyword' and 'count' keys
"""
cutoff_date = timezone.now() - timedelta(days=days)
queryset = SocialMediaComment.objects.filter(
scraped_at__gte=cutoff_date,
ai_analysis__isnull=False
).exclude(ai_analysis={})
if platform:
queryset = queryset.filter(platform=platform)
# Count keywords from ai_analysis
keyword_counts = {}
for comment in queryset:
keywords_en = comment.ai_analysis.get('keywords', {}).get('en', [])
for keyword in keywords_en:
keyword_counts[keyword] = keyword_counts.get(keyword, 0) + 1
# Sort by count and return top N
sorted_keywords = sorted(
keyword_counts.items(),
key=lambda x: x[1],
reverse=True
)[:limit]
return [
{'keyword': keyword, 'count': count}
for keyword, count in sorted_keywords
]

366
apps/social/services/comment_service.py
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@ -1,366 +0,0 @@
"""
Service class for managing social media comment scraping and database operations.
"""
import logging
from typing import List, Dict, Any, Optional
from datetime import datetime
from django.conf import settings
from ..models import SocialMediaComment
from ..scrapers import YouTubeScraper, FacebookScraper, InstagramScraper, TwitterScraper, LinkedInScraper, GoogleReviewsScraper
logger = logging.getLogger(__name__)
class CommentService:
"""
Service class to manage scraping from all social media platforms
and saving comments to the database.
"""
def __init__(self):
"""Initialize the comment service."""
self.scrapers = {}
self._initialize_scrapers()
def _initialize_scrapers(self):
"""Initialize all platform scrapers with configuration from settings."""
# YouTube scraper
youtube_config = {
'api_key': getattr(settings, 'YOUTUBE_API_KEY', None),
'channel_id': getattr(settings, 'YOUTUBE_CHANNEL_ID', None),
}
if youtube_config['api_key']:
self.scrapers['youtube'] = YouTubeScraper(youtube_config)
# Facebook scraper
facebook_config = {
'access_token': getattr(settings, 'FACEBOOK_ACCESS_TOKEN', None),
'page_id': getattr(settings, 'FACEBOOK_PAGE_ID', None),
}
if facebook_config['access_token']:
self.scrapers['facebook'] = FacebookScraper(facebook_config)
# Instagram scraper
instagram_config = {
'access_token': getattr(settings, 'INSTAGRAM_ACCESS_TOKEN', None),
'account_id': getattr(settings, 'INSTAGRAM_ACCOUNT_ID', None),
}
if instagram_config['access_token']:
self.scrapers['instagram'] = InstagramScraper(instagram_config)
# Twitter/X scraper
twitter_config = {
'bearer_token': getattr(settings, 'TWITTER_BEARER_TOKEN', None),
'username': getattr(settings, 'TWITTER_USERNAME', None),
}
if twitter_config['bearer_token']:
self.scrapers['twitter'] = TwitterScraper(twitter_config)
# LinkedIn scraper
linkedin_config = {
'access_token': getattr(settings, 'LINKEDIN_ACCESS_TOKEN', None),
'organization_id': getattr(settings, 'LINKEDIN_ORGANIZATION_ID', None),
}
if linkedin_config['access_token']:
self.scrapers['linkedin'] = LinkedInScraper(linkedin_config)
# Google Reviews scraper (requires credentials)
google_reviews_config = {
'credentials_file': getattr(settings, 'GOOGLE_CREDENTIALS_FILE', None),
'token_file': getattr(settings, 'GOOGLE_TOKEN_FILE', 'token.json'),
'locations': getattr(settings, 'GOOGLE_LOCATIONS', None),
}
if google_reviews_config['credentials_file']:
try:
self.scrapers['google_reviews'] = GoogleReviewsScraper(google_reviews_config)
except (FileNotFoundError, Exception) as e:
logger.warning(f"Google Reviews scraper not initialized: {e}")
logger.info("Google Reviews will be skipped. See GOOGLE_REVIEWS_INTEGRATION_GUIDE.md for setup.")
logger.info(f"Initialized scrapers: {list(self.scrapers.keys())}")
def scrape_and_save(
self,
platforms: Optional[List[str]] = None,
platform_id: Optional[str] = None
) -> Dict[str, Dict[str, int]]:
"""
Scrape comments from specified platforms and save to database.
Args:
platforms: List of platforms to scrape (e.g., ['youtube', 'facebook'])
If None, scrape all available platforms
platform_id: Optional platform-specific ID (channel_id, page_id, account_id)
Returns:
Dictionary with platform names as keys and dictionaries containing:
- 'new': Number of new comments added
- 'updated': Number of existing comments updated
"""
if platforms is None:
platforms = list(self.scrapers.keys())
results = {}
for platform in platforms:
if platform not in self.scrapers:
logger.warning(f"Scraper for {platform} not initialized")
results[platform] = {'new': 0, 'updated': 0}
continue
try:
logger.info(f"Starting scraping for {platform}")
comments = self.scrapers[platform].scrape_comments(platform_id=platform_id)
save_result = self._save_comments(platform, comments)
results[platform] = save_result
logger.info(f"From {platform}: {save_result['new']} new, {save_result['updated']} updated comments")
except Exception as e:
logger.error(f"Error scraping {platform}: {e}")
results[platform] = {'new': 0, 'updated': 0}
return results
def scrape_youtube(
self,
channel_id: Optional[str] = None,
save_to_db: bool = True
) -> List[Dict[str, Any]]:
"""
Scrape comments from YouTube.
Args:
channel_id: YouTube channel ID
save_to_db: If True, save comments to database
Returns:
List of scraped comments
"""
if 'youtube' not in self.scrapers:
raise ValueError("YouTube scraper not initialized")
comments = self.scrapers['youtube'].scrape_comments(channel_id=channel_id)
if save_to_db:
self._save_comments('youtube', comments)
return comments
def scrape_facebook(
self,
page_id: Optional[str] = None,
save_to_db: bool = True
) -> List[Dict[str, Any]]:
"""
Scrape comments from Facebook.
Args:
page_id: Facebook page ID
save_to_db: If True, save comments to database
Returns:
List of scraped comments
"""
if 'facebook' not in self.scrapers:
raise ValueError("Facebook scraper not initialized")
comments = self.scrapers['facebook'].scrape_comments(page_id=page_id)
if save_to_db:
self._save_comments('facebook', comments)
return comments
def scrape_instagram(
self,
account_id: Optional[str] = None,
save_to_db: bool = True
) -> List[Dict[str, Any]]:
"""
Scrape comments from Instagram.
Args:
account_id: Instagram account ID
save_to_db: If True, save comments to database
Returns:
List of scraped comments
"""
if 'instagram' not in self.scrapers:
raise ValueError("Instagram scraper not initialized")
comments = self.scrapers['instagram'].scrape_comments(account_id=account_id)
if save_to_db:
self._save_comments('instagram', comments)
return comments
def scrape_twitter(
self,
username: Optional[str] = None,
save_to_db: bool = True
) -> List[Dict[str, Any]]:
"""
Scrape comments (replies) from Twitter/X.
Args:
username: Twitter username
save_to_db: If True, save comments to database
Returns:
List of scraped comments
"""
if 'twitter' not in self.scrapers:
raise ValueError("Twitter scraper not initialized")
comments = self.scrapers['twitter'].scrape_comments(username=username)
if save_to_db:
self._save_comments('twitter', comments)
return comments
def scrape_linkedin(
self,
organization_id: Optional[str] = None,
save_to_db: bool = True
) -> List[Dict[str, Any]]:
"""
Scrape comments from LinkedIn organization posts.
Args:
organization_id: LinkedIn organization URN (e.g., 'urn:li:organization:1234567')
save_to_db: If True, save comments to database
Returns:
List of scraped comments
"""
if 'linkedin' not in self.scrapers:
raise ValueError("LinkedIn scraper not initialized")
comments = self.scrapers['linkedin'].scrape_comments(organization_id=organization_id)
if save_to_db:
self._save_comments('linkedin', comments)
return comments
def scrape_google_reviews(
self,
location_names: Optional[List[str]] = None,
save_to_db: bool = True
) -> List[Dict[str, Any]]:
"""
Scrape Google Reviews from business locations.
Args:
location_names: Optional list of location names to scrape (uses all locations if None)
save_to_db: If True, save comments to database
Returns:
List of scraped reviews
"""
if 'google_reviews' not in self.scrapers:
raise ValueError("Google Reviews scraper not initialized")
comments = self.scrapers['google_reviews'].scrape_comments(location_names=location_names)
if save_to_db:
self._save_comments('google_reviews', comments)
return comments
def _save_comments(self, platform: str, comments: List[Dict[str, Any]]) -> Dict[str, int]:
"""
Save comments to database using get_or_create to prevent duplicates.
Updates existing comments with fresh data (likes, etc.).
Args:
platform: Platform name
comments: List of comment dictionaries
Returns:
Dictionary with:
- 'new': Number of new comments added
- 'updated': Number of existing comments updated
"""
new_count = 0
updated_count = 0
for comment_data in comments:
try:
# Parse published_at timestamp
published_at = None
if comment_data.get('published_at'):
try:
published_at = datetime.fromisoformat(
comment_data['published_at'].replace('Z', '+00:00')
)
except (ValueError, AttributeError):
pass
# Prepare default values
defaults = {
'comments': comment_data.get('comments', ''),
'author': comment_data.get('author', ''),
'post_id': comment_data.get('post_id'),
'media_url': comment_data.get('media_url'),
'like_count': comment_data.get('like_count', 0),
'reply_count': comment_data.get('reply_count', 0),
'rating': comment_data.get('rating'),
'published_at': published_at,
'raw_data': comment_data.get('raw_data', {})
}
# Use get_or_create to prevent duplicates
comment, created = SocialMediaComment.objects.get_or_create(
platform=platform,
comment_id=comment_data['comment_id'],
defaults=defaults
)
if created:
# New comment was created
new_count += 1
logger.debug(f"New comment added: {comment_data['comment_id']}")
else:
# Comment already exists, update it with fresh data
comment.comments = defaults['comments']
comment.author = defaults['author']
comment.post_id = defaults['post_id']
comment.media_url = defaults['media_url']
comment.like_count = defaults['like_count']
comment.reply_count = defaults['reply_count']
comment.rating = defaults['rating']
if defaults['published_at']:
comment.published_at = defaults['published_at']
comment.raw_data = defaults['raw_data']
comment.save()
updated_count += 1
logger.debug(f"Comment updated: {comment_data['comment_id']}")
except Exception as e:
logger.error(f"Error saving comment {comment_data.get('comment_id')}: {e}")
logger.info(f"Saved comments for {platform}: {new_count} new, {updated_count} updated")
return {'new': new_count, 'updated': updated_count}
def get_latest_comments(self, platform: Optional[str] = None, limit: int = 100) -> List[SocialMediaComment]:
"""
Get latest comments from database.
Args:
platform: Filter by platform (optional)
limit: Maximum number of comments to return
Returns:
List of SocialMediaComment objects
"""
queryset = SocialMediaComment.objects.all()
if platform:
queryset = queryset.filter(platform=platform)
return list(queryset.order_by('-published_at')[:limit])

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import json
import time
import logging
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import Flow
from googleapiclient.discovery import build
from google.auth.transport.requests import Request
from googleapiclient.errors import HttpError
from django.conf import settings
from django.utils import timezone
from apps.social.utils.google import SCOPES, API_VERSION_MYBUSINESS, API_VERSION_ACCOUNT_MGMT
logger = logging.getLogger(__name__)
class GoogleAPIError(Exception):
pass
class GoogleBusinessService:
@staticmethod
def _get_credentials_object(account):
creds_dict = account.credentials_json
if 'token' not in creds_dict:
raise GoogleAPIError("Missing token.")
creds = Credentials.from_authorized_user_info(creds_dict, SCOPES)
if creds.expired and creds.refresh_token:
try:
# FIX: Model field is 'name', not 'account_name'
logger.info(f"Refreshing token for {account.name}...")
creds.refresh(Request())
account.credentials_json = json.loads(creds.to_json())
account.save()
except Exception as e:
raise GoogleAPIError(f"Token refresh failed: {e}")
return creds
@staticmethod
def get_service(account, api_name='mybusiness', api_version='v4'):
creds = GoogleBusinessService._get_credentials_object(account)
return build(api_name, api_version, credentials=creds)
@staticmethod
def get_auth_url(request):
flow = Flow.from_client_secrets_file(
settings.GMB_CLIENT_SECRETS_FILE,
scopes=SCOPES,
redirect_uri=settings.GMB_REDIRECT_URI
)
state = request.session.session_key
flow.redirect_uri = settings.GMB_REDIRECT_URI
auth_url, _ = flow.authorization_url(access_type='offline', prompt='consent', state=state)
return auth_url
@staticmethod
def exchange_code_for_token(code):
flow = Flow.from_client_secrets_file(
settings.GMB_CLIENT_SECRETS_FILE,
scopes=SCOPES,
redirect_uri=settings.GMB_REDIRECT_URI
)
try:
flow.fetch_token(code=code)
return json.loads(flow.credentials.to_json())
except Exception as e:
raise GoogleAPIError(f"Token exchange failed: {e}")
@staticmethod
def fetch_locations(account):
service = GoogleBusinessService.get_service(account, 'mybusinessaccountmanagement', API_VERSION_ACCOUNT_MGMT)
locations = []
page_token = None
while True:
try:
request = service.accounts().listLocations(
parent=account.account_id, # Assuming account_id is stored correctly (e.g., "accounts/123")
pageSize=100,
pageToken=page_token,
readMask="name,title,storeCode"
)
response = request.execute()
locations.extend(response.get('locations', []))
page_token = response.get('nextPageToken')
if not page_token:
break
time.sleep(0.5)
except HttpError as e:
logger.error(f"Error fetching locations: {e}")
break
return locations
@staticmethod
def fetch_reviews_delta(account, location):
"""
Fetches reviews.
'location' argument here is an instance of SocialContent model.
"""
service = GoogleBusinessService.get_service(account, 'mybusiness', API_VERSION_MYBUSINESS)
reviews = []
next_page_token = None
while True:
try:
request = service.accounts().locations().reviews().list(
# FIX: Model field is 'content_id', not 'location_id'
parent=location.content_id,
pageSize=50,
pageToken=next_page_token,
orderBy="update_time desc"
)
response = request.execute()
batch = response.get('reviews', [])
for r_data in batch:
update_str = r_data.get('updateTime')
# Note: You are manually handling 'Z' here. parse_datetime() is safer,
# but this works if you prefer it.
if update_str.endswith('Z'):
update_str = update_str[:-1] + '+00:00'
try:
r_time = timezone.make_aware(timezone.datetime.strptime(update_str, "%Y-%m-%dT%H:%M:%S%z"))
except:
r_time = timezone.now()
# FIX: Model field is 'last_comment_sync_at', not 'last_review_sync_at'
if r_time <= location.last_comment_sync_at:
return reviews
reviews.append(r_data)
next_page_token = response.get('nextPageToken')
if not next_page_token:
break
time.sleep(0.5)
except HttpError as e:
if e.resp.status == 429:
time.sleep(10)
continue
logger.error(f"API Error fetching reviews: {e}")
break
return reviews
@staticmethod
def post_reply(account, review_name, comment_text):
service = GoogleBusinessService.get_service(account, 'mybusiness', API_VERSION_MYBUSINESS)
try:
request = service.accounts().locations().reviews().reply(
name=review_name,
body={'comment': comment_text}
)
return request.execute()
except HttpError as e:
raise GoogleAPIError(f"Failed to post reply: {e}")

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import requests
import time
import datetime
import hmac
import hashlib
from urllib.parse import urlencode, quote
from django.conf import settings
from django.utils import timezone
from apps.social.utils.linkedin import LinkedInConstants
from apps.social.models import SocialAccount
class LinkedInAPIError(Exception):
"""Custom exception for LinkedIn API errors"""
pass
class LinkedInService:
"""Service class for LinkedIn API interactions (RestLi 2.0)"""
# ==========================================
# HELPER METHODS
# ==========================================
@staticmethod
def _get_headers(token):
"""Generate headers for LinkedIn API requests"""
return {
"Authorization": f"Bearer {token}",
"Linkedin-Version": LinkedInConstants.API_VERSION,
"X-Restli-Protocol-Version": "2.0.0",
"Content-Type": "application/json"
}
@staticmethod
def _normalize_urn(platform_id, urn_type="organization"):
"""
Normalize platform ID to proper URN format.
Args:
platform_id: Either a bare ID or full URN
urn_type: Type of URN (organization, person, etc.)
Returns:
Properly formatted URN string
"""
if not platform_id:
raise ValueError("platform_id cannot be empty")
# If it already looks like a URN (contains colons), return it as-is.
# This prevents corrupting 'urn:li:share:123' into 'urn:li:organization:urn:li:share:123'
if ":" in platform_id:
return platform_id
urn_prefix = f"urn:li:{urn_type}:"
return f"{urn_prefix}{platform_id}"
@staticmethod
def _encode_urn(urn):
"""URL encode URN for use in API requests"""
return quote(urn, safe='')
@staticmethod
def _parse_timestamp(time_ms):
"""
Convert LinkedIn timestamp (milliseconds since epoch) to Django timezone-aware datetime.
Args:
time_ms: Timestamp in milliseconds
Returns:
Timezone-aware datetime object or current time if parsing fails
"""
if not time_ms:
return timezone.now()
try:
# LinkedIn returns milliseconds, divide by 1000 for seconds
return datetime.datetime.fromtimestamp(
time_ms / 1000.0,
tz=datetime.timezone.utc
)
except (ValueError, OSError):
return timezone.now()
# ==========================================
# AUTHENTICATION
# ==========================================
@staticmethod
def get_auth_url(state=None):
"""Generate LinkedIn OAuth authorization URL."""
params = {
"response_type": "code",
"client_id": settings.LINKEDIN_CLIENT_ID,
"redirect_uri": settings.LINKEDIN_REDIRECT_URI,
"scope": " ".join(LinkedInConstants.SCOPES),
"state": state or "random_state_123",
}
return f"{LinkedInConstants.AUTH_URL}?{urlencode(params)}"
@staticmethod
def exchange_code_for_token(code):
"""Exchange authorization code for access token."""
payload = {
"grant_type": "authorization_code",
"code": code,
"redirect_uri": settings.LINKEDIN_REDIRECT_URI,
"client_id": settings.LINKEDIN_CLIENT_ID,
"client_secret": settings.LINKEDIN_CLIENT_SECRET
}
response = requests.post(LinkedInConstants.TOKEN_URL, data=payload)
if response.status_code != 200:
raise LinkedInAPIError(f"Token Exchange Failed: {response.text}")
return response.json()
@staticmethod
def refresh_access_token(account):
"""Refresh access token if expired or expiring soon."""
if not account.is_active:
raise LinkedInAPIError("Account is inactive")
# Refresh if token expires within 15 minutes
if timezone.now() >= account.expires_at - datetime.timedelta(minutes=15):
payload = {
"grant_type": "refresh_token",
"refresh_token": account.refresh_token,
"client_id": settings.LINKEDIN_CLIENT_ID,
"client_secret": settings.LINKEDIN_CLIENT_SECRET,
}
response = requests.post(LinkedInConstants.TOKEN_URL, data=payload)
if response.status_code != 200:
account.is_active = False
account.save()
raise LinkedInAPIError(f"Refresh Token Expired: {response.text}")
data = response.json()
account.access_token = data['access_token']
account.expires_at = timezone.now() + datetime.timedelta(seconds=data['expires_in'])
if 'refresh_token' in data:
account.refresh_token = data['refresh_token']
account.save()
return account.access_token
# ==========================================
# API REQUEST HANDLER
# ==========================================
@staticmethod
def _make_request(account, method, url, payload=None, retry_count=0):
"""Make authenticated API request with rate limit handling."""
token = LinkedInService.refresh_access_token(account)
headers = LinkedInService._get_headers(token)
try:
if method == "GET":
response = requests.get(url, headers=headers, params=payload, timeout=30)
elif method == "POST":
response = requests.post(url, headers=headers, json=payload, timeout=30)
elif method == "DELETE":
response = requests.delete(url, headers=headers, params=payload, timeout=30)
else:
raise ValueError(f"Unsupported HTTP method: {method}")
# Handle rate limiting
if response.status_code == 429:
if retry_count >= LinkedInConstants.MAX_RETRIES:
raise LinkedInAPIError("Max retries exceeded for rate limit")
reset_time = int(response.headers.get('X-RateLimit-Reset', time.time() + 60))
sleep_duration = max(1, reset_time - int(time.time()))
print(f"[Rate Limit] Sleeping for {sleep_duration}s (attempt {retry_count + 1})")
time.sleep(sleep_duration)
return LinkedInService._make_request(account, method, url, payload, retry_count + 1)
# Handle 404 as empty response (resource not found)
if response.status_code == 404:
return {}
# Handle other errors
if response.status_code >= 400:
raise LinkedInAPIError(f"API Error {response.status_code}: {response.text}")
return response.json()
except requests.exceptions.RequestException as e:
raise LinkedInAPIError(f"Request failed: {str(e)}")
# ==========================================
# POSTS API
# ==========================================
@staticmethod
def fetch_posts(account, count=50):
"""
Fetch organization posts using new Posts API.
Returns post objects containing full URNs (e.g., urn:li:share:123).
"""
posts = []
org_urn = LinkedInService._normalize_urn(account.platform_id, "organization")
params = {
"author": org_urn,
"q": "author",
"count": min(count, LinkedInConstants.MAX_PAGE_SIZE),
"sortBy": "LAST_MODIFIED"
}
try:
data = LinkedInService._make_request(
account,
"GET",
f"{LinkedInConstants.BASE_URL}/posts", # versioned endpoint
payload=params
)
posts = data.get('elements', [])
except LinkedInAPIError as e:
print(f"Error fetching posts: {e}")
return posts
# ==========================================
# COMMENTS API
# ==========================================
@staticmethod
def fetch_all_comments(account, post_urn):
"""
Fetch ALL comments for a post (for complete historical sync).
post_urn: Must be full URN (e.g., urn:li:share:123)
"""
comments = []
start = 0
batch_size = LinkedInConstants.DEFAULT_PAGE_SIZE
encoded_urn = LinkedInService._encode_urn(post_urn)
while True:
params = {"count": batch_size, "start": start}
try:
data = LinkedInService._make_request(
account,
"GET",
f"{LinkedInConstants.BASE_URL}/socialActions/{encoded_urn}/comments",
payload=params
)
except LinkedInAPIError as e:
print(f"Error fetching comments: {e}")
break
if not data or 'elements' not in data:
break
elements = data.get('elements', [])
if not elements:
break
for comment in elements:
comment['post_urn'] = post_urn
comments.append(comment)
if len(elements) < batch_size:
break
start += batch_size
time.sleep(0.3)
return comments
@staticmethod
def fetch_comments_limited(account, post_urn, limit=200):
"""Fetch limited number of most recent comments."""
comments = []
start = 0
batch_size = LinkedInConstants.DEFAULT_PAGE_SIZE
encoded_urn = LinkedInService._encode_urn(post_urn)
while len(comments) < limit:
remaining = limit - len(comments)
current_batch = min(batch_size, remaining)
params = {"count": current_batch, "start": start}
try:
data = LinkedInService._make_request(
account,
"GET",
f"{LinkedInConstants.BASE_URL}/socialActions/{encoded_urn}/comments",
payload=params
)
except LinkedInAPIError as e:
print(f"Error fetching comments: {e}")
break
if not data or 'elements' not in data:
break
elements = data.get('elements', [])
if not elements:
break
for comment in elements:
comment['post_urn'] = post_urn
comments.append(comment)
if len(elements) < current_batch:
break
start += current_batch
time.sleep(0.3)
return comments
@staticmethod
def fetch_comments_delta(account, post_urn, since_timestamp=None):
"""Fetch only NEW comments since a specific timestamp."""
comments = []
start = 0
batch_size = LinkedInConstants.DEFAULT_PAGE_SIZE
encoded_urn = LinkedInService._encode_urn(post_urn)
while True:
params = {"count": batch_size, "start": start}
try:
data = LinkedInService._make_request(
account,
"GET",
f"{LinkedInConstants.BASE_URL}/socialActions/{encoded_urn}/comments",
payload=params
)
except LinkedInAPIError as e:
print(f"Error fetching comments: {e}")
break
if not data or 'elements' not in data:
break
elements = data.get('elements', [])
if not elements:
break
# Optimization: Check newest item in batch first
newest_in_batch = elements[0].get('created', {}).get('time')
if since_timestamp and newest_in_batch:
newest_dt = LinkedInService._parse_timestamp(newest_in_batch)
if newest_dt <= since_timestamp:
break # Even the newest comment is old, stop entirely
# Check oldest item to see if we should stop paginating
if since_timestamp and elements:
oldest_in_batch = elements[-1].get('created', {}).get('time')
if oldest_in_batch:
oldest_dt = LinkedInService._parse_timestamp(oldest_in_batch)
if oldest_dt <= since_timestamp:
# Filter only those strictly newer than timestamp
for comment in elements:
c_time_ms = comment.get('created', {}).get('time')
if c_time_ms:
c_dt = LinkedInService._parse_timestamp(c_time_ms)
if c_dt > since_timestamp:
comment['post_urn'] = post_urn
comments.append(comment)
break
for comment in elements:
comment['post_urn'] = post_urn
comments.append(comment)
if len(elements) < batch_size:
break
start += batch_size
time.sleep(0.3)
return comments
@staticmethod
def fetch_single_comment(account, post_urn, comment_id):
"""
Fetch a specific comment by ID (efficient for webhook processing).
"""
encoded_post_urn = LinkedInService._encode_urn(post_urn)
url = f"{LinkedInConstants.BASE_URL}/socialActions/{encoded_post_urn}/comments/{comment_id}"
try:
data = LinkedInService._make_request(account, "GET", url)
if data:
data['post_urn'] = post_urn
return data
except LinkedInAPIError as e:
print(f"Error fetching comment {comment_id}: {e}")
return None
# ==========================================
# COMMENT ACTIONS
# ==========================================
@staticmethod
def post_reply(account, parent_urn, text):
"""
Reply to a post or comment.
parent_urn: URN of the post (urn:li:share:...) or comment (urn:li:comment:...)
"""
encoded_parent_urn = LinkedInService._encode_urn(parent_urn)
url = f"{LinkedInConstants.BASE_URL}/socialActions/{encoded_parent_urn}/comments"
org_urn = LinkedInService._normalize_urn(account.platform_id, "organization")
payload = {
"actor": org_urn,
"message": {
"text": text
}
}
return LinkedInService._make_request(account, "POST", url, payload=payload)
# @staticmethod
# def delete_comment(account, post_urn, comment_id):
# """
# Delete a comment.
# Note: The 'actor' is NOT passed as a query parameter in the new API.
# It is derived from the OAuth Access Token.
# """
# encoded_post_urn = LinkedInService._encode_urn(post_urn)
# # Construct URL
# url = f"{LinkedInConstants.BASE_URL}/socialActions/{encoded_post_urn}/comments/{comment_id}"
# # Make request (payload={} for safe DELETE handling in _make_request)
# LinkedInService._make_request(account, "DELETE", url, payload={})
# return True
# ==========================================
# WEBHOOK UTILITIES
# ==========================================
@staticmethod
def calculate_hmac_sha256(secret_key, message):
"""Calculate HMAC-SHA256 signature."""
if isinstance(message, str):
message = message.encode('utf-8')
if isinstance(secret_key, str):
secret_key = secret_key.encode('utf-8')
return hmac.new(secret_key, message, hashlib.sha256).hexdigest()
@staticmethod
def verify_webhook_signature(received_signature, body_raw, client_secret):
"""Verify webhook signature for authenticity."""
if not received_signature or not body_raw:
return False
calculated_digest = LinkedInService.calculate_hmac_sha256(client_secret, body_raw)
expected_signature = f"hmacsha256={calculated_digest}"
return hmac.compare_digest(received_signature, expected_signature)

418
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@ -0,0 +1,418 @@
# social/services/meta.py - FIXED VERSION
import requests
import time
import hmac
import hashlib
import logging
import datetime
from urllib.parse import urlencode
from django.conf import settings
from django.utils import timezone
from apps.social.utils.meta import BASE_GRAPH_URL, META_SCOPES, BASE_AUTH_URL
logger = logging.getLogger(__name__)
class MetaAPIError(Exception):
pass
class MetaService:
# --- AUTHENTICATION ---
@staticmethod
def get_auth_url():
params = {
"client_id": settings.META_APP_ID,
"redirect_uri": settings.META_REDIRECT_URI,
"scope": ",".join(META_SCOPES),
"response_type": "code",
}
return f"{BASE_AUTH_URL}/dialog/oauth?{urlencode(params)}"
@staticmethod
def exchange_code_for_tokens(code):
"""Exchanges code for a long-lived User Access Token"""
# Step 1: Get short-lived token
res = requests.post(f"{BASE_GRAPH_URL}/oauth/access_token", data={
"client_id": settings.META_APP_ID,
"client_secret": settings.META_APP_SECRET,
"code": code,
"redirect_uri": settings.META_REDIRECT_URI,
})
data = MetaService._handle_api_response(res)
# Step 2: Exchange for long-lived token
long_res = requests.post(f"{BASE_GRAPH_URL}/oauth/access_token", data={
"grant_type": "fb_exchange_token",
"client_id": settings.META_APP_ID,
"client_secret": settings.META_APP_SECRET,
"fb_exchange_token": data['access_token']
})
long_data = MetaService._handle_api_response(long_res)
expires_in = long_data.get('expires_in', 5184000)
return {
"access_token": long_data['access_token'],
"expires_at": timezone.now() + datetime.timedelta(seconds=expires_in)
}
# --- API HELPER ---
@staticmethod
def _handle_api_response(response):
"""Handle API response with proper error checking and rate limit handling"""
try:
data = response.json()
except:
raise MetaAPIError(f"Invalid JSON response: {response.text}")
if 'error' in data:
error_code = data['error'].get('code')
error_msg = data['error'].get('message', 'Unknown error')
# Handle rate limits
if error_code in [4, 17, 32]:
logger.warning(f"Rate limit hit (code {error_code}). Waiting 60 seconds...")
time.sleep(60)
raise MetaAPIError(f"Rate limited: {error_msg}")
# Handle permission errors
if error_code in [200, 190, 102]:
raise MetaAPIError(f"Permission error: {error_msg}")
raise MetaAPIError(f"API Error (code {error_code}): {error_msg}")
return data
# --- DISCOVERY ---
@staticmethod
def discover_pages_and_ig(user_access_token):
"""
Returns a list of manageable entities (FB Pages & IG Business Accounts).
Each dict contains: platform ('FB'|'IG'), native_id, name, access_token
"""
entities = []
next_page = None
while True:
params = {
"access_token": user_access_token,
"fields": "id,name,access_token,instagram_business_account{id,username}",
"limit": 100
}
if next_page:
params['after'] = next_page
try:
res = requests.get(f"{BASE_GRAPH_URL}/me/accounts", params=params)
data = MetaService._handle_api_response(res)
for page in data.get('data', []):
# 1. Add Facebook Page
entities.append({
'platform': 'FB',
'native_id': page['id'],
'name': page['name'],
'access_token': page['access_token'],
'is_permanent': True # Page tokens don't expire if app is active
})
# 2. Add Linked Instagram Business Account (if exists)
ig_data = page.get('instagram_business_account')
if ig_data:
entities.append({
'platform': 'IG',
'native_id': ig_data['id'],
'name': f"IG: {ig_data.get('username', page['name'])}",
'access_token': page['access_token'],
'is_permanent': True,
'parent_page_id': page['id']
})
next_page = data.get('paging', {}).get('cursors', {}).get('after')
if not next_page:
break
except MetaAPIError as e:
logger.error(f"Discovery Error: {e}")
break
except Exception as e:
logger.error(f"Discovery Exception: {e}")
break
return entities
# --- DATA FETCHING ---
@staticmethod
def fetch_posts(entity_id, access_token, platform_type):
"""
Fetches posts from a specific FB Page or IG Account.
"""
posts = []
next_page = None
# Determine endpoint and fields based on platform
if platform_type == "FB":
if entity_id == 'me':
endpoint = f"{entity_id}/feed"
else:
endpoint = f"{entity_id}/feed"
fields = "id,message,created_time,permalink_url"
else: # Instagram
endpoint = f"{entity_id}/media"
fields = "id,caption,timestamp,permalink,media_type,media_url,thumbnail_url"
while True:
params = {
"access_token": access_token,
"limit": 25,
"fields": fields
}
if next_page:
params['after'] = next_page
try:
res = requests.get(f"{BASE_GRAPH_URL}/{endpoint}", params=params)
res_json = res.json()
if 'error' in res_json:
error_msg = res_json.get('error', {}).get('message', 'Unknown error')
logger.warning(f"API Error fetching posts for {entity_id}: {error_msg}")
break
posts_data = res_json.get('data', [])
posts.extend(posts_data)
paging = res_json.get('paging', {})
next_page = paging.get('cursors', {}).get('after')
if not next_page:
break
time.sleep(0.5) # Rate limiting
except requests.exceptions.RequestException as e:
logger.error(f"Network error fetching posts for {entity_id}: {e}")
break
except Exception as e:
logger.error(f"Exception fetching posts for {entity_id}: {e}", exc_info=True)
break
logger.info(f"Fetched total of {len(posts)} posts for {entity_id}")
return posts
@staticmethod
def fetch_comments_for_post(post_id, access_token, since_timestamp=None):
"""
Fetches comments for a specific post (works for both FB and IG).
FIXED: Dynamically selects fields based on platform detection to avoid
Error #100 (nonexisting field 'name' on IGCommentFromUser).
"""
url = f"{BASE_GRAPH_URL}/{post_id}/comments"
comments = []
next_page = None
# --- Platform Detection ---
# Instagram IDs typically start with 17 or 18.
str_post_id = str(post_id)
is_instagram = str_post_id.startswith('17') or str_post_id.startswith('18')
# --- Field Selection ---
if is_instagram:
# IG: Use 'username' if available, but NEVER 'name' on the user object
# Note: 'username' is usually available on IGCommentFromUser
request_fields = "id,from{id,username},message,text,created_time,post,like_count,comment_count,attachment"
else:
# FB: 'name' is standard
request_fields = "id,from{id,name},message,text,created_time,post,like_count,comment_count,attachment"
while True:
params = {
"access_token": access_token,
"limit": 50,
"fields": request_fields, # Use the selected fields
"order": "reverse_chronological"
}
if since_timestamp:
if isinstance(since_timestamp, datetime.datetime):
since_timestamp = int(since_timestamp.timestamp())
params['since'] = since_timestamp
if next_page:
params['after'] = next_page
try:
res = requests.get(url, params=params)
data = MetaService._handle_api_response(res)
new_comments = data.get('data', [])
if not new_comments:
break
comments.extend(new_comments)
next_page = data.get('paging', {}).get('cursors', {}).get('after')
if not next_page:
break
time.sleep(0.5) # Rate limiting
except MetaAPIError as e:
logger.warning(f"Error fetching comments for {post_id}: {e}")
break
except Exception as e:
logger.error(f"Exception fetching comments for {e}")
break
return comments
@staticmethod
def fetch_single_comment(comment_id, access_token):
"""Fetch a single comment by ID (works for both FB and IG)"""
url = f"{BASE_GRAPH_URL}/{comment_id}"
# Safe fallback fields usually work for both, but IG might reject 'name'
# We'll default to username for safety if it looks like IG
str_id = str(comment_id)
if str_id.startswith('17') or str_id.startswith('18'):
fields = "id,from{id,username},message,text,created_time,post,like_count,attachment"
else:
fields = "id,from{id,name},message,text,created_time,post,like_count,attachment"
params = {
"fields": fields,
"access_token": access_token
}
res = requests.get(url, params=params)
data = MetaService._handle_api_response(res)
return data
# @staticmethod
# def post_reply(comment_id, access_token, text):
# """
# Post a reply to a comment (works for both FB and IG).
# """
# url = f"{BASE_GRAPH_URL}/{comment_id}/comments"
# try:
# res = requests.post(
# url,
# params={"access_token": access_token},
# json={"message": text}
# )
# data = MetaService._handle_api_response(res)
# # Graceful handling for Error 100 (Unsupported operation)
# error = data.get('error', {})
# if error and error.get('code') == 100 and "Unsupported" in error.get('message', ''):
# logger.warning(f"Reply failed for {comment_id}: Comment might be deleted, private, or restricted.")
# return data
# return data
# except MetaAPIError as e:
# raise MetaAPIError(f"Reply failed: {str(e)}")
# except requests.exceptions.RequestException as e:
# raise MetaAPIError(f"Network error posting reply: {str(e)}")
@staticmethod
def post_reply(comment_id, access_token, platform='FB', text=None):
"""
Post a reply to a comment (Handle FB vs IG endpoints).
Args:
platform (str): 'facebook' or 'instagram' (default: 'facebook')
"""
# STEP 1: Choose the correct endpoint
if platform.lower() == 'ig':
# Instagram requires /replies endpoint for comments
url = f"{BASE_GRAPH_URL}/{comment_id}/replies"
else:
# Facebook treats replies as 'comments on a comment'
url = f"{BASE_GRAPH_URL}/{comment_id}/comments"
try:
res = requests.post(
url,
params={"access_token": access_token},
json={"message": text}
)
data = MetaService._handle_api_response(res)
return data
except MetaAPIError as e:
# Check for Error 100 (Unsupported operation)
# This often happens if you try to reply to an IG comment that is ALREADY a reply
# (Instagram only supports 1 level of nesting)
error_code = e.args[0] if isinstance(e, tuple) and len(e.args) > 0 else None
if error_code == 100 or "Unsupported" in str(e):
logger.warning(f"Reply failed for {comment_id} ({platform}): Object might be deleted, restricted, or you are trying to reply to a reply (nested) which IG blocks.")
raise e
# Re-raise other errors
raise e
except requests.exceptions.RequestException as e:
raise MetaAPIError(f"Network error posting reply: {str(e)}")
@staticmethod
def subscribe_webhook(page_id, access_token):
"""
Subscribes a specific page to the app's webhook.
"""
url = f"{BASE_GRAPH_URL}/{page_id}/subscribed_apps"
res = requests.post(
url,
json={
"access_token": access_token,
"subscribed_fields": ["comments", "feed"]
}
)
data = MetaService._handle_api_response(res)
return True
# --- WEBHOOK UTILS ---
@staticmethod
def verify_webhook_signature(received_signature, body_raw, client_secret):
"""Verify webhook signature from Meta"""
if not received_signature or not body_raw:
return False
calculated_digest = hmac.new(
client_secret.encode('utf-8'),
body_raw,
hashlib.sha256
).hexdigest()
expected_signature = f"sha256={calculated_digest}"
return hmac.compare_digest(received_signature, expected_signature)
# --- HELPER METHODS ---
@staticmethod
def detect_source_platform(comment_id, post_id=None):
"""
Reliably detect if comment is from FB or IG based on ID format.
"""
if comment_id and comment_id.startswith('17') and comment_id.isdigit():
return 'IG'
elif comment_id and '_' in comment_id:
return 'FB'
elif post_id:
# Fallback: Check post ID format
if str(post_id).startswith('17') and str(post_id).isdigit():
return 'IG'
return 'FB' # Default to Facebook

430
apps/social/services/openrouter_service.py
View File

@ -1,430 +0,0 @@
"""
OpenRouter API service for AI-powered comment analysis.
Handles authentication, requests, and response parsing for sentiment analysis,
keyword extraction, topic identification, and entity recognition.
"""
import logging
import json
from typing import Dict, List, Any, Optional
from decimal import Decimal
import httpx
from django.conf import settings
from django.utils import timezone
logger = logging.getLogger(__name__)
class OpenRouterService:
"""
Service for interacting with OpenRouter API to analyze comments.
Provides sentiment analysis, keyword extraction, topic identification, and entity recognition.
"""
DEFAULT_MODEL = "anthropic/claude-3-haiku"
DEFAULT_MAX_TOKENS = 1024
DEFAULT_TEMPERATURE = 0.1
def __init__(
self,
api_key: Optional[str] = None,
model: Optional[str] = None,
timeout: int = 30
):
"""
Initialize OpenRouter service.
Args:
api_key: OpenRouter API key (defaults to settings.OPENROUTER_API_KEY)
model: Model to use (defaults to settings.OPENROUTER_MODEL or DEFAULT_MODEL)
timeout: Request timeout in seconds
"""
self.api_key = api_key or getattr(settings, 'OPENROUTER_API_KEY', None)
self.model = model or getattr(settings, 'OPENROUTER_MODEL', self.DEFAULT_MODEL)
self.timeout = timeout
self.api_url = "https://openrouter.ai/api/v1/chat/completions"
if not self.api_key:
logger.warning(
"OpenRouter API key not configured. "
"Set OPENROUTER_API_KEY in your .env file."
)
logger.info(f"OpenRouter service initialized with model: {self.model}")
def _build_analysis_prompt(self, comments: List[Dict[str, Any]]) -> str:
"""
Build prompt for batch comment analysis with bilingual output.
Args:
comments: List of comment dictionaries with 'id' and 'text' keys
Returns:
Formatted prompt string
"""
comments_text = "\n".join([
f"Comment {i+1}: {c['text']}"
for i, c in enumerate(comments)
])
# Using regular string instead of f-string to avoid JSON brace escaping issues
prompt = """You are a bilingual AI analyst specializing in social media sentiment analysis. Analyze the following comments and provide a COMPLETE bilingual analysis in BOTH English and Arabic.
Comments to analyze:
""" + comments_text + """
IMPORTANT REQUIREMENTS:
1. ALL analysis MUST be provided in BOTH English and Arabic
2. Use clear, modern Arabic that all Arabic speakers can understand
3. Detect comment's language and provide appropriate translations
4. Maintain accuracy and cultural appropriateness in both languages
For each comment, provide:
A. Sentiment Analysis (Bilingual)
- classification: {"en": "positive|neutral|negative", "ar": "إيجابي|محايد|سلبي"}
- score: number from -1.0 to 1.0
- confidence: number from 0.0 to 1.0
B. Summaries (Bilingual)
- en: 2-3 sentence English summary of comment's main points and sentiment
- ar: 2-3 sentence Arabic summary (ملخص بالعربية) with the same depth
C. Keywords (Bilingual - 5-7 each)
- en: list of English keywords
- ar: list of Arabic keywords
D. Topics (Bilingual - 3-5 each)
- en: list of English topics
- ar: list of Arabic topics
E. Entities (Bilingual)
- For each entity: {"text": {"en": "...", "ar": "..."}, "type": {"en": "PERSON|ORGANIZATION|LOCATION|BRAND|OTHER", "ar": "شخص|منظمة|موقع|علامة تجارية|أخرى"}}
F. Emotions
- Provide scores for: joy, anger, sadness, fear, surprise, disgust
- Each emotion: 0.0 to 1.0
- labels: {"emotion_name": {"en": "English label", "ar": "Arabic label"}}
Return ONLY valid JSON in this exact format:
{
"analyses": [
{
"comment_index": 0,
"sentiment": {
"classification": {"en": "positive", "ar": "إيجابي"},
"score": 0.85,
"confidence": 0.92
},
"summaries": {
"en": "The customer is very satisfied with the excellent service and fast delivery. They praised the staff's professionalism and product quality.",
"ar": "العميل راضٍ جداً عن الخدمة الممتازة والتسليم السريع. أشاد باحترافية الموظفين وجودة المنتج."
},
"keywords": {
"en": ["excellent service", "fast delivery", "professional", "quality"],
"ar": ["خدمة ممتازة", "تسليم سريع", "احترافي", "جودة"]
},
"topics": {
"en": ["customer service", "delivery speed", "staff professionalism"],
"ar": ["خدمة العملاء", "سرعة التسليم", "احترافية الموظفين"]
},
"entities": [
{
"text": {"en": "Amazon", "ar": "أمازون"},
"type": {"en": "ORGANIZATION", "ar": "منظمة"}
}
],
"emotions": {
"joy": 0.9,
"anger": 0.05,
"sadness": 0.0,
"fear": 0.0,
"surprise": 0.15,
"disgust": 0.0,
"labels": {
"joy": {"en": "Joy/Happiness", "ar": "فرح/سعادة"},
"anger": {"en": "Anger", "ar": "غضب"},
"sadness": {"en": "Sadness", "ar": "حزن"},
"fear": {"en": "Fear", "ar": "خوف"},
"surprise": {"en": "Surprise", "ar": "مفاجأة"},
"disgust": {"en": "Disgust", "ar": "اشمئزاز"}
}
}
}
]
}
"""
return prompt
async def analyze_comments_async(self, comments: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Analyze a batch of comments using OpenRouter API (async).
Args:
comments: List of comment dictionaries with 'id' and 'text' keys
Returns:
Dictionary with success status and analysis results
"""
logger.info("=" * 80)
logger.info("STARTING OPENROUTER API ANALYSIS")
logger.info("=" * 80)
if not self.api_key:
logger.error("API KEY NOT CONFIGURED")
return {
'success': False,
'error': 'OpenRouter API key not configured'
}
logger.info(f"API Key: {self.api_key[:20]}...{self.api_key[-4:]}")
if not comments:
logger.warning("No comments to analyze")
return {
'success': True,
'analyses': []
}
try:
logger.info(f"Building prompt for {len(comments)} comments...")
prompt = self._build_analysis_prompt(comments)
logger.info(f"Prompt length: {len(prompt)} characters")
headers = {
'Authorization': f'Bearer {self.api_key}',
'Content-Type': 'application/json',
'HTTP-Referer': getattr(settings, 'SITE_URL', 'http://localhost'),
'X-Title': 'Social Media Comment Analyzer'
}
logger.info(f"Request headers prepared: {list(headers.keys())}")
payload = {
'model': self.model,
'messages': [
{
'role': 'system',
'content': 'You are an expert social media sentiment analyzer. Always respond with valid JSON only.'
},
{
'role': 'user',
'content': prompt
}
],
'max_tokens': self.DEFAULT_MAX_TOKENS,
'temperature': self.DEFAULT_TEMPERATURE
}
logger.info(f"Request payload prepared:")
logger.info(f" - Model: {payload['model']}")
logger.info(f" - Max tokens: {payload['max_tokens']}")
logger.info(f" - Temperature: {payload['temperature']}")
logger.info(f" - Messages: {len(payload['messages'])}")
logger.info(f" - Payload size: {len(json.dumps(payload))} bytes")
logger.info("-" * 80)
logger.info("SENDING HTTP REQUEST TO OPENROUTER API")
logger.info("-" * 80)
logger.info(f"URL: {self.api_url}")
logger.info(f"Timeout: {self.timeout}s")
async with httpx.AsyncClient(timeout=self.timeout) as client:
response = await client.post(
self.api_url,
headers=headers,
json=payload
)
logger.info("-" * 80)
logger.info("RESPONSE RECEIVED")
logger.info("-" * 80)
logger.info(f"Status Code: {response.status_code}")
logger.info(f"Status Reason: {response.reason_phrase}")
logger.info(f"HTTP Version: {response.http_version}")
logger.info(f"Headers: {dict(response.headers)}")
# Get raw response text BEFORE any parsing
raw_content = response.text
logger.info(f"Raw response length: {len(raw_content)} characters")
# Log first and last parts of response for debugging
logger.debug("-" * 80)
logger.debug("RAW RESPONSE CONTENT (First 500 chars):")
logger.debug(raw_content[:500])
logger.debug("-" * 80)
logger.debug("RAW RESPONSE CONTENT (Last 500 chars):")
logger.debug(raw_content[-500:] if len(raw_content) > 500 else raw_content)
logger.debug("-" * 80)
response.raise_for_status()
logger.info("Response status OK (200), attempting to parse JSON...")
data = response.json()
logger.info(f"Successfully parsed JSON response")
logger.info(f"Response structure: {list(data.keys()) if isinstance(data, dict) else type(data)}")
# Extract analysis from response
if 'choices' in data and len(data['choices']) > 0:
logger.info(f"Found {len(data['choices'])} choices in response")
content = data['choices'][0]['message']['content']
logger.info(f"Content message length: {len(content)} characters")
# Parse JSON response
try:
# Clean up response in case there's any extra text
logger.info("Cleaning response content...")
content = content.strip()
logger.info(f"After strip: {len(content)} chars")
# Remove markdown code blocks if present
if content.startswith('```json'):
logger.info("Detected ```json prefix, removing...")
content = content[7:]
elif content.startswith('```'):
logger.info("Detected ``` prefix, removing...")
content = content[3:]
if content.endswith('```'):
logger.info("Detected ``` suffix, removing...")
content = content[:-3]
content = content.strip()
logger.info(f"After cleaning: {len(content)} chars")
logger.debug("-" * 80)
logger.debug("CLEANED CONTENT (First 300 chars):")
logger.debug(content[:300])
logger.debug("-" * 80)
logger.info("Attempting to parse JSON...")
analysis_data = json.loads(content)
logger.info("JSON parsed successfully!")
logger.info(f"Analysis data keys: {list(analysis_data.keys()) if isinstance(analysis_data, dict) else type(analysis_data)}")
if 'analyses' in analysis_data:
logger.info(f"Found {len(analysis_data['analyses'])} analyses")
# Map comment indices back to IDs
analyses = []
for idx, analysis in enumerate(analysis_data.get('analyses', [])):
comment_idx = analysis.get('comment_index', 0)
if comment_idx < len(comments):
comment_id = comments[comment_idx]['id']
logger.debug(f" Analysis {idx+1}: comment_index={comment_idx}, comment_id={comment_id}")
analyses.append({
'comment_id': comment_id,
**analysis
})
# Extract metadata
metadata = {
'model': self.model,
'prompt_tokens': data.get('usage', {}).get('prompt_tokens', 0),
'completion_tokens': data.get('usage', {}).get('completion_tokens', 0),
'total_tokens': data.get('usage', {}).get('total_tokens', 0),
'analyzed_at': timezone.now().isoformat()
}
logger.info(f"Metadata: {metadata}")
logger.info("=" * 80)
logger.info("ANALYSIS COMPLETED SUCCESSFULLY")
logger.info("=" * 80)
return {
'success': True,
'analyses': analyses,
'metadata': metadata
}
except json.JSONDecodeError as e:
logger.error("=" * 80)
logger.error("JSON PARSE ERROR")
logger.error("=" * 80)
logger.error(f"Error: {e}")
logger.error(f"Error position: Line {e.lineno}, Column {e.colno}")
logger.error(f"Error message: {e.msg}")
logger.error("-" * 80)
logger.error("FULL CONTENT THAT FAILED TO PARSE:")
logger.error("-" * 80)
logger.error(content)
logger.error("-" * 80)
logger.error("CHARACTER AT ERROR POSITION:")
logger.error("-" * 80)
if hasattr(e, 'pos') and e.pos:
start = max(0, e.pos - 100)
end = min(len(content), e.pos + 100)
logger.error(content[start:end])
logger.error(f"^ (error at position {e.pos})")
return {
'success': False,
'error': f'Invalid JSON response from API: {str(e)}'
}
else:
logger.error(f"No choices found in response. Response keys: {list(data.keys()) if isinstance(data, dict) else type(data)}")
return {
'success': False,
'error': 'No analysis returned from API'
}
except httpx.HTTPStatusError as e:
logger.error("=" * 80)
logger.error("HTTP STATUS ERROR")
logger.error("=" * 80)
logger.error(f"Status Code: {e.response.status_code}")
logger.error(f"Response Text: {e.response.text}")
return {
'success': False,
'error': f'API error: {e.response.status_code} - {str(e)}'
}
except httpx.RequestError as e:
logger.error("=" * 80)
logger.error("HTTP REQUEST ERROR")
logger.error("=" * 80)
logger.error(f"Error: {str(e)}")
return {
'success': False,
'error': f'Request failed: {str(e)}'
}
except Exception as e:
logger.error("=" * 80)
logger.error("UNEXPECTED ERROR")
logger.error("=" * 80)
logger.error(f"Error Type: {type(e).__name__}")
logger.error(f"Error Message: {str(e)}")
logger.error("=" * 80)
logger.error("FULL TRACEBACK:", exc_info=True)
logger.error("=" * 80)
return {
'success': False,
'error': f'Unexpected error: {str(e)}'
}
def analyze_comments(self, comments: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Analyze a batch of comments using OpenRouter API (synchronous wrapper).
Args:
comments: List of comment dictionaries with 'id' and 'text' keys
Returns:
Dictionary with success status and analysis results
"""
import asyncio
try:
# Run async function in event loop
loop = asyncio.get_event_loop()
except RuntimeError:
# No event loop exists, create new one
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
return loop.run_until_complete(self.analyze_comments_async(comments))
def is_configured(self) -> bool:
"""Check if service is properly configured."""
return bool(self.api_key)

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# social/services/tiktok.py
import requests
import datetime
import time
import logging
from django.conf import settings
from django.utils import timezone
from apps.social.utils.tiktok import TikTokConstants
from apps.social.models import SocialAccount
logger = logging.getLogger(__name__)
class TikTokAPIError(Exception):
pass
class TikTokService:
@staticmethod
def get_auth_url():
state = "random_secure_string" # Ideally generate dynamically
return (
f"{TikTokConstants.ENDPOINTS['AUTH']}"
f"?app_id={settings.TIKTOK_CLIENT_KEY}"
f"&state={state}"
f"&scope={TikTokConstants.SCOPES}"
f"&redirect_uri={settings.TIKTOK_REDIRECT_URI}"
)
@staticmethod
def exchange_code_for_token(code):
url = f"{TikTokConstants.BASE_URL}{TikTokConstants.ENDPOINTS['TOKEN']}"
payload = {
"app_id": settings.TIKTOK_CLIENT_KEY,
"secret": settings.TIKTOK_CLIENT_SECRET,
"auth_code": code,
}
resp = requests.post(url, json=payload)
data = resp.json()
if data.get('code') != 0:
logger.error(f"TikTok Token Error: {data}")
raise TikTokAPIError(data.get('message', 'Token exchange failed'))
return data.get('data', {})
@staticmethod
def refresh_tokens(account):
url = f"{TikTokConstants.BASE_URL}{TikTokConstants.ENDPOINTS['TOKEN']}"
payload = {
"app_id": settings.TIKTOK_CLIENT_KEY,
"secret": settings.TIKTOK_CLIENT_SECRET,
"refresh_token": account.refresh_token,
}
resp = requests.post(url, json=payload)
data = resp.json()
if data.get('code') != 0:
account.is_active = False
account.save()
raise TikTokAPIError("Refresh Failed")
token_data = data.get('data', {})
account.access_token = token_data['access_token']
account.refresh_token = token_data['refresh_token']
account.expires_at = timezone.now() + datetime.timedelta(seconds=token_data.get('expires_in', 86400))
account.save()
return account
@staticmethod
def get_valid_token(account):
if account.expires_at <= timezone.now() + datetime.timedelta(minutes=5):
account = TikTokService.refresh_tokens(account)
return account.access_token
@staticmethod
def make_api_request(endpoint, account, payload=None, method="POST"):
token = TikTokService.get_valid_token(account)
headers = {
"Access-Token": token, # Business API specific header
"Content-Type": "application/json"
}
url = f"{TikTokConstants.BASE_URL}{endpoint}"
if payload is None:
payload = {}
# CRITICAL: Business API requires advertiser_id in the body
if not account.platform_id:
raise TikTokAPIError("Advertiser ID is missing from account configuration.")
payload['advertiser_id'] = account.platform_id
try:
# Business API primarily uses POST for data retrieval
if method == "POST":
response = requests.post(url, headers=headers, json=payload)
else:
response = requests.get(url, headers=headers, params=payload)
data = response.json()
if data.get('code') != 0:
raise TikTokAPIError(f"API Error {data.get('code')}: {data.get('message')}")
return data.get('data', {})
except Exception as e:
raise TikTokAPIError(str(e))
@staticmethod
def fetch_ads(account, page=1, page_size=20):
"""Fetch Ads to act as 'Content' containers for comments."""
payload = {
"page": page,
"page_size": page_size,
# Filtering could be added here, e.g., filtering by active status
}
return TikTokService.make_api_request(TikTokConstants.ENDPOINTS['AD_LIST'], account, payload)
@staticmethod
def fetch_comments_for_ad(account, ad_id, page=1, page_size=20):
"""Fetch comments for a specific Advertisement."""
payload = {
# 'item_id' in Business API context is the Ad ID
"item_id": ad_id,
"page": page,
"page_size": page_size,
}
return TikTokService.make_api_request(TikTokConstants.ENDPOINTS['COMMENT_LIST'], account, payload)
@staticmethod
def reply_to_comment(account, ad_id, comment_id, text):
"""Reply to a comment on an Advertisement."""
payload = {
"item_id": ad_id, # Ad ID required
"comment_id": comment_id,
"text": text
}
return TikTokService.make_api_request(TikTokConstants.ENDPOINTS['COMMENT_REPLY'], account, payload)

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import requests
import time
import base64
import hashlib
import secrets
import datetime
from urllib.parse import urlencode
from django.conf import settings
from django.utils import timezone
from apps.social.utils.x import XConfig
import logging
logger = logging.getLogger(__name__)
class XAPIError(Exception):
pass
class XRateLimitError(Exception):
"""Custom exception to signal Celery to retry with a countdown."""
def __init__(self, reset_at_timestamp):
self.reset_at = reset_at_timestamp
super().__init__(f"Rate limit hit. Retry after {reset_at_timestamp}")
class XService:
TWITTER_EPOCH = 1288834974657
# --- AUTHENTICATION ---
@staticmethod
def _get_auth_header():
"""
Creates the Authorization header using Basic Auth.
Required for Confidential Clients (Web Apps).
"""
auth_str = f"{settings.X_CLIENT_ID}:{settings.X_CLIENT_SECRET}"
encoded_auth = base64.b64encode(auth_str.encode()).decode()
return {"Authorization": f"Basic {encoded_auth}", "Content-Type": "application/x-www-form-urlencoded"}
@staticmethod
def generate_pkce_pair():
code_verifier = base64.urlsafe_b64encode(secrets.token_bytes(32)).decode('utf-8').replace('=', '')
code_challenge = base64.urlsafe_b64encode(hashlib.sha256(code_verifier.encode('utf-8')).digest()).decode('utf-8').replace('=', '')
return code_verifier, code_challenge
@staticmethod
def generate_auth_params():
code_verifier, code_challenge = XService.generate_pkce_pair()
state = secrets.token_urlsafe(32)
return code_verifier, code_challenge, state
@staticmethod
def get_auth_url(code_challenge, state):
# Note: PKCE uses client_id in URL, but token swap uses Basic Auth
params = {
"response_type": "code",
"client_id": settings.X_CLIENT_ID,
"redirect_uri": settings.X_REDIRECT_URI,
"scope": " ".join(XConfig.SCOPES),
"state": state,
"code_challenge": code_challenge,
"code_challenge_method": "S256"
}
return f"{XConfig.AUTH_URL}?{urlencode(params)}"
@staticmethod
def exchange_code_for_token(code, code_verifier):
# FIX: Use Basic Auth Header, not body client_id
headers = XService._get_auth_header()
payload = {
"code": code,
"grant_type": "authorization_code",
"redirect_uri": settings.X_REDIRECT_URI,
"code_verifier": code_verifier,
}
res = requests.post(XConfig.TOKEN_URL, headers=headers, data=payload)
data = res.json()
if 'error' in data:
raise XAPIError(data.get('error_description'))
expires_in = data.get('expires_in', 7200)
return {
"access_token": data['access_token'],
"refresh_token": data['refresh_token'],
"expires_at": timezone.now() + datetime.timedelta(seconds=expires_in)
}
@staticmethod
def refresh_tokens(account):
# FIX: Use Basic Auth Header
headers = XService._get_auth_header()
payload = {
"grant_type": "refresh_token",
"refresh_token": account.refresh_token,
}
res = requests.post(XConfig.TOKEN_URL, headers=headers, data=payload)
data = res.json()
if 'error' in data:
account.is_active = False
account.save()
raise XAPIError(data.get('error_description'))
account.access_token = data['access_token']
if 'refresh_token' in data:
account.refresh_token = data['refresh_token']
account.expires_at = timezone.now() + datetime.timedelta(seconds=data.get('expires_in', 7200))
account.save()
@staticmethod
def get_valid_token(account):
if account.expires_at <= timezone.now() + datetime.timedelta(minutes=5):
XService.refresh_tokens(account)
return account.access_token
@staticmethod
def _make_request(endpoint, account, method="GET", payload=None):
token = XService.get_valid_token(account)
headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
url = f"{XConfig.BASE_URL}/{endpoint}"
try:
if method == "GET":
response = requests.get(url, headers=headers, params=payload)
else:
response = requests.post(url, headers=headers, json=payload)
if response.status_code == 429:
# FIX: Raise specific exception for Celery to handle, don't block worker
reset_header = response.headers.get('x-rate-limit-reset')
if reset_header:
reset_time = int(reset_header)
raise XRateLimitError(reset_time)
else:
# Fallback if header missing
raise XRateLimitError(int(time.time()) + 60)
if response.status_code >= 400:
raise XAPIError(f"API Error {response.status_code}: {response.text}")
return response.json()
except requests.exceptions.RequestException as e:
raise XAPIError(f"Network Error: {str(e)}")
# --- DATA FETCHING HELPERS ---
@staticmethod
def _datetime_to_snowflake_id(dt):
if not dt: return None
timestamp_ms = int(dt.timestamp() * 1000)
snowflake = (timestamp_ms - XService.TWITTER_EPOCH) << 22
return str(snowflake)
@staticmethod
def _datetime_to_iso_string(dt):
if not dt: return None
if dt.tzinfo: dt = dt.astimezone(datetime.timezone.utc)
return dt.strftime("%Y-%m-%dT%H:%M:%SZ")
@staticmethod
def _attach_expansions(data, response):
"""
Maps users from 'includes' to their respective tweets in 'data'.
This allows the tasks to simply access r_data['author'].
"""
users = {u['id']: u for u in response.get('includes', {}).get('users', [])}
media = {m['media_key']: m for m in response.get('includes', {}).get('media', [])}
for item in data:
item['author'] = users.get(item.get('author_id'))
item['media_objects'] = [media[k] for k in item.get('attachments', {}).get('media_keys', []) if k in media]
return data
@staticmethod
def get_user_tweets(account):
tweets = []
next_token = None
while True:
# FIX: Added expansions
params = {
"tweet.fields": "created_at,conversation_id,author_id,attachments,public_metrics",
"expansions": "author_id,attachments.media_keys",
"user.fields": "username,name",
"media.fields": "type,url,preview_image_url,alt_text",
"max_results": 100,
"exclude": "retweets"
}
if next_token: params['pagination_token'] = next_token
data = XService._make_request(f"users/{account.platform_id}/tweets", account, "GET", params=params)
raw_tweets = data.get('data', [])
# Attach author/media data
enriched_tweets = XService._attach_expansions(raw_tweets, data)
tweets.extend(enriched_tweets)
next_token = data.get('meta', {}).get('next_token')
if not next_token: break
time.sleep(0.5) # Small politeness delay between pages
return tweets
@staticmethod
def fetch_tweet_replies(account, conversation_id, since_datetime=None, owner_id=None):
use_enterprise = getattr(settings, 'X_USE_ENTERPRISE', False)
endpoint = XConfig.SEARCH_RECENT_URL if not use_enterprise else XConfig.SEARCH_ALL_URL
# Note: Free Tier (Basic) does not support Search.
# If this returns 403 on Basic Tier, you are on Free Tier which cannot search replies.
next_token = None
replies = []
while True:
query = f"conversation_id:{conversation_id}"
if owner_id: query += f" to:{owner_id}"
query += " -is:retweet"
# FIX: Added expansions
params = {
"query": query,
"tweet.fields": "created_at,author_id,text,referenced_tweets,in_reply_to_user_id",
"expansions": "author_id",
"user.fields": "username,name",
"max_results": 100
}
if since_datetime:
if use_enterprise:
params['start_time'] = XService._datetime_to_iso_string(since_datetime)
else:
params['since_id'] = XService._datetime_to_snowflake_id(since_datetime)
if next_token: params['pagination_token'] = next_token
try:
data = XService._make_request(endpoint, account, "GET", params=params)
raw_replies = data.get('data', [])
if not raw_replies: break
# Attach authors so tasks can read .get('author')
enriched_replies = XService._attach_expansions(raw_replies, data)
replies.extend(enriched_replies)
next_token = data.get('meta', {}).get('next_token')
if not next_token: break
time.sleep(0.5) # Politeness delay
except XRateLimitError as e:
# Re-raise this specific exception for the task to handle
raise e
except XAPIError as e:
if "403" in str(e):
# Free tier limitation: Cannot use search endpoint
logger.warning(f"Search API Forbidden. Account might be on Free Tier.")
break # Stop task on other errors to prevent loops
return replies
@staticmethod
def post_reply(account, tweet_id, text):
payload = {"text": text, "reply": {"in_reply_to_tweet_id": tweet_id}}
return XService._make_request("tweets", account, "POST", payload=payload)

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# youtube/services.py
# ... imports ...
import json
import logging
import datetime
import time
from google.oauth2.credentials import Credentials
from google_auth_oauthlib.flow import Flow
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
from google.auth.transport.requests import Request
# CHANGE: Import install/uninstall if we want to suppress warnings, but better to fix via pip
import warnings
from django.conf import settings
from django.utils import timezone
from apps.social.utils.youtube import YOUTUBE_SCOPES, YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION
logger = logging.getLogger(__name__)
class YouTubeAPIError(Exception): pass
class RateLimitError(Exception): pass
class YouTubeService:
# --- AUTH ---
@staticmethod
def _get_flow():
return Flow.from_client_secrets_file(
settings.YOUTUBE_CLIENT_SECRETS_FILE,
scopes=YOUTUBE_SCOPES,
redirect_uri=settings.YOUTUBE_REDIRECT_URI
)
@staticmethod
def get_auth_url(state=None):
flow = YouTubeService._get_flow()
auth_url, generated_state = flow.authorization_url(
access_type='offline', prompt='consent', state=state
)
return auth_url, generated_state
@staticmethod
def exchange_code_for_token(code):
flow = YouTubeService._get_flow()
flow.fetch_token(code=code)
creds = flow.credentials
return json.loads(creds.to_json())
@staticmethod
def _get_credentials(account):
if not account.credentials_json:
raise YouTubeAPIError("No token found.")
# FIX 1: Suppress noisy library warnings if necessary
with warnings.catch_warnings():
warnings.simplefilter("ignore")
creds = Credentials.from_authorized_user_info(account.credentials_json)
if not creds.valid:
if creds.expired and creds.refresh_token:
try:
logger.info(f"Refreshing token for {account.name}...")
# FIX 2: Ensure Request object is correct
creds.refresh(Request())
# Save refreshed token
account.credentials_json = json.loads(creds.to_json())
# Recalculate expiration
account.expires_at = timezone.now() + datetime.timedelta(hours=1) # Conservative buffer
account.save()
except Exception as e:
logger.error(f"Token refresh failed: {e}")
account.is_active = False
account.save()
raise YouTubeAPIError("Token refresh failed")
elif creds.expired and not creds.refresh_token:
logger.error("Token expired and no refresh token found.")
account.is_active = False
account.save()
raise YouTubeAPIError("No refresh token")
return creds
@staticmethod
def _build_client(account):
try:
return build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION, credentials=YouTubeService._get_credentials(account))
except Exception as e:
logger.error("Build client failed", exc_info=True)
raise YouTubeAPIError(str(e))
# --- FETCHING LOGIC ---
@staticmethod
def fetch_user_videos(account):
"""Fetch all videos for a user"""
youtube = YouTubeService._build_client(account)
videos = []
next_page_token = None
# FIX 3: Check if playlist ID exists
playlist_id = account.credentials_json.get('uploads_playlist_id')
if not playlist_id:
logger.error(f"No 'uploads_playlist_id' found in account credentials for {account.name}. Cannot sync videos.")
return []
while True:
try:
request = youtube.playlistItems().list(
part="snippet,contentDetails",
playlistId=playlist_id,
maxResults=50,
pageToken=next_page_token
)
response = request.execute()
for item in response.get('items', []):
videos.append({
'id': item['contentDetails']['videoId'],
'snippet': {
'title': item['snippet']['title'],
'description': item['snippet'].get('description', ''),
'publishedAt': item['contentDetails']['videoPublishedAt']
}
})
next_page_token = response.get('nextPageToken')
if not next_page_token: break
time.sleep(0.5)
except HttpError as e:
# FIX 4: LOG THE ERROR! Don't just return empty.
logger.error(f"API Error fetching videos for {account.name}: {e.resp.status} {e.content}")
if e.resp.status == 429:
raise RateLimitError("Quota limit")
if e.resp.status in [401, 403]:
raise YouTubeAPIError(f"Authentication Failed: {e}")
break
return videos
@staticmethod
def fetch_activities_incremental(account):
youtube = YouTubeService._build_client(account)
published_after = (timezone.now() - datetime.timedelta(days=2)).isoformat() + 'Z'
comment_ids = []
next_page_token = None
try:
while True:
request = youtube.activities().list(
part='snippet,contentDetails',
mine=True,
publishedAfter=published_after,
maxResults=50,
pageToken=next_page_token
)
response = request.execute()
for item in response.get('items', []):
if item['snippet']['type'] == 'comment':
c_id = item.get('contentDetails', {}).get('comment', {}).get('id')
if c_id: comment_ids.append(c_id)
next_page_token = response.get('nextPageToken')
if not next_page_token: break
time.sleep(0.5)
except HttpError as e:
if e.resp.status == 429: raise RateLimitError("Quota limit")
return comment_ids
@staticmethod
def fetch_video_comments(account, video_id):
"""Fetch comments for a video"""
youtube = YouTubeService._build_client(account)
comments = []
next_page_token = None
while True:
try:
request = youtube.commentThreads().list(
part="snippet",
videoId=video_id,
maxResults=100,
order="time",
pageToken=next_page_token
)
response = request.execute()
comments.extend(response.get('items', []))
next_page_token = response.get('nextPageToken')
if not next_page_token: break
time.sleep(1)
except HttpError as e:
if e.resp.status == 429: raise RateLimitError("Quota limit")
break
return comments
@staticmethod
def post_reply(account, parent_id, text):
youtube = YouTubeService._build_client(account)
request = youtube.comments().insert(
part="snippet",
body={"snippet": {"parentId": parent_id, "textOriginal": text}}
)
return request.execute()

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# Google Business Profile API Setup Guide
This guide provides step-by-step instructions for setting up Google Business Profile (formerly Google My Business) API integration for managing reviews.
---
## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [Google Cloud Console Setup](#google-cloud-console-setup)
4. [Environment Configuration](#environment-configuration)
5. [OAuth Redirect URI Configuration](#oauth-redirect-uri-configuration)
6. [Permissions & Scopes](#permissions--scopes)
7. [Development vs Production](#development-vs-production)
8. [Troubleshooting](#troubleshooting)
---
## Overview
**API Version:** My Business API v4 / Account Management v1
**Base URL:** Google API Services
**Auth Method:** OAuth 2.0
### Features Supported
- Fetch business locations
- Read Google reviews for locations
- Reply to reviews as the business owner
- Monitor review ratings and feedback
---
## Prerequisites
- A Google account with owner/manager access to a Google Business Profile
- Access to [Google Cloud Console](https://console.cloud.google.com/)
- A verified business location on Google Maps
---
## Google Cloud Console Setup
### Step 1: Create a New Project
1. Navigate to [Google Cloud Console](https://console.cloud.google.com/)
2. Click on the project selector dropdown at the top
3. Click **"New Project"**
4. Enter project details:
- **Project Name:** e.g., "PX360 Social Integration"
- **Organization:** Select your organization (if applicable)
5. Click **"Create"**
6. Select your new project
### Step 2: Enable Required APIs
1. Go to **"APIs & Services"** → **"Library"**
2. Search for and enable the following APIs:
- **Google My Business API** (Note: May require verification)
- **My Business Account Management API**
- **My Business Business Information API**
> ⚠️ **Important:** Google My Business API requires approval from Google. You may need to fill out a form explaining your use case.
### Step 3: Configure OAuth Consent Screen
1. Go to **"APIs & Services"** → **"OAuth consent screen"**
2. Select **"External"** user type (unless you have a Google Workspace account)
3. Click **"Create"**
4. Fill in the required fields:
- **App Name:** Your application name
- **User Support Email:** Your support email
- **App Logo:** Upload your logo
- **Application Home Page:** Your website URL
- **Authorized Domains:** Your domain(s)
- **Developer Contact Email:** Your email
5. Click **"Save and Continue"**
6. Add scopes (click "Add or Remove Scopes"):
- `https://www.googleapis.com/auth/business.manage`
7. Click **"Save and Continue"**
8. Add test users (for development)
9. Click **"Save and Continue"**
### Step 4: Create OAuth 2.0 Credentials
1. Go to **"APIs & Services"** → **"Credentials"**
2. Click **"Create Credentials"** → **"OAuth client ID"**
3. Select **"Web application"**
4. Configure:
- **Name:** e.g., "PX360 Web Client"
- **Authorized JavaScript origins:**
- Development: `http://127.0.0.1:8000`
- Production: `https://yourdomain.com`
- **Authorized redirect URIs:**
- Development: `http://127.0.0.1:8000/social/callback/GO/`
- Production: `https://yourdomain.com/social/callback/GO/`
5. Click **"Create"**
6. **Download the JSON file** - This is your credentials file
### Step 5: Save Credentials File
1. Rename the downloaded JSON file to `gmb_client_secrets.json`
2. Place it in your project's `secrets/` directory:
```
your_project/
├── secrets/
│ └── gmb_client_secrets.json
└── ...
```
The JSON file structure should look like:
```json
{
"web": {
"client_id": "xxxxx.apps.googleusercontent.com",
"project_id": "your-project-id",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_secret": "your-client-secret",
"redirect_uris": ["http://127.0.0.1:8000/social/callback/GO/"]
}
}
```
---
## Environment Configuration
### Django Settings (settings.py)
```python
# Google Business Profile API Configuration
import os
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent.parent.parent
# Google My Business (Reviews) Configuration
GMB_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'gmb_client_secrets.json'
GMB_REDIRECT_URI = 'https://yourdomain.com/social/callback/GO/'
```
### Environment Variables (.env)
While the credentials are in a JSON file, you can set the redirect URI via environment:
```env
GMB_REDIRECT_URI=https://yourdomain.com/social/callback/GO/
```
---
## OAuth Redirect URI Configuration
The redirect URI must match exactly what's configured in Google Cloud Console.
### Development
```
http://127.0.0.1:8000/social/callback/GO/
http://localhost:8000/social/callback/GO/
```
### Production
```
https://yourdomain.com/social/callback/GO/
```
> ⚠️ **Note:** Google accepts both HTTP and HTTPS for `localhost`/`127.0.0.1`, but production must use HTTPS.
---
## Permissions & Scopes
The application requires the following OAuth scope:
| Scope | Description | Required |
|-------|-------------|----------|
| `https://www.googleapis.com/auth/business.manage` | Full access to manage business listings and reviews | ✅ Yes |
### Code Reference
```python
# apps/social/utils/google.py
SCOPES = ['https://www.googleapis.com/auth/business.manage']
API_VERSION_MYBUSINESS = 'v4'
API_VERSION_ACCOUNT_MGMT = 'v1'
```
---
## Development vs Production
### Development Setup
| Setting | Value |
|---------|-------|
| `GMB_REDIRECT_URI` | `http://127.0.0.1:8000/social/callback/GO/` |
| Protocol | HTTP allowed for localhost |
| App Verification | Not required for testing |
| User Access | Only added test users |
### Production Setup
| Setting | Value |
|---------|-------|
| `GMB_REDIRECT_URI` | `https://yourdomain.com/social/callback/GO/` |
| Protocol | **HTTPS required** |
| App Verification | **Required** by Google |
| User Access | Any Google account |
### Google App Verification
For production, if your app requests sensitive scopes, you may need to go through Google's verification process:
1. Submit your app for verification in Google Cloud Console
2. Provide a demo video showing how the integration works
3. Wait for Google's review (can take several days to weeks)
**Alternative:** Use a service account for internal business use (no verification needed if only accessing your own business data).
---
## Service Account Alternative (Recommended for Internal Use)
If you're only managing your own business locations, consider using a Service Account:
### Step 1: Create Service Account
1. Go to **"IAM & Admin"** → **"Service Accounts"**
2. Click **"Create Service Account"**
3. Enter name and description
4. Click **"Create and Continue"**
5. Skip optional steps
6. Click **"Done"**
### Step 2: Create Key
1. Click on the created service account
2. Go to **"Keys"** tab
3. Click **"Add Key"** → **"Create new key"**
4. Select **"JSON"**
5. Click **"Create"**
6. Save the JSON file securely
### Step 3: Grant Business Access
1. Go to [Google Business Profile Manager](https://business.google.com/)
2. Select your business
3. Go to **"Users"** → **"Add users"**
4. Add the service account email (found in the JSON file)
5. Grant appropriate access level (Owner or Manager)
---
## Troubleshooting
### Common Error: "Access Denied - Requested client not authorized"
**Cause:** OAuth consent screen not configured or app not verified.
**Solution:**
1. Ensure OAuth consent screen is properly configured
2. Add user as a test user if app is in testing mode
3. Submit app for verification if needed for production
---
### Common Error: "Invalid Grant"
**Cause:** Authorization code expired or already used.
**Solution:**
- Authorization codes are single-use and expire quickly
- Ensure your code handles the callback immediately
- Check that redirect URI matches exactly
---
### Common Error: "The caller does not have permission"
**Cause:** User doesn't have access to the business location.
**Solution:**
1. Verify user is an Owner or Manager of the Google Business Profile
2. Check business account permissions at business.google.com
3. Ensure the correct account is selected during OAuth
---
### Common Error: "API Not Enabled"
**Cause:** Required APIs not enabled in Google Cloud Console.
**Solution:**
1. Go to APIs & Services → Library
2. Enable: Google My Business API
3. Enable: My Business Account Management API
4. Wait a few minutes for changes to propagate
---
### Common Error: "Token Refresh Failed"
**Cause:** Refresh token expired or revoked.
**Solution:**
- Google OAuth tokens expire after 6 months of inactivity
- User must re-authenticate
- Ensure `offline_access` is requested during initial auth
---
### Common Error: "Quota Exceeded"
**Cause:** API quota limit reached.
**Solution:**
- Default quota: varies by API method
- Request higher quota in Google Cloud Console
- Implement rate limiting in your application
---
## API Quotas & Limits
| Resource | Default Limit |
|----------|---------------|
| Read Requests | 150 per minute |
| Write Requests | 50 per minute |
| Locations per Account | 10,000 |
The application implements rate limiting to stay within these bounds.
---
## Verification
After setup, verify the integration:
1. Ensure `gmb_client_secrets.json` is in place
2. Navigate to `/social/` in your application
3. Click "Connect Google Business"
4. Authorize with your Google account
5. Select your business location
6. Verify reviews are fetched
7. Test replying to a review
---
## Support Resources
- [Google Business Profile API Documentation](https://developers.google.com/my-business)
- [OAuth 2.0 for Web Server Applications](https://developers.google.com/identity/protocols/oauth2/web-server)
- [Google Cloud Console Support](https://support.google.com/cloud/)
---
*Last Updated: February 2026*
*API Version: My Business v4 / Account Management v1*

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# LinkedIn API Setup Guide
This guide provides step-by-step instructions for setting up LinkedIn API integration for the Social Media Management System.
---
## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [LinkedIn Developer Portal Setup](#linkedin-developer-portal-setup)
4. [Environment Configuration](#environment-configuration)
5. [OAuth Redirect URI Configuration](#oauth-redirect-uri-configuration)
6. [Permissions & Scopes](#permissions--scopes)
7. [Webhook Configuration (Optional)](#webhook-configuration-optional)
8. [Development vs Production](#development-vs-production)
9. [Troubleshooting](#troubleshooting)
---
## Overview
**API Version:** RestLi 2.0 (Version 202411)
**Base URL:** `https://api.linkedin.com/rest`
**Auth URL:** `https://www.linkedin.com/oauth/v2/authorization`
**Token URL:** `https://www.linkedin.com/oauth/v2/accessToken`
### Features Supported
- Fetch organization posts
- Read and manage comments on organization posts
- Reply to comments as the organization
- Webhook support for real-time comment notifications
---
## Prerequisites
- A LinkedIn account with admin access to a LinkedIn Company/Organization Page
- Access to [LinkedIn Developer Portal](https://www.linkedin.com/developers/)
- HTTPS-enabled server for production (required for redirect URIs)
---
## LinkedIn Developer Portal Setup
### Step 1: Create a New App
1. Navigate to [LinkedIn Developer Portal](https://www.linkedin.com/developers/)
2. Click **"Create App"**
3. Fill in the required details:
- **App Name:** Your application name (e.g., "PX360 Social Manager")
- **LinkedIn Page:** Select your company/organization page
- **Privacy Policy URL:** Your privacy policy URL
- **App Logo:** Upload your app logo (required for review)
4. Click **"Create App"**
### Step 2: Request API Products
1. In your app dashboard, go to **"Products"** tab
2. Request access to the following products:
- **Marketing API** (for posts and comments management)
- **Share on LinkedIn** (for posting content)
- **Sign In with LinkedIn** (optional, for user authentication)
3. Some products require LinkedIn approval. Submit a detailed use case explaining:
> "We are building a Social Media Management Tool that allows organizations to manage and respond to comments on their LinkedIn posts from a centralized dashboard. This helps community managers respond faster and maintain engagement with their audience."
### Step 3: Get Credentials
1. Go to **"Auth"** tab in your app dashboard
2. Copy the following values:
- **Client ID** → This is your `LINKEDIN_CLIENT_ID`
- **Client Secret** → Click "Show" to reveal → This is your `LINKEDIN_CLIENT_SECRET`
---
## Environment Configuration
Add the following to your `settings.py` or `.env` file:
### Django Settings (settings.py)
```python
# LinkedIn API Configuration
LINKEDIN_CLIENT_ID = 'your_client_id_here'
LINKEDIN_CLIENT_SECRET = 'your_client_secret_here'
LINKEDIN_REDIRECT_URI = 'https://yourdomain.com/social/callback/LI/'
LINKEDIN_WEBHOOK_VERIFY_TOKEN = 'your_random_secret_string_123'
```
### Environment Variables (.env)
```env
LINKEDIN_CLIENT_ID=your_client_id_here
LINKEDIN_CLIENT_SECRET=your_client_secret_here
LINKEDIN_REDIRECT_URI=https://yourdomain.com/social/callback/LI/
LINKEDIN_WEBHOOK_VERIFY_TOKEN=your_random_secret_string_123
```
---
## OAuth Redirect URI Configuration
### Step 1: Add Redirect URI in LinkedIn App
1. Go to **"Auth"** tab → **"OAuth 2.0 settings"**
2. Click **"Add redirect URL"**
3. Add your callback URL:
**Development:**
```
http://127.0.0.1:8000/social/callback/LI/
http://localhost:8000/social/callback/LI/
```
**Production:**
```
https://yourdomain.com/social/callback/LI/
```
> ⚠️ **Important:** LinkedIn only accepts HTTPS URLs in production. For local development, `http://127.0.0.1` or `http://localhost` is allowed.
---
## Permissions & Scopes
The application requests the following OAuth scopes:
| Scope | Description | Required |
|-------|-------------|----------|
| `r_organization_social` | Read organization posts and comments | ✅ Yes |
| `w_organization_social` | Post content and reply to comments as organization | ✅ Yes |
| `rw_organization_admin` | Manage organization account settings | ✅ Yes |
### Code Reference
```python
# apps/social/utils/linkedin.py
SCOPES = [
"r_organization_social",
"w_organization_social",
"rw_organization_admin"
]
```
---
## Webhook Configuration (Optional)
Webhooks allow real-time notifications when new comments are posted.
### Step 1: Create Webhook Endpoint
Your application should have an endpoint to receive LinkedIn webhooks:
```
POST /social/webhooks/linkedin/
```
### Step 2: Register Webhook
1. In LinkedIn Developer Portal, go to **"Products"** → **"Marketing API"**
2. Configure webhook subscriptions for:
- `socialActions` (comments and reactions)
### Step 3: Verify Webhook
LinkedIn sends a verification request with a challenge. Your server must respond with the challenge:
```python
# Webhook verification handler
def verify_webhook(request):
challenge = request.GET.get('challenge')
verify_token = request.GET.get('verifyToken')
if verify_token == settings.LINKEDIN_WEBHOOK_VERIFY_TOKEN:
return HttpResponse(challenge, status=200)
return HttpResponse(status=403)
```
---
## Development vs Production
### Development Setup
| Setting | Value |
|---------|-------|
| `LINKEDIN_REDIRECT_URI` | `http://127.0.0.1:8000/social/callback/LI/` |
| Protocol | HTTP allowed |
| App Review | Not required for testing |
### Production Setup
| Setting | Value |
|---------|-------|
| `LINKEDIN_REDIRECT_URI` | `https://yourdomain.com/social/callback/LI/` |
| Protocol | **HTTPS required** |
| App Review | Required for Marketing API access |
| Rate Limits | Higher limits for approved apps |
### Using ngrok for Local Testing
If you need to test webhooks locally:
```bash
# Install ngrok
npm install -g ngrok
# Create tunnel to local server
ngrok http 8000
# Use the ngrok URL as your redirect URI
# Example: https://abc123.ngrok.io/social/callback/LI/
```
---
## Troubleshooting
### Common Error: "Access Denied"
**Cause:** User doesn't have admin access to the organization page.
**Solution:** Ensure the authenticating user has one of these roles:
- Super Admin
- Content Admin
- Curator
---
### Common Error: "Invalid Redirect URI"
**Cause:** The redirect URI in your request doesn't match what's configured in LinkedIn.
**Solution:**
1. Check exact URL in LinkedIn Developer Portal → Auth → OAuth 2.0 settings
2. Ensure trailing slashes match
3. Verify protocol (http vs https)
---
### Common Error: "Scope Not Authorized"
**Cause:** Your app hasn't been approved for the requested scope.
**Solution:**
1. Check Products tab in LinkedIn Developer Portal
2. Submit use case for Marketing API if not approved
3. Wait for LinkedIn review (can take 1-5 business days)
---
### Common Error: "Token Expired"
**Cause:** Access tokens expire after 60 days.
**Solution:** The application automatically refreshes tokens using refresh tokens. Ensure:
- User reconnects if refresh fails
- `offline_access` scope was granted during initial authorization
---
### Common Error: Rate Limit (429)
**Cause:** Too many API requests in a short period.
**Solution:**
- Application implements automatic retry with exponential backoff
- Default rate limit: 100,000 requests per day per app
- Check `X-RateLimit-Reset` header for when limit resets
---
## API Rate Limits
| Endpoint Type | Rate Limit |
|---------------|------------|
| Profile API | 100,000/day |
| Share API | 100,000/day |
| Social Actions (Comments) | 100,000/day |
The application handles rate limits automatically with retry logic.
---
## Verification
After setup, verify the integration:
1. Navigate to `/social/` in your application
2. Click "Connect LinkedIn Account"
3. Authorize with LinkedIn
4. Verify organization posts are fetched
5. Test replying to a comment
---
## Support Resources
- [LinkedIn Marketing API Documentation](https://learn.microsoft.com/en-us/linkedin/marketing/)
- [LinkedIn Developer Forums](https://www.linkedin.com/developers/forum/)
- [API Status Page](https://www.linkedin-status.com/)
---
*Last Updated: February 2026*
*API Version: RestLi 2.0 (202411)*

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# Meta (Facebook & Instagram) API Setup Guide
This guide provides step-by-step instructions for setting up Meta Graph API integration for managing Facebook Pages and Instagram Business accounts.
---
## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [Meta for Developers Setup](#meta-for-developers-setup)
4. [Environment Configuration](#environment-configuration)
5. [OAuth Redirect URI Configuration](#oauth-redirect-uri-configuration)
6. [Permissions & Scopes](#permissions--scopes)
7. [Webhook Configuration](#webhook-configuration)
8. [Development vs Production](#development-vs-production)
9. [Troubleshooting](#troubleshooting)
---
## Overview
**API Version:** Graph API v24.0
**Base URL:** `https://graph.facebook.com/v24.0`
**Auth URL:** `https://www.facebook.com/v24.0/dialog/oauth`
### Features Supported
- **Facebook Pages:**
- Fetch page posts
- Read comments on posts
- Reply to comments as the Page
- **Instagram Business:**
- Fetch Instagram media posts
- Read comments on posts
- Reply to comments (with nesting limitations)
### How It Works
1. User authenticates with Facebook
2. App discovers all Facebook Pages the user manages
3. For each Page, app also discovers linked Instagram Business accounts
4. Page Access Tokens are permanent (don't expire if app is active)
---
## Prerequisites
- A Facebook account with admin access to at least one Facebook Page
- An Instagram Business Account linked to your Facebook Page
- Access to [Meta for Developers](https://developers.facebook.com/)
- HTTPS-enabled server for production
### Linking Instagram to Facebook Page
1. Go to your Facebook Page settings
2. Navigate to **Instagram** → **Connect Account**
3. Log in to your Instagram Business account
4. Authorize the connection
---
## Meta for Developers Setup
### Step 1: Create a Meta App
1. Navigate to [Meta for Developers](https://developers.facebook.com/apps/)
2. Click **"Create App"**
3. Select **"Business"** as the app type
4. Fill in the details:
- **App Name:** Your application name (e.g., "PX360 Social Manager")
- **App Contact Email:** Your contact email
- **Business Account:** Select your business (if applicable)
5. Click **"Create App"**
6. Complete security verification if prompted
### Step 2: Configure Basic Settings
1. Go to **"Settings"** → **"Basic"**
2. Fill in required fields:
- **Privacy Policy URL:** Your privacy policy URL
- **User Data Deletion:** Provide deletion instructions or URL
- **Category:** Select "Business Tools"
3. Add **App Domains** (your application's domain)
4. Click **"Save Changes"**
### Step 3: Add Facebook Login Product
1. Go to **"Add Products"** (left sidebar)
2. Find **"Facebook Login"** and click **"Set Up"**
3. Select **"Web"** as platform
4. Enter your site URL
5. Configure OAuth settings (see Redirect URI section below)
### Step 4: Get App Credentials
1. Go to **"Settings"** → **"Basic"**
2. Copy the following:
- **App ID** → This is your `META_APP_ID`
- **App Secret** → Click "Show" → This is your `META_APP_SECRET`
> ⚠️ **Important:** Never expose your App Secret in client-side code.
---
## Environment Configuration
### Django Settings (settings.py)
```python
# Meta (Facebook/Instagram) API Configuration
META_APP_ID = 'your_app_id_here'
META_APP_SECRET = 'your_app_secret_here'
META_REDIRECT_URI = 'https://yourdomain.com/social/callback/META/'
META_WEBHOOK_VERIFY_TOKEN = 'your_random_secret_string_here'
```
### Environment Variables (.env)
```env
META_APP_ID=your_app_id_here
META_APP_SECRET=your_app_secret_here
META_REDIRECT_URI=https://yourdomain.com/social/callback/META/
META_WEBHOOK_VERIFY_TOKEN=your_random_secret_string_here
```
---
## OAuth Redirect URI Configuration
### In Meta Developer Portal
1. Go to **"Facebook Login"** → **"Settings"**
2. Under **"Valid OAuth Redirect URIs"**, add:
- Development: `http://127.0.0.1:8000/social/callback/META/`
- Production: `https://yourdomain.com/social/callback/META/`
3. Click **"Save Changes"**
### Development vs Production URIs
| Environment | Redirect URI |
|-------------|--------------|
| Development | `http://127.0.0.1:8000/social/callback/META/` |
| Production | `https://yourdomain.com/social/callback/META/` |
> ⚠️ **Note:** Meta accepts HTTP for localhost but requires HTTPS for production.
---
## Permissions & Scopes
The application requests the following OAuth scopes:
| Scope | Description | Required |
|-------|-------------|----------|
| `pages_manage_engagement` | Reply to comments | ✅ Yes |
| `pages_read_engagement` | Read comments and reactions | ✅ Yes |
| `pages_show_list` | Discover pages and get tokens | ✅ Yes |
| `pages_read_user_content` | Read user-generated content | ✅ Yes |
| `instagram_basic` | Basic Instagram info | ✅ Yes |
| `instagram_manage_comments` | Manage Instagram comments | ✅ Yes |
| `public_profile` | Basic user profile | ✅ Yes |
### Code Reference
```python
# apps/social/utils/meta.py
BASE_GRAPH_URL = "https://graph.facebook.com/v24.0"
BASE_AUTH_URL = "https://www.facebook.com/v24.0"
META_SCOPES = [
"pages_manage_engagement",
"pages_read_engagement",
"pages_show_list",
"pages_read_user_content",
"instagram_basic",
"instagram_manage_comments",
"public_profile",
]
```
### App Review Requirements
Some permissions require Meta's App Review:
1. Go to **"App Review"** → **"Permissions and Features"**
2. Request permissions that require review
3. Submit detailed use case and screencast
4. Typical use case explanation:
> "This application helps businesses manage their social media presence by allowing them to read and respond to comments on their Facebook Pages and Instagram Business accounts from a centralized dashboard."
---
## Webhook Configuration
Webhooks allow real-time notifications for new comments.
### Step 1: Create Webhook Endpoint
Your application needs an endpoint to receive webhook events:
```
POST /social/webhooks/meta/
```
### Step 2: Configure Webhook in Meta Portal
1. Go to **"Webhooks"** in your app dashboard
2. Click **"Add Subscription"**
3. Enter your callback URL:
```
https://yourdomain.com/social/webhooks/meta/
```
4. Enter your verify token (from `META_WEBHOOK_VERIFY_TOKEN`)
5. Click **"Verify and Save"**
### Step 3: Subscribe to Events
1. After verification, select subscriptions:
- **Page Events:** `feed`, `comments`
- **Instagram Events:** `comments`, `mentions`
### Step 4: Subscribe Individual Pages
For each Page, subscribe to webhook events:
```python
# Done automatically during account connection
from apps.social.services.meta import MetaService
MetaService.subscribe_webhook(page_id, page_access_token)
```
### Webhook Verification Handler
```python
def verify_webhook(request):
mode = request.GET.get('hub.mode')
challenge = request.GET.get('hub.challenge')
verify_token = request.GET.get('hub.verify_token')
if mode == 'subscribe' and verify_token == settings.META_WEBHOOK_VERIFY_TOKEN:
return HttpResponse(challenge, status=200)
return HttpResponse(status=403)
```
---
## Development vs Production
### Development Setup
| Setting | Value |
|---------|-------|
| `META_REDIRECT_URI` | `http://127.0.0.1:8000/social/callback/META/` |
| Protocol | HTTP allowed for localhost |
| App Mode | Development |
| App Review | Not required for testing |
| Test Users | Add yourself and team members |
### Production Setup
| Setting | Value |
|---------|-------|
| `META_REDIRECT_URI` | `https://yourdomain.com/social/callback/META/` |
| Protocol | **HTTPS required** |
| App Mode | Live |
| App Review | Required for sensitive permissions |
| Rate Limits | Higher limits for approved apps |
### Using ngrok for Local Webhooks
```bash
# Install ngrok
npm install -g ngrok
# Create tunnel
ngrok http 8000
# Use ngrok URL for webhook
# Example: https://abc123.ngrok.io/social/webhooks/meta/
```
---
## Troubleshooting
### Common Error: "Error Validating Verification Code"
**Cause:** Redirect URI mismatch.
**Solution:**
1. Check exact URL in Facebook Login → Settings → Valid OAuth Redirect URIs
2. Ensure trailing slashes match
3. Verify protocol (http vs https)
---
### Common Error: "Permission Error (Code 200)"
**Cause:** Missing permissions or user doesn't have page access.
**Solution:**
1. Verify all required scopes are requested
2. Ensure user has Page Admin role
3. Check if permission needs App Review approval
---
### Common Error: "Invalid OAuth Access Token (Code 190)"
**Cause:** Token expired or invalid.
**Solution:**
1. Page tokens should be permanent, but user tokens expire
2. User may need to re-authenticate
3. Check if app is in Development mode and user is a test user
---
### Common Error: "Unsupported Post Request (Code 100)"
**Cause:** Trying to reply to a reply on Instagram (nested replies not supported).
**Solution:**
- Instagram only supports 1 level of comment nesting
- You can reply to a top-level comment, but cannot reply to a reply
- The application handles this gracefully
---
### Common Error: "Non-Existent Field 'name' (Instagram)"
**Cause:** Instagram comments use `username`, not `name` for author.
**Solution:**
- The application dynamically selects fields based on platform
- This is handled automatically in the code
---
### Common Error: "Rate Limit (Code 4, 17, 32)"
**Cause:** Too many API requests.
**Solution:**
- Application implements automatic retry with delay
- Wait for rate limit to reset (usually 1 hour)
- Reduce polling frequency
---
### Common Error: "Page Not Found (Code 404)"
**Cause:** Page doesn't exist or user doesn't have access.
**Solution:**
1. Verify page exists and is published
2. Ensure user has Page Admin/Editor role
3. Check page ID is correct
---
## API Rate Limits
| Resource | Rate Limit |
|----------|------------|
| Graph API Calls | 200 calls/hour per user per app |
| Page-level Calls | Higher limits for page tokens |
| Webhook Events | Unlimited |
The application implements rate limiting to stay within bounds.
---
## Facebook vs Instagram ID Detection
The application automatically detects platform based on ID format:
```python
# Instagram IDs typically start with 17 or 18
if str(comment_id).startswith('17') and str(comment_id).isdigit():
platform = 'IG'
elif '_' in str(comment_id):
platform = 'FB' # Facebook IDs often contain underscore
```
---
## Page Access Token Lifecycle
| Token Type | Lifetime | Notes |
|------------|----------|-------|
| User Access Token | ~60 days | Short-lived, can be exchanged |
| Page Access Token | **Permanent** | Doesn't expire if app remains active |
| Instagram Token | Same as Page | Uses Page token for access |
> ✅ **Good News:** Page tokens are permanent. Once a user connects their account, the integration continues working indefinitely.
---
## Verification
After setup, verify the integration:
### For Facebook:
1. Navigate to `/social/` in your application
2. Click "Connect Facebook/Instagram"
3. Authorize with Facebook
4. Select your Facebook Page
5. Verify posts are fetched
6. Test replying to a comment
### For Instagram:
1. After connecting Facebook, Instagram accounts are auto-discovered
2. Verify Instagram Business account appears in account list
3. Check if Instagram media is fetched
4. Test replying to an Instagram comment
### Testing in Django Shell
```python
from apps.social.services.meta import MetaService
from apps.social.models import SocialAccount
# Test Facebook
fb_account = SocialAccount.objects.filter(platform='FB').first()
posts = MetaService.fetch_posts(fb_account.platform_id, fb_account.access_token, 'FB')
print(f"Found {len(posts)} FB posts")
# Test Instagram
ig_account = SocialAccount.objects.filter(platform='IG').first()
if ig_account:
media = MetaService.fetch_posts(ig_account.platform_id, ig_account.access_token, 'IG')
print(f"Found {len(media)} IG media posts")
```
---
## Support Resources
- [Meta Graph API Documentation](https://developers.facebook.com/docs/graph-api/)
- [Facebook Login Guide](https://developers.facebook.com/docs/facebook-login/)
- [Instagram Graph API](https://developers.facebook.com/docs/instagram-api/)
- [Webhooks Documentation](https://developers.facebook.com/docs/graph-api/webhooks/)
- [Meta Bug Tracker](https://developers.facebook.com/support/bugs/)
---
*Last Updated: February 2026*
*API Version: Meta Graph API v24.0*

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# TikTok Business API Setup Guide
This guide provides step-by-step instructions for setting up TikTok Business API integration for managing ad comments.
---
## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [TikTok Business Center Setup](#tiktok-business-center-setup)
4. [Environment Configuration](#environment-configuration)
5. [OAuth Redirect URI Configuration](#oauth-redirect-uri-configuration)
6. [Permissions & Scopes](#permissions--scopes)
7. [Development vs Production](#development-vs-production)
8. [Troubleshooting](#troubleshooting)
---
## Overview
**API Version:** Business API v1.3
**Base URL:** `https://business-api.tiktok.com/open_api/v1.3/`
**Auth Portal:** `https://business-api.tiktok.com/portal/auth`
**Token Endpoint:** `oauth2/access_token/`
### Features Supported
- Fetch advertisements (which act as "content")
- Read comments on advertisements
- Reply to ad comments
### ⚠️ Critical Limitations
> **Important:** This implementation **only supports Ad Comments (Paid Ads)**. TikTok's API does **NOT** support organic video comment management. You cannot:
> - Fetch comments on regular TikTok videos
> - Reply to organic video comments
> - Manage comments on non-ad content
This is a TikTok API limitation, not an application limitation.
---
## Prerequisites
- A TikTok account with access to [TikTok Business Center](https://business.tiktok.com/)
- Admin or Analyst access to a TikTok Ad Account
- An approved TikTok App in Business Center
---
## TikTok Business Center Setup
### Step 1: Access TikTok Business Center
1. Navigate to [TikTok Business Center](https://business.tiktok.com/)
2. Sign in with your TikTok account
3. If you don't have a Business Center, create one
> ⚠️ **Note:** This is **NOT** the same as the "TikTok for Developers" portal used for the Display API. You must use the Business Center.
### Step 2: Create or Access Your App
1. Go to **User Center** (top right) → **App Management** → **My Apps**
2. If you don't have an app, click **"Create App"**
3. Fill in the required details:
- **App Name:** Your application name
- **App Description:** Describe your use case
- **Category:** Select "Business Tools" or similar
4. Submit for approval
### Step 3: Get App Credentials
Once your app is created/approved:
1. Go to **User Center** → **App Management** → **My Apps**
2. Select your app
3. Find the credentials:
- **App ID** → This is your `TIKTOK_CLIENT_KEY`
- **App Secret** → Click "View" to reveal → This is your `TIKTOK_CLIENT_SECRET`
> ⚠️ **Important:** Store these credentials securely. The App Secret is only shown once.
---
## Environment Configuration
### Django Settings (settings.py)
```python
# TikTok Business API Configuration
TIKTOK_CLIENT_KEY = 'your_app_id_here'
TIKTOK_CLIENT_SECRET = 'your_app_secret_here'
TIKTOK_REDIRECT_URI = 'https://yourdomain.com/social/callback/TT/'
```
### Environment Variables (.env)
```env
TIKTOK_CLIENT_KEY=your_app_id_here
TIKTOK_CLIENT_SECRET=your_app_secret_here
TIKTOK_REDIRECT_URI=https://yourdomain.com/social/callback/TT/
```
---
## OAuth Redirect URI Configuration
### Step 1: Configure Redirect URI in TikTok App
1. In your app settings, go to **App Settings** → **Login Kit** / **Redirect URI settings**
2. Add your callback URL:
**Development:**
```
http://127.0.0.1:8000/social/callback/TT/
http://localhost:8000/social/callback/TT/
```
> ⚠️ **Note:** TikTok often rejects `localhost` URLs. Use ngrok for local testing (see below).
**Production:**
```
https://yourdomain.com/social/callback/TT/
```
### Using ngrok for Local Development
TikTok may reject HTTP/localhost redirect URIs. Use ngrok:
```bash
# Install ngrok
npm install -g ngrok
# Create tunnel
ngrok http 8000
# Use the ngrok URL as your redirect URI
# Example: https://abc123.ngrok.io/social/callback/TT/
# Update settings.py
TIKTOK_REDIRECT_URI = 'https://abc123.ngrok.io/social/callback/TT/'
```
---
## Permissions & Scopes
The application requests the following OAuth scopes:
| Scope | Description | Required |
|-------|-------------|----------|
| `user.info.basic` | Basic user information | ✅ Yes |
| `ad.read` | Read advertisement data | ✅ Yes |
| `comment.manage` | Manage ad comments | ✅ Yes |
### Code Reference
```python
# apps/social/utils/tiktok.py
class TikTokConstants:
BASE_URL = "https://business-api.tiktok.com/open_api/v1.3/"
SCOPES = "user.info.basic,ad.read,comment.manage"
ENDPOINTS = {
"AUTH": "https://business-api.tiktok.com/portal/auth",
"TOKEN": "oauth2/access_token/",
"USER_INFO": "user/info/",
"AD_LIST": "ad/get/",
"COMMENT_LIST": "comment/list/",
"COMMENT_REPLY": "comment/reply/",
}
```
### Requesting Permissions
1. In TikTok Business Center, go to **App Management** → **Permissions**
2. Request permission for:
- **Ads Management** (for `ad.read`)
- **Comments Management** (for `comment.manage`)
3. Submit a use case explaining:
> "We are building a Social Media Management Tool for managing ad comments. This allows advertisers to respond to user engagement on their TikTok advertisements from a centralized dashboard."
> ⚠️ **Note:** TikTok may reject generic requests. Be specific about your use case.
---
## Ad Account Access Requirements
### User Permissions
When connecting via OAuth, the authenticating user must have proper access to the Ad Account:
| Role | Can Sync Ads | Can Reply to Comments |
|------|--------------|----------------------|
| Admin | ✅ Yes | ✅ Yes |
| Analyst | ✅ Yes | ❌ No (read-only) |
| Operator | ✅ Yes | ✅ Yes |
### Granting Access
1. In TikTok Business Center, go to **Ad Accounts**
2. Select your ad account
3. Go to **User Permissions**
4. Add users with appropriate roles
---
## Development vs Production
### Development Setup
| Setting | Value |
|---------|-------|
| `TIKTOK_REDIRECT_URI` | `https://xxx.ngrok.io/social/callback/TT/` (via ngrok) |
| Protocol | HTTPS recommended (ngrok) |
| App Status | Sandbox/Testing mode |
### Production Setup
| Setting | Value |
|---------|-------|
| `TIKTOK_REDIRECT_URI` | `https://yourdomain.com/social/callback/TT/` |
| Protocol | **HTTPS required** |
| App Status | Approved/Production mode |
---
## Troubleshooting
### Common Error: "Invalid Redirect URI"
**Cause:** The redirect URI doesn't match TikTok's configuration.
**Solution:**
1. Verify exact URL in TikTok Business Center → App Settings → Redirect URI
2. Ensure HTTPS is used (or ngrok URL)
3. Check for trailing slashes
4. Wait a few minutes for changes to propagate
---
### Common Error: "Permission Denied for Ad Account"
**Cause:** User doesn't have access to the ad account.
**Solution:**
1. Verify user has Admin, Operator, or Analyst role
2. Check ad account permissions in Business Center
3. Ensure correct ad account is selected during OAuth flow
---
### Common Error: "Scope Not Authorized"
**Cause:** App hasn't been approved for requested permissions.
**Solution:**
1. Go to App Management → Permissions
2. Request required permissions with detailed use case
3. Wait for TikTok approval (can take several days)
---
### Common Error: "No Ads Found"
**Cause:** No active ads in the ad account, or ads don't have comments.
**Solution:**
1. Verify ads exist and are active in TikTok Ads Manager
2. Ensure ads have received comments
3. Check that ad account is properly linked
---
### Common Error: "API Rate Limit Exceeded"
**Cause:** Too many API requests.
**Solution:**
- TikTok Business API has rate limits (varies by endpoint)
- Implement exponential backoff
- Wait for limit to reset
---
### Common Error: "Cannot Reply to Comment"
**Cause:** User has Analyst role (read-only) or comment is deleted.
**Solution:**
1. Ensure user has Admin or Operator role
2. Verify the comment still exists
3. Check that the ad is still active
---
## API Rate Limits
| Endpoint | Rate Limit |
|----------|------------|
| Ad List | 10 requests/second |
| Comment List | 10 requests/second |
| Comment Reply | 10 requests/second |
The application implements rate limiting to stay within these bounds.
---
## API Endpoints Used
| Endpoint | Purpose |
|----------|---------|
| `oauth2/access_token/` | Exchange auth code for access token |
| `user/info/` | Get authenticated user information |
| `ad/get/` | Fetch advertisements for an advertiser |
| `comment/list/` | List comments on an advertisement |
| `comment/reply/` | Reply to a comment |
---
## Verification
After setup, verify the integration:
1. Navigate to `/social/` in your application
2. Click "Connect TikTok"
3. Authorize with TikTok Business account
4. Select your advertiser account
5. Verify ads are fetched
6. Check if comments on ads are loaded
7. Test replying to an ad comment
### Testing in Django Shell
```python
from apps.social.services.tiktok import TikTokService
from apps.social.models import SocialAccount
account = SocialAccount.objects.filter(platform='TT').first()
# Test getting ads
ads = TikTokService.fetch_ads(account)
print(f"Found {len(ads)} ads")
# Test getting comments
if ads:
comments = TikTokService.fetch_comments(account, ads[0]['ad_id'])
print(f"Found {len(comments)} comments")
```
---
## Important Notes
1. **Organic Content Not Supported:** TikTok's Business API only supports ad management. You cannot manage comments on organic (non-ad) videos.
2. **Advertiser ID Required:** You need a valid TikTok Advertiser ID with active ads to use this integration.
3. **App Approval:** TikTok may take several days to approve your app and permission requests.
4. **HTTPS Required:** Production redirect URIs must use HTTPS.
5. **Regional Availability:** TikTok Business API may not be available in all regions.
---
## Support Resources
- [TikTok Business API Documentation](https://ads.tiktok.com/marketing_api/docs)
- [TikTok Business Center](https://business.tiktok.com/)
- [TikTok Marketing API Forum](https://community.tiktok.com/)
- [TikTok Ads Manager](https://ads.tiktok.com/)
---
*Last Updated: February 2026*
*API Version: TikTok Business API v1.3*

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# X (Twitter) API Setup Guide
This guide provides step-by-step instructions for setting up X (formerly Twitter) API integration for managing tweets and replies.
---
## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [X Developer Portal Setup](#x-developer-portal-setup)
4. [Environment Configuration](#environment-configuration)
5. [OAuth Redirect URI Configuration](#oauth-redirect-uri-configuration)
6. [Permissions & Scopes](#permissions--scopes)
7. [API Tiers & Limitations](#api-tiers--limitations)
8. [Development vs Production](#development-vs-production)
9. [Troubleshooting](#troubleshooting)
---
## Overview
**API Version:** X API v2
**Base URL:** `https://api.twitter.com/2`
**Auth URL:** `https://twitter.com/i/oauth2/authorize`
**Token URL:** `https://api.twitter.com/2/oauth2/token`
**Auth Method:** OAuth 2.0 with PKCE (Proof Key for Code Exchange)
### Features Supported
- Fetch user tweets
- Read tweet replies (conversation threads)
- Reply to tweets
- Automatic token refresh
### ⚠️ Important Limitations
- **Search API** (for fetching replies) requires **Basic tier or higher**
- **Free tier** cannot search for replies - only direct mentions accessible
- The system supports both tiers with graceful degradation
---
## Prerequisites
- An X (Twitter) account
- Access to [X Developer Portal](https://developer.twitter.com/)
- HTTPS-enabled server for production (required for OAuth redirect URIs)
---
## X Developer Portal Setup
### Step 1: Apply for Developer Access
1. Navigate to [X Developer Portal](https://developer.twitter.com/en/portal/dashboard)
2. Sign in with your X account
3. Click **"Sign up"** for free access (or choose a paid tier)
4. Fill out the application form:
- **Country:** Select your country
- **Use Case:** Select "Building tools for my own use" or appropriate option
- **Description:** Describe your use case in detail:
> "We are building a social media management dashboard that allows organizations to manage and respond to comments and replies on their X/Twitter posts from a centralized interface. This helps community managers respond faster to audience engagement."
5. Review and accept the developer agreement
6. Click **"Submit"**
### Step 2: Create a Project & App
1. Once approved, go to the [Developer Portal](https://developer.twitter.com/en/portal/dashboard)
2. Create a **Project**:
- **Project Name:** e.g., "PX360 Social"
- **Use Case:** Select "Building tools for my own use"
- **Project Description:** Brief description of your application
3. Create an **App** within the project:
- **App Name:** Unique name for your app (must be globally unique)
- **Environment:** Select "Production"
### Step 3: Configure OAuth 2.0
1. In your app settings, go to **"Settings"** tab
2. Scroll to **"User authentication settings"**
3. Click **"Set up"**
4. Select **"Web App, Automated App or Bot"**
5. Configure OAuth 2.0:
**General Settings:**
- **Callback URI / Redirect URL:**
- Development: `http://127.0.0.1:8000/social/callback/X/`
- Production: `https://yourdomain.com/social/callback/X/`
- **Website URL:** Your application URL
- **Terms of Service URL:** (Optional) Your ToS URL
- **Privacy Policy URL:** (Optional) Your privacy policy URL
6. Click **"Save"**
### Step 4: Get API Credentials
1. Go to **"Keys and Tokens"** tab in your app
2. Under **"OAuth 2.0 Client ID and Client Secret"**:
- Click **"Regenerate"** if needed
- Copy the **Client ID** → This is your `X_CLIENT_ID`
- Copy the **Client Secret** → This is your `X_CLIENT_SECRET`
> ⚠️ **Important:** The Client Secret is only shown once. Store it securely!
---
## Environment Configuration
### Django Settings (settings.py)
```python
# X (Twitter) API Configuration
X_CLIENT_ID = 'your_client_id_here'
X_CLIENT_SECRET = 'your_client_secret_here'
X_REDIRECT_URI = 'https://yourdomain.com/social/callback/X/'
# TIER CONFIGURATION
# Set to True if you have Enterprise Access (Full-Archive Search)
# Set to False for Free/Basic/Pro tiers (Recent Search)
X_USE_ENTERPRISE = False
```
### Environment Variables (.env)
```env
X_CLIENT_ID=your_client_id_here
X_CLIENT_SECRET=your_client_secret_here
X_REDIRECT_URI=https://yourdomain.com/social/callback/X/
X_USE_ENTERPRISE=False
```
---
## OAuth Redirect URI Configuration
The redirect URI must match exactly what's configured in the X Developer Portal.
### Development
```
http://127.0.0.1:8000/social/callback/X/
http://localhost:8000/social/callback/X/
```
### Production
```
https://yourdomain.com/social/callback/X/
```
> ⚠️ **Note:** X requires HTTPS for production redirect URIs. Localhost is allowed for development.
---
## Permissions & Scopes
The application requests the following OAuth scopes:
| Scope | Description | Required |
|-------|-------------|----------|
| `tweet.read` | Read tweets and timeline | ✅ Yes |
| `tweet.write` | Post and reply to tweets | ✅ Yes |
| `users.read` | Read user profile information | ✅ Yes |
| `offline.access` | Refresh tokens for long-term access | ✅ Yes |
### Code Reference
```python
# apps/social/utils/x.py
class XConfig:
BASE_URL = "https://api.twitter.com/2"
AUTH_URL = "https://twitter.com/i/oauth2/authorize"
TOKEN_URL = "https://api.twitter.com/2/oauth2/token"
SCOPES = [
"tweet.read",
"tweet.write",
"users.read",
"offline.access"
]
```
---
## API Tiers & Limitations
X API has different access tiers with varying capabilities:
### Free Tier ($0/month)
| Feature | Limit |
|---------|-------|
| Post Tweets | 1,500/month |
| User Lookup | 500/month |
| **Search API** | ❌ **NOT AVAILABLE** |
> ⚠️ **Critical:** Free tier cannot use the search endpoint, which means **fetching tweet replies is not possible**. You will see a 403 Forbidden error when trying to search.
### Basic Tier ($100/month)
| Feature | Limit |
|---------|-------|
| Post Tweets | 3,000/month |
| Recent Search | 60 requests/15 min |
| User Lookup | 900/15 min |
| **Reply Fetching** | ✅ **SUPPORTED** |
> ✅ **Recommended:** Basic tier is the minimum required for full functionality.
### Pro Tier ($5,000/month)
| Feature | Limit |
|---------|-------|
| Post Tweets | 100,000/month |
| Recent Search | 300 requests/15 min |
| Full-Archive Search | Available |
### Enterprise Tier (Custom)
| Feature | Limit |
|---------|-------|
| Full-Archive Search | ✅ Available |
| Higher Rate Limits | Custom |
### Setting the Tier
```python
# For Basic/Pro tiers (Recent Search)
X_USE_ENTERPRISE = False # Uses tweets/search/recent
# For Enterprise tier (Full-Archive Search)
X_USE_ENTERPRISE = True # Uses tweets/search/all
```
---
## Development vs Production
### Development Setup
| Setting | Value |
|---------|-------|
| `X_REDIRECT_URI` | `http://127.0.0.1:8000/social/callback/X/` |
| Protocol | HTTP allowed for localhost |
| `X_USE_ENTERPRISE` | `False` |
| Rate Limits | Lower limits per 15-min window |
### Production Setup
| Setting | Value |
|---------|-------|
| `X_REDIRECT_URI` | `https://yourdomain.com/social/callback/X/` |
| Protocol | **HTTPS required** |
| `X_USE_ENTERPRISE` | Based on your tier |
| Rate Limits | Higher limits for paid tiers |
### Using ngrok for Local Testing
If you need to test with HTTPS locally:
```bash
# Install ngrok
npm install -g ngrok
# Create tunnel to local server
ngrok http 8000
# Use the ngrok URL as your redirect URI
# Example: https://abc123.ngrok.io/social/callback/X/
# Update X Developer Portal with the ngrok URL
```
---
## Troubleshooting
### Common Error: "403 Forbidden on Search"
**Cause:** You're on the Free tier which doesn't include Search API access.
**Solution:**
1. Upgrade to at least **Basic tier** ($100/month)
2. Or, accept limited functionality (cannot fetch replies)
3. Check your tier in X Developer Portal → Products
---
### Common Error: "Invalid Redirect URI"
**Cause:** The redirect URI doesn't match what's configured in X Developer Portal.
**Solution:**
1. Go to X Developer Portal → Your App → Settings
2. Check "User authentication settings"
3. Ensure redirect URI matches exactly (including trailing slash)
4. Wait a few minutes for changes to propagate
---
### Common Error: "Client Authentication Failed"
**Cause:** Invalid Client ID/Secret or incorrect auth header format.
**Solution:**
1. Verify credentials in X Developer Portal → Keys and Tokens
2. Ensure you're using **Basic Auth** for token exchange:
```python
# Correct: Base64 encoded "client_id:client_secret"
Authorization: Basic base64(client_id:client_secret)
```
3. Regenerate credentials if needed
---
### Common Error: "Rate Limit Exceeded (429)"
**Cause:** Too many API requests in the rate limit window.
**Solution:**
- Application handles rate limits with automatic retry
- Check `x-rate-limit-reset` header for reset time
- Wait for the window to reset (usually 15 minutes)
- Consider upgrading to a higher tier for more capacity
---
### Common Error: "Token Expired"
**Cause:** Access token expired (typically after 2 hours).
**Solution:**
- Application automatically refreshes tokens using `offline.access` scope
- If refresh fails, user needs to re-authenticate
- Check that refresh token is stored in database
---
### Common Error: "PKCE Verification Failed"
**Cause:** Code verifier doesn't match the challenge used during authorization.
**Solution:**
- Application generates PKCE pair automatically
- Ensure `code_verifier` is stored in session during auth flow
- Verify S256 challenge method is used
---
## API Rate Limits
### Free Tier
| Endpoint | Rate Limit |
|----------|------------|
| Post Tweet | 1,500/month |
| Get User | 500/month |
### Basic Tier
| Endpoint | Rate Limit |
|----------|------------|
| Post Tweet | 3,000/month |
| Recent Search | 60/15 min |
| Get User | 900/15 min |
### Pro Tier
| Endpoint | Rate Limit |
|----------|------------|
| Post Tweet | 100,000/month |
| Recent Search | 300/15 min |
| Get User | 900/15 min |
---
## Verification
After setup, verify the integration:
1. Navigate to `/social/` in your application
2. Click "Connect X (Twitter)"
3. Authorize with your X account
4. Verify your profile is fetched
5. Check if tweets are loaded
6. Test posting a reply to a tweet
### Testing Search (Basic+ Tier Required)
```python
# In Django shell
from apps.social.services.x import XService
from apps.social.models import SocialAccount
account = SocialAccount.objects.filter(platform='X').first()
tweets = XService.get_user_tweets(account)
print(f"Fetched {len(tweets)} tweets")
```
---
## Support Resources
- [X API Documentation](https://developer.twitter.com/en/docs/twitter-api)
- [X API v2 Reference](https://developer.twitter.com/en/docs/twitter-api/users/lookup/api-reference)
- [OAuth 2.0 PKCE Guide](https://developer.twitter.com/en/docs/authentication/oauth-2-0/authorization-code)
- [X Developer Forum](https://twittercommunity.com/)
- [X API Status](https://api.twitterstat.us/)
---
*Last Updated: February 2026*
*API Version: X API v2*

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# YouTube Data API Setup Guide
This guide provides step-by-step instructions for setting up YouTube Data API integration for managing video comments.
---
## Table of Contents
1. [Overview](#overview)
2. [Prerequisites](#prerequisites)
3. [Google Cloud Console Setup](#google-cloud-console-setup)
4. [Environment Configuration](#environment-configuration)
5. [OAuth Redirect URI Configuration](#oauth-redirect-uri-configuration)
6. [Permissions & Scopes](#permissions--scopes)
7. [Development vs Production](#development-vs-production)
8. [Troubleshooting](#troubleshooting)
---
## Overview
**API Version:** YouTube Data API v3
**API Service Name:** `youtube`
**API Version:** `v3`
**Auth Method:** OAuth 2.0
### Features Supported
- Fetch channel videos
- Read video comments
- Reply to comments
- Automatic token refresh
---
## Prerequisites
- A Google account with a YouTube channel
- Access to [Google Cloud Console](https://console.cloud.google.com/)
- Videos uploaded to your YouTube channel
---
## Google Cloud Console Setup
### Step 1: Create a New Project
1. Navigate to [Google Cloud Console](https://console.cloud.google.com/)
2. Click on the project selector dropdown at the top
3. Click **"New Project"**
4. Enter project details:
- **Project Name:** e.g., "PX360 YouTube Integration"
- **Organization:** Select your organization (if applicable)
5. Click **"Create"**
6. Select your new project
### Step 2: Enable YouTube Data API
1. Go to **"APIs & Services"** → **"Library"**
2. Search for **"YouTube Data API v3"**
3. Click on it and click **"Enable"**
### Step 3: Configure OAuth Consent Screen
1. Go to **"APIs & Services"** → **"OAuth consent screen"**
2. Select **"External"** user type (unless you have a Google Workspace account)
3. Click **"Create"**
4. Fill in the required fields:
- **App Name:** Your application name
- **User Support Email:** Your support email
- **App Logo:** Upload your logo
- **Application Home Page:** Your website URL
- **Authorized Domains:** Your domain(s)
- **Developer Contact Email:** Your email
5. Click **"Save and Continue"**
6. Add scopes (click "Add or Remove Scopes"):
- `https://www.googleapis.com/auth/youtube.readonly`
- `https://www.googleapis.com/auth/youtube.force-ssl`
7. Click **"Save and Continue"**
8. Add test users (for development)
9. Click **"Save and Continue"**
### Step 4: Create OAuth 2.0 Credentials
1. Go to **"APIs & Services"** → **"Credentials"**
2. Click **"Create Credentials"** → **"OAuth client ID"**
3. Select **"Web application"**
4. Configure:
- **Name:** e.g., "PX360 YouTube Client"
- **Authorized JavaScript origins:**
- Development: `http://127.0.0.1:8000`
- Production: `https://yourdomain.com`
- **Authorized redirect URIs:**
- Development: `http://127.0.0.1:8000/social/callback/YT/`
- Production: `https://yourdomain.com/social/callback/YT/`
5. Click **"Create"**
6. **Download the JSON file** - This is your credentials file
### Step 5: Save Credentials File
1. Rename the downloaded JSON file to `yt_client_secrets.json`
2. Place it in your project's `secrets/` directory:
```
your_project/
├── secrets/
│ ├── gmb_client_secrets.json
│ └── yt_client_secrets.json
└── ...
```
The JSON file structure:
```json
{
"web": {
"client_id": "xxxxx.apps.googleusercontent.com",
"project_id": "your-project-id",
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_secret": "your-client-secret",
"redirect_uris": ["http://127.0.0.1:8000/social/callback/YT/"]
}
}
```
---
## Environment Configuration
### Django Settings (settings.py)
```python
# YouTube Data API Configuration
import os
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent.parent.parent
# YouTube API Configuration
YOUTUBE_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'yt_client_secrets.json'
YOUTUBE_REDIRECT_URI = 'https://yourdomain.com/social/callback/YT/'
```
### Environment Variables (.env)
```env
YOUTUBE_REDIRECT_URI=https://yourdomain.com/social/callback/YT/
```
---
## OAuth Redirect URI Configuration
The redirect URI must match exactly what's configured in Google Cloud Console.
### Development
```
http://127.0.0.1:8000/social/callback/YT/
http://localhost:8000/social/callback/YT/
```
### Production
```
https://yourdomain.com/social/callback/YT/
```
> ⚠️ **Note:** Google accepts both HTTP and HTTPS for `localhost`/`127.0.0.1`, but production must use HTTPS.
---
## Permissions & Scopes
The application requests the following OAuth scopes:
| Scope | Description | Required |
|-------|-------------|----------|
| `https://www.googleapis.com/auth/youtube.readonly` | Read channel, videos, comments | ✅ Yes |
| `https://www.googleapis.com/auth/youtube.force-ssl` | Post replies to comments | ✅ Yes |
### Code Reference
```python
# apps/social/utils/youtube.py
YOUTUBE_SCOPES = [
'https://www.googleapis.com/auth/youtube.readonly',
'https://www.googleapis.com/auth/youtube.force-ssl'
]
YOUTUBE_API_SERVICE_NAME = 'youtube'
YOUTUBE_API_VERSION = 'v3'
```
---
## Development vs Production
### Development Setup
| Setting | Value |
|---------|-------|
| `YOUTUBE_REDIRECT_URI` | `http://127.0.0.1:8000/social/callback/YT/` |
| Protocol | HTTP allowed for localhost |
| App Verification | Not required for testing |
| User Access | Only added test users |
### Production Setup
| Setting | Value |
|---------|-------|
| `YOUTUBE_REDIRECT_URI` | `https://yourdomain.com/social/callback/YT/` |
| Protocol | **HTTPS required** |
| App Verification | Required for sensitive scopes |
| User Access | Any Google account |
### Google App Verification
For production, if your app requests sensitive scopes (like YouTube), you may need verification:
1. Submit your app for verification in Google Cloud Console
2. Provide a demo video showing the integration
3. Wait for Google's review (can take several days)
---
## YouTube API Quotas
YouTube Data API has strict quota limits:
### Default Quota
| Resource | Daily Quota |
|----------|-------------|
| Total API Units | 10,000 units/day |
| Search | 100 units/request |
| Videos List | 1 unit/request |
| Comments List | 1 unit/request |
| Comments Insert | 50 units/request |
### Quota Calculation Example
```
Daily sync of 50 videos:
- 50 video list requests = 50 units
- Comments for each video (avg 5 pages) = 250 units
- Total: ~300 units/day (well within limits)
```
### Requesting Higher Quota
If you need more quota:
1. Go to Google Cloud Console → **"IAM & Admin"** → **"Quotas"**
2. Filter by "YouTube Data API"
3. Click **"Edit Quotas"**
4. Submit a request explaining your use case
---
## Troubleshooting
### Common Error: "Access Denied - Requested client not authorized"
**Cause:** OAuth consent screen not configured or app not verified.
**Solution:**
1. Ensure OAuth consent screen is properly configured
2. Add user as a test user if app is in testing mode
3. Submit app for verification if needed for production
---
### Common Error: "Invalid Grant"
**Cause:** Authorization code expired or already used.
**Solution:**
- Authorization codes are single-use and expire quickly
- Ensure your code handles the callback immediately
- Check that redirect URI matches exactly
---
### Common Error: "Quota Exceeded"
**Cause:** Daily API quota exceeded.
**Solution:**
1. Check quota usage in Google Cloud Console
2. Optimize API calls to use fewer units
3. Request quota increase if needed
4. Wait for quota reset (daily at midnight Pacific Time)
---
### Common Error: "Comments Disabled for This Video"
**Cause:** Video owner has disabled comments.
**Solution:**
- This is expected behavior for some videos
- Application handles this gracefully
- Skip videos with disabled comments
---
### Common Error: "No Uploads Playlist ID Found"
**Cause:** Channel has no uploaded videos or credentials incomplete.
**Solution:**
1. Ensure the YouTube channel has uploaded videos
2. Check that the uploads playlist ID is stored in credentials
3. Re-authenticate if needed
---
### Common Error: "Token Refresh Failed"
**Cause:** Refresh token expired or revoked.
**Solution:**
- Google OAuth tokens expire after 6 months of inactivity
- User must re-authenticate
- Ensure `offline` access type is requested
---
### Common Error: "Comment Thread Not Found"
**Cause:** Comment was deleted or video was removed.
**Solution:**
- Application handles 404 errors gracefully
- Skip deleted comments during sync
---
## API Rate Limits & Best Practices
### Rate Limits
| Resource | Limit |
|----------|-------|
| Requests per second | Varies by endpoint |
| Daily quota units | 10,000 (default) |
### Best Practices
1. **Batch Requests:** Minimize API calls by fetching multiple items
2. **Pagination:** Use page tokens for large result sets
3. **Caching:** Cache video/comment data locally
4. **Delta Sync:** Only fetch new comments since last sync
5. **Error Handling:** Gracefully handle quota and rate limit errors
---
## Token Lifecycle
| Token Type | Lifetime | Notes |
|------------|----------|-------|
| Access Token | ~1 hour | Short-lived |
| Refresh Token | 6 months | Expires with inactivity |
| Offline Access | Indefinite | Requires `offline` access type |
> ⚠️ **Note:** If a refresh token expires, the user must re-authenticate.
---
## Verification
After setup, verify the integration:
1. Ensure `yt_client_secrets.json` is in place
2. Navigate to `/social/` in your application
3. Click "Connect YouTube"
4. Authorize with your Google account
5. Verify your channel is detected
6. Check if videos are fetched
7. Test replying to a comment
### Testing in Django Shell
```python
from apps.social.services.youtube import YouTubeService
from apps.social.models import SocialAccount
account = SocialAccount.objects.filter(platform='YT').first()
# Test getting videos
videos = YouTubeService.fetch_user_videos(account)
print(f"Found {len(videos)} videos")
# Test getting comments
if videos:
video_id = videos[0]['id']
comments = YouTubeService.fetch_video_comments(account, video_id)
print(f"Found {len(comments)} comments for video {video_id}")
```
---
## Sharing Credentials with Google Business
If you're already using Google Business API, you can use the same Google Cloud project:
1. Both APIs use OAuth 2.0 with similar configuration
2. You can create separate OAuth clients or use the same one
3. The `secrets/` directory can contain multiple credential files:
- `gmb_client_secrets.json` (Google Business)
- `yt_client_secrets.json` (YouTube)
### Single OAuth Client for Both
You can use a single OAuth client for both services:
1. Enable both APIs in Google Cloud Console
2. Request scopes for both services during OAuth
3. Use the same credentials file
4. Update settings to point to the same file:
```python
# If using single OAuth client
GOOGLE_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'google_client_secrets.json'
GMB_CLIENT_SECRETS_FILE = GOOGLE_CLIENT_SECRETS_FILE
YOUTUBE_CLIENT_SECRETS_FILE = GOOGLE_CLIENT_SECRETS_FILE
```
---
## Support Resources
- [YouTube Data API Documentation](https://developers.google.com/youtube/v3)
- [YouTube API Code Samples](https://developers.google.com/youtube/v3/code_samples)
- [OAuth 2.0 for Web Server Applications](https://developers.google.com/identity/protocols/oauth2/web-server)
- [YouTube API Quota Calculator](https://developers.google.com/youtube/v3/determine_quota_cost)
- [Google Cloud Console Support](https://support.google.com/cloud/)
---
*Last Updated: February 2026*
*API Version: YouTube Data API v3*

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"""
Django signals for automatic AI analysis of comments.
Triggers background analysis when new comments are created.
"""
import logging
from django.db.models.signals import post_save
from django.dispatch import receiver
from apps.social.models import SocialComment
from apps.social.tasks.ai import analyze_comment_task
logger = logging.getLogger(__name__)
@receiver(post_save, sender=SocialComment)
def analyze_comment_on_creation(sender, instance, created, **kwargs):
"""
Automatically trigger AI analysis when a new comment is created.
This signal fires immediately after a SocialComment is saved to the database.
It checks if it's a new comment (created=True) and triggers
background analysis via Celery.
Args:
sender: The model class that sent the signal
instance: The actual comment instance that was saved
created: Boolean - True if this is a new record, False if it's an update
**kwargs: Additional keyword arguments
"""
if created:
logger.info(f"New comment created (ID: {instance.id}), triggering AI analysis...")
# Trigger background analysis task
try:
analyze_comment_task.delay(instance.id)
logger.info(f"AI analysis task queued for comment {instance.id}")
except Exception as e:
logger.error(f"Failed to queue AI analysis for comment {instance.id}: {e}")
# Don't raise exception to avoid preventing comment from being saved

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"""
Celery scheduled tasks for social media comment scraping and analysis.
"""
import logging
from celery import shared_task
from celery.schedules import crontab
from django.utils import timezone
from datetime import timedelta
from django.conf import settings
from .services import CommentService
from .services.analysis_service import AnalysisService
logger = logging.getLogger(__name__)
# Analysis settings
ANALYSIS_BATCH_SIZE = 10 # Number of comments to analyze per batch
logger = logging.getLogger(__name__)
@shared_task
def scrape_all_platforms():
"""
Scheduled task to scrape all configured social media platforms.
This task is scheduled using Celery Beat.
After scraping, automatically queues analysis for pending comments.
Usage: Schedule this task to run at regular intervals (e.g., daily, hourly)
Returns:
Dictionary with results from each platform
"""
logger.info("Starting scheduled scrape for all platforms")
try:
service = CommentService()
results = service.scrape_and_save()
logger.info(f"Completed scheduled scrape. Results: {results}")
# Automatically queue analysis for pending comments
analyze_pending_comments.delay(limit=ANALYSIS_BATCH_SIZE)
logger.info("Queued analysis task for pending comments")
return results
except Exception as e:
logger.error(f"Error in scheduled scrape task: {e}")
raise
@shared_task
def scrape_youtube_comments(channel_id: str = None):
"""
Scheduled task to scrape YouTube comments.
Args:
channel_id: Optional YouTube channel ID (uses default from settings if not provided)
Returns:
Dictionary with 'total' and 'comments'
"""
logger.info("Starting scheduled YouTube scrape")
try:
service = CommentService()
result = service.scrape_youtube(channel_id=channel_id, save_to_db=True)
logger.info(f"Completed YouTube scrape. Total comments: {len(result)}")
# Automatically queue analysis for pending comments
analyze_pending_comments.delay(limit=ANALYSIS_BATCH_SIZE)
logger.info("Queued analysis task for pending comments")
return {'total': len(result), 'comments': result}
except Exception as e:
logger.error(f"Error in YouTube scrape task: {e}")
raise
@shared_task
def scrape_facebook_comments(page_id: str = None):
"""
Scheduled task to scrape Facebook comments.
Args:
page_id: Optional Facebook page ID (uses default from settings if not provided)
Returns:
Dictionary with 'total' and 'comments'
"""
logger.info("Starting scheduled Facebook scrape")
try:
service = CommentService()
result = service.scrape_facebook(page_id=page_id, save_to_db=True)
logger.info(f"Completed Facebook scrape. Total comments: {len(result)}")
# Automatically queue analysis for pending comments
analyze_pending_comments.delay(limit=ANALYSIS_BATCH_SIZE)
logger.info("Queued analysis task for pending comments")
return {'total': len(result), 'comments': result}
except Exception as e:
logger.error(f"Error in Facebook scrape task: {e}")
raise
@shared_task
def scrape_instagram_comments(account_id: str = None):
"""
Scheduled task to scrape Instagram comments.
Args:
account_id: Optional Instagram account ID (uses default from settings if not provided)
Returns:
Dictionary with 'total' and 'comments'
"""
logger.info("Starting scheduled Instagram scrape")
try:
service = CommentService()
result = service.scrape_instagram(account_id=account_id, save_to_db=True)
logger.info(f"Completed Instagram scrape. Total comments: {len(result)}")
# Automatically queue analysis for pending comments
analyze_pending_comments.delay(limit=ANALYSIS_BATCH_SIZE)
logger.info("Queued analysis task for pending comments")
return {'total': len(result), 'comments': result}
except Exception as e:
logger.error(f"Error in Instagram scrape task: {e}")
raise
@shared_task
def scrape_twitter_comments(username: str = None):
"""
Scheduled task to scrape Twitter/X comments (replies).
Args:
username: Optional Twitter username (uses default from settings if not provided)
Returns:
Dictionary with 'total' and 'comments'
"""
logger.info("Starting scheduled Twitter/X scrape")
try:
service = CommentService()
result = service.scrape_twitter(username=username, save_to_db=True)
logger.info(f"Completed Twitter/X scrape. Total comments: {len(result)}")
# Automatically queue analysis for pending comments
analyze_pending_comments.delay(limit=ANALYSIS_BATCH_SIZE)
logger.info("Queued analysis task for pending comments")
return {'total': len(result), 'comments': result}
except Exception as e:
logger.error(f"Error in Twitter/X scrape task: {e}")
raise
@shared_task
def scrape_linkedin_comments(organization_id: str = None):
"""
Scheduled task to scrape LinkedIn comments from organization posts.
Args:
organization_id: Optional LinkedIn organization URN (uses default from settings if not provided)
Returns:
Dictionary with 'total' and 'comments'
"""
logger.info("Starting scheduled LinkedIn scrape")
try:
service = CommentService()
result = service.scrape_linkedin(organization_id=organization_id, save_to_db=True)
logger.info(f"Completed LinkedIn scrape. Total comments: {len(result)}")
# Automatically queue analysis for pending comments
analyze_pending_comments.delay(limit=ANALYSIS_BATCH_SIZE)
logger.info("Queued analysis task for pending comments")
return {'total': len(result), 'comments': result}
except Exception as e:
logger.error(f"Error in LinkedIn scrape task: {e}")
raise
@shared_task
def scrape_google_reviews(location_names: list = None):
"""
Scheduled task to scrape Google Reviews from business locations.
Args:
location_names: Optional list of location names to scrape (uses all locations if not provided)
Returns:
Dictionary with 'total' and 'reviews'
"""
logger.info("Starting scheduled Google Reviews scrape")
try:
service = CommentService()
result = service.scrape_google_reviews(location_names=location_names, save_to_db=True)
logger.info(f"Completed Google Reviews scrape. Total reviews: {len(result)}")
# Automatically queue analysis for pending comments
analyze_pending_comments.delay(limit=ANALYSIS_BATCH_SIZE)
logger.info("Queued analysis task for pending comments")
return {'total': len(result), 'reviews': result}
except Exception as e:
logger.error(f"Error in Google Reviews scrape task: {e}")
raise
# ============================================================================
# AI Analysis Tasks
# ============================================================================
@shared_task
def analyze_pending_comments(limit: int = 100):
"""
Scheduled task to analyze all pending (unanalyzed) comments.
Args:
limit: Maximum number of comments to analyze in one run
Returns:
Dictionary with analysis statistics
"""
if not getattr(settings, 'ANALYSIS_ENABLED', True):
logger.info("Comment analysis is disabled")
return {'success': False, 'message': 'Analysis disabled'}
logger.info("Starting scheduled comment analysis")
try:
service = AnalysisService()
results = service.analyze_pending_comments(limit=limit)
logger.info(f"Completed comment analysis. Results: {results}")
# Check if there are more pending comments and queue another batch if needed
from .models import SocialMediaComment
pending_count = SocialMediaComment.objects.filter(
ai_analysis__isnull=True
).count() + SocialMediaComment.objects.filter(
ai_analysis={}
).count()
# FIXED: Queue if ANY pending comments remain (not just >= batch size)
if pending_count > 0:
logger.info(f" - Found {pending_count} pending comments, queuing next batch")
# Use min() to ensure we don't exceed batch size
batch_size = min(pending_count, ANALYSIS_BATCH_SIZE)
analyze_pending_comments.delay(limit=batch_size)
return results
except Exception as e:
logger.error(f"Error in comment analysis task: {e}", exc_info=True)
raise
@shared_task
def analyze_recent_comments(hours: int = 24, limit: int = 100):
"""
Scheduled task to analyze comments scraped in the last N hours.
Args:
hours: Number of hours to look back
limit: Maximum number of comments to analyze
Returns:
Dictionary with analysis statistics
"""
if not getattr(settings, 'ANALYSIS_ENABLED', True):
logger.info("Comment analysis is disabled")
return {'success': False, 'message': 'Analysis disabled'}
logger.info(f"Starting analysis for comments from last {hours} hours")
try:
service = AnalysisService()
results = service.analyze_recent_comments(hours=hours, limit=limit)
logger.info(f"Completed recent comment analysis. Results: {results}")
return results
except Exception as e:
logger.error(f"Error in recent comment analysis task: {e}", exc_info=True)
raise
@shared_task
def analyze_platform_comments(platform: str, limit: int = 100):
"""
Scheduled task to analyze comments from a specific platform.
Args:
platform: Platform name (e.g., 'youtube', 'facebook', 'instagram')
limit: Maximum number of comments to analyze
Returns:
Dictionary with analysis statistics
"""
if not getattr(settings, 'ANALYSIS_ENABLED', True):
logger.info("Comment analysis is disabled")
return {'success': False, 'message': 'Analysis disabled'}
logger.info(f"Starting analysis for {platform} comments")
try:
service = AnalysisService()
results = service.analyze_comments_by_platform(platform=platform, limit=limit)
logger.info(f"Completed {platform} comment analysis. Results: {results}")
return results
except Exception as e:
logger.error(f"Error in {platform} comment analysis task: {e}", exc_info=True)
raise

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# Social Tasks - All Platform Tasks
# This module contains Celery tasks for all social platforms
# LinkedIn tasks
from .linkedin import sync_all_accounts_task, initial_historical_sync_task
# Google tasks
from .google import sync_all_accounts_periodic, sync_single_account
# Meta tasks (ONLY platform with webhooks!)
from .meta import meta_historical_backfill_task, process_webhook_comment_task, meta_poll_new_comments_task
# TikTok tasks
from .tiktok import extract_all_comments_task, poll_new_comments_task
# X (Twitter) tasks
from .x import sync_all_accounts_periodic, extract_all_replies_task
# YouTube tasks
from .youtube import poll_new_comments_task, deep_historical_backfill_task
__all__ = [
# LinkedIn
'initial_historical_sync_task',
'sync_all_accounts_task',
# Google
'sync_all_accounts_periodic',
'sync_single_account',
# Meta
'meta_historical_backfill_task',
'process_webhook_comment_task',
'meta_poll_new_comments_task',
# TikTok
'extract_all_comments_task',
'poll_new_comments_task',
# X
'extract_all_replies_task',
'sync_all_accounts_periodic',
# YouTube
'poll_new_comments_task',
'deep_historical_backfill_task',
]

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"""
Celery tasks for AI-powered comment analysis.
Coordinates between SocialComment model and OpenRouter service.
"""
import logging
from typing import Optional
from datetime import timedelta
from celery import shared_task
from django.conf import settings
from django.utils import timezone
from django.db.models import Q
from apps.social.models import SocialComment
from apps.social.services.ai_service import OpenRouterService
logger = logging.getLogger(__name__)
@shared_task(bind=True, max_retries=3)
def analyze_pending_comments_task(
self,
limit: Optional[int] = None,
platform_type: Optional[str] = None,
hours_ago: Optional[int] = None
):
"""
Analyze comments that haven't been analyzed yet (individually).
Args:
limit: Maximum number of comments to analyze
platform_type: Filter by platform type (LI, GO, FB, IG, TT, X, YT)
hours_ago: Only analyze comments added in the last N hours
"""
service = OpenRouterService()
if not service.is_configured():
logger.error("OpenRouter service not configured")
return {
'success': False,
'error': 'OpenRouter service not configured',
'analyzed': 0,
'failed': 0
}
# Build queryset for unanalyzed comments
queryset = SocialComment.objects.filter(
Q(ai_analysis__isnull=True) | Q(ai_analysis={})
).order_by('-created_at')
if platform_type:
queryset = queryset.filter(platform_type=platform_type)
if hours_ago:
cutoff_time = timezone.now() - timedelta(hours=hours_ago)
queryset = queryset.filter(added_at__gte=cutoff_time)
if limit:
queryset = queryset[:limit]
comments = list(queryset)
if not comments:
logger.info("No pending comments to analyze")
return {
'success': True,
'analyzed': 0,
'failed': 0,
'message': 'No pending comments to analyze'
}
logger.info(f"Found {len(comments)} pending comments to analyze")
analyzed_count = 0
failed_count = 0
# Analyze each comment individually
for comment in comments:
logger.info(f"Analyzing comment {comment.id} ({analyzed_count + 1}/{len(comments)})")
# Trigger analysis for this comment
result = service.analyze_comment(str(comment.id), comment.text)
if result.get('success'):
analysis = result.get('analysis', {})
# Build bilingual analysis structure
ai_analysis = {
'sentiment': analysis.get('sentiment', {}),
'summaries': analysis.get('summaries', {}),
'keywords': analysis.get('keywords', {}),
'topics': analysis.get('topics', {}),
'entities': analysis.get('entities', []),
'emotions': analysis.get('emotions', {}),
'metadata': {
**result.get('metadata', {}),
'analyzed_at': timezone.now().isoformat()
}
}
# Update comment with bilingual analysis
comment.ai_analysis = ai_analysis
comment.save()
analyzed_count += 1
logger.debug(f"Updated comment {comment.id} with bilingual analysis")
else:
error = result.get('error', 'Unknown error')
logger.error(f"Analysis failed for comment {comment.id}: {error}")
failed_count += 1
logger.info(
f"Analysis complete: {analyzed_count} analyzed, "
f"{failed_count} failed"
)
return {
'success': True,
'analyzed': analyzed_count,
'failed': failed_count,
'total': len(comments)
}
@shared_task
def analyze_comment_task(comment_id: int):
"""
Analyze a single comment.
Args:
comment_id: ID of the comment to analyze
"""
service = OpenRouterService()
if not service.is_configured():
logger.error("OpenRouter service not configured")
return {
'success': False,
'error': 'OpenRouter service not configured'
}
try:
comment = SocialComment.objects.get(id=comment_id)
except SocialComment.DoesNotExist:
logger.error(f"Comment {comment_id} not found")
return {
'success': False,
'error': f'Comment {comment_id} not found'
}
logger.info(f"Analyzing comment {comment_id}")
# Analyze single comment
result = service.analyze_comment(str(comment.id), comment.text)
if result.get('success'):
analysis = result.get('analysis', {})
# Build bilingual analysis structure
ai_analysis = {
'sentiment': analysis.get('sentiment', {}),
'summaries': analysis.get('summaries', {}),
'keywords': analysis.get('keywords', {}),
'topics': analysis.get('topics', {}),
'entities': analysis.get('entities', []),
'emotions': analysis.get('emotions', {}),
'metadata': {
**result.get('metadata', {}),
'analyzed_at': timezone.now().isoformat()
}
}
# Update comment with bilingual analysis
comment.ai_analysis = ai_analysis
comment.save()
sentiment_en = ai_analysis.get('sentiment', {}).get('classification', {}).get('en')
confidence_val = ai_analysis.get('sentiment', {}).get('confidence', 0)
logger.info(f"Comment {comment_id} analyzed successfully: {sentiment_en}")
return {
'success': True,
'comment_id': comment_id,
'sentiment': sentiment_en,
'confidence': float(confidence_val)
}
else:
error = result.get('error', 'Unknown error')
logger.error(f"Analysis failed for comment {comment_id}: {error}")
return {
'success': False,
'error': error
}
@shared_task
def reanalyze_comment_task(comment_id: int):
"""
Re-analyze a specific comment (overwrite existing analysis).
Args:
comment_id: ID of the comment to re-analyze
"""
logger.info(f"Re-analyzing comment {comment_id}")
return analyze_comment_task(comment_id)
@shared_task
def daily_unanalyzed_comments_task():
"""
Daily task to analyze any comments that haven't been analyzed (individually).
This is a backup mechanism to catch any comments that were missed.
Runs once per day and analyzes all comments with empty ai_analysis.
"""
logger.info("=" * 80)
logger.info("STARTING DAILY UNANALYZED COMMENTS CHECK")
logger.info("=" * 80)
service = OpenRouterService()
max_analyze = getattr(settings, 'DAILY_ANALYSIS_LIMIT', 100)
if not service.is_configured():
logger.error("OpenRouter service not configured, skipping daily analysis")
return {
'success': False,
'error': 'OpenRouter service not configured'
}
# Get unanalyzed comments
from django.db.models import Q
queryset = SocialComment.objects.filter(
Q(ai_analysis__isnull=True) | Q(ai_analysis={})
).order_by('-created_at')[:max_analyze]
comments = list(queryset)
if not comments:
logger.info("No unanalyzed comments found")
return {
'success': True,
'analyzed': 0,
'failed': 0,
'message': 'No unanalyzed comments found'
}
logger.info(f"Found {len(comments)} unanalyzed comments")
analyzed_count = 0
failed_count = 0
# Analyze each comment individually
for comment in comments:
logger.info(f"Daily analysis: Analyzing comment {comment.id} ({analyzed_count + 1}/{len(comments)})")
# Trigger analysis for this comment
result = service.analyze_comment(str(comment.id), comment.text)
if result.get('success'):
analysis = result.get('analysis', {})
# Build bilingual analysis structure
ai_analysis = {
'sentiment': analysis.get('sentiment', {}),
'summaries': analysis.get('summaries', {}),
'keywords': analysis.get('keywords', {}),
'topics': analysis.get('topics', {}),
'entities': analysis.get('entities', []),
'emotions': analysis.get('emotions', {}),
'metadata': {
**result.get('metadata', {}),
'analyzed_at': timezone.now().isoformat(),
'analysis_type': 'daily_check'
}
}
# Update comment with bilingual analysis
comment.ai_analysis = ai_analysis
comment.save()
analyzed_count += 1
logger.debug(f"Updated comment {comment.id} with daily analysis")
else:
error = result.get('error', 'Unknown error')
logger.error(f"Daily analysis failed for comment {comment.id}: {error}")
failed_count += 1
logger.info(
f"Daily analysis complete: {analyzed_count} analyzed, "
f"{failed_count} failed"
)
return {
'success': True,
'analyzed': analyzed_count,
'failed': failed_count,
'total': len(comments)
}

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from celery import shared_task
from django.utils import timezone
from django.utils.dateparse import parse_datetime
from django.db import transaction # <--- FIX: Added missing import
from apps.social.models import SocialAccount, SocialContent, SocialComment
from apps.social.services.google import GoogleBusinessService, GoogleAPIError
import logging
logger = logging.getLogger(__name__)
@shared_task
def sync_all_accounts_periodic():
# smartly run for both historical and delta sync based on last_synced_at
"""Runs Periodically (via Celery Beat) to pull new/updated reviews."""
logger.info("Starting Periodic Sync for Google Business...")
accounts = SocialAccount.objects.filter(platform_type='GO', is_active=True)
for account in accounts:
try:
sync_single_account.delay(account.id) # <--- FIX: Use .delay() for async
# Note: Updating last_synced_at inside the loop here is risky if .delay() hasn't finished yet.
# It's better to update it inside sync_single_account, but I kept your structure.
except Exception as e:
logger.error(f"Failed to trigger sync for account {account.id}: {e}")
@shared_task
def sync_single_account(account_id):
"""Background job to sync a specific account."""
try:
account = SocialAccount.objects.get(id=account_id, platform_type='GO')
locations_data = GoogleBusinessService.fetch_locations(account)
for loc_data in locations_data:
location, created = SocialContent.objects.get_or_create(
platform_type='GO',
content_id=loc_data['name'],
defaults={
'account': account,
'title': loc_data.get('title', 'Unknown Location'),
'text': "",
'content_data': loc_data
}
)
if created or location.last_comment_sync_at is None:
# Set to old date for Historical Sync
location.last_comment_sync_at = timezone.now() - timezone.timedelta(days=365 * 5)
location.save()
reviews_data = GoogleBusinessService.fetch_reviews_delta(account, location)
if not reviews_data:
continue
latest_time = location.last_comment_sync_at
with transaction.atomic(): # <--- This import is now fixed
for r_data in reviews_data:
review_time = _save_review(account, location, r_data)
if review_time and review_time > latest_time:
latest_time = review_time
location.last_comment_sync_at = latest_time
location.save()
# Update account sync time here to be accurate
account.last_synced_at = timezone.now()
account.save()
except SocialAccount.DoesNotExist:
logger.error(f"Account {account_id} not found")
except Exception as e:
logger.error(f"Error syncing account {account_id}: {e}")
raise
def _save_review(account, location, r_data):
"""Helper to save/update a single review matching the SocialComment model."""
review_id = r_data.get('name')
created_str = r_data.get('createTime')
update_str = r_data.get('updateTime')
created_at = timezone.now()
if created_str:
try:
dt = parse_datetime(created_str)
if dt: created_at = dt if dt.tzinfo else timezone.make_aware(dt)
except:
pass
update_time = None
if update_str:
try:
dt = parse_datetime(update_str)
if dt: update_time = dt if dt.tzinfo else timezone.make_aware(dt)
except:
pass
star_map = {'ONE': 1, 'TWO': 2, 'THREE': 3, 'FOUR': 4, 'FIVE': 5}
rating = star_map.get(r_data.get('starRating', 'ZERO'), 0)
reply_obj = r_data.get('reply')
reply_count = 1 if reply_obj else 0
comment_text = r_data.get('comment', '')
comment_data = r_data.copy()
comment_data['star_rating'] = rating
comment_data['reviewer_name'] = r_data.get('reviewer', {}).get('displayName', 'Anonymous')
SocialComment.objects.update_or_create(
platform_type='GO',
comment_id=review_id,
defaults={
'account': account,
'content': location,
'author_name': r_data.get('reviewer', {}).get('displayName', 'Anonymous'),
'author_id': r_data.get('reviewer', {}).get('displayName'),
'text': comment_text,
'created_at': created_at,
'rating': rating,
'reply_count': reply_count,
'comment_data': comment_data
}
)
return update_time

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from celery import shared_task
from django.utils import timezone
from django.db import transaction
from apps.social.models import SocialAccount, SocialContent, SocialComment
from apps.social.services.linkedin import LinkedInService, LinkedInAPIError
from apps.social.utils.linkedin import LinkedInConstants
import logging
import time
logger = logging.getLogger(__name__)
# ==========================================
# INITIAL HISTORICAL SYNC (One-Time)
# ==========================================
@shared_task(bind=True, max_retries=3)
def initial_historical_sync_task(self, account_id, max_comments_per_post=None):
"""
ONE-TIME initial historical sync when client first connects.
Fetches posts and limited historical comments to bootstrap the database.
"""
if max_comments_per_post is None:
max_comments_per_post = LinkedInConstants.INITIAL_SYNC_COMMENT_LIMIT
try:
account = SocialAccount.objects.select_for_update().get(
id=account_id,
platform_type='LI'
)
# Check token validity
if account.is_token_expired():
account.is_active = False
account.save()
logger.error(f"Account {account.name} token expired")
return
logger.info(f"Starting initial historical sync for {account.name}")
# Fetch all posts (returns full URNs)
posts_data = LinkedInService.fetch_posts(account, count=100)
logger.info(f"Found {len(posts_data)} posts for {account.name}")
total_comments_synced = 0
for idx, post_data in enumerate(posts_data, 1):
post_urn = post_data.get('id')
if not post_urn:
logger.warning(f"Post missing ID, skipping: {post_data}")
continue
# Extract post data
commentary = post_data.get('commentary', '')
created_time_ms = post_data.get('created', {}).get('time')
created_at = LinkedInService._parse_timestamp(created_time_ms)
# Create or update post
with transaction.atomic():
post, created = SocialContent.objects.get_or_create(
platform_type='LI',
content_id=post_urn,
defaults={
'account': account,
'text': commentary,
'created_at': created_at,
'content_data': post_data,
'last_comment_sync_at': None
}
)
if not created and post.last_comment_sync_at:
logger.info(f"Post {post_urn} already synced, skipping")
continue
logger.info(f"Processing post {idx}/{len(posts_data)}: {post_urn}")
# Fetch LIMITED historical comments using full URN
comments_data = LinkedInService.fetch_comments_limited(
account,
post_urn,
limit=max_comments_per_post
)
logger.info(f"Found {len(comments_data)} comments for post {post_urn}")
latest_comment_time = created_at
# Save comments in batch
with transaction.atomic():
for c_data in comments_data:
comment_id = c_data.get('id')
if not comment_id:
continue
c_time_ms = c_data.get('created', {}).get('time')
c_time = LinkedInService._parse_timestamp(c_time_ms)
actor = c_data.get('actor', 'Unknown')
message_text = c_data.get('message', {}).get('text', '')
SocialComment.objects.update_or_create(
platform_type='LI',
comment_id=comment_id,
defaults={
'account': account,
'content': post,
'author_name': actor,
'author_id': actor,
'text': message_text,
'created_at': c_time,
'synced_via_webhook': False,
'comment_data': c_data
}
)
if c_time > latest_comment_time:
latest_comment_time = c_time
total_comments_synced += 1
# Update post's last sync timestamp
post.last_comment_sync_at = latest_comment_time
post.save()
# Rate limit protection
time.sleep(0.5)
# Mark account as synced
account.last_synced_at = timezone.now()
account.save()
logger.info(
f"✅ Initial historical sync complete for {account.name}: "
f"{len(posts_data)} posts, {total_comments_synced} comments"
)
except SocialAccount.DoesNotExist:
logger.error(f"Account {account_id} not found")
except Exception as e:
logger.error(f"Initial historical sync error for account {account_id}: {e}")
# Retry with exponential backoff
raise self.retry(exc=e, countdown=60 * (2 ** self.request.retries))
# ==========================================
# DELTA SYNC FOR NEW COMMENTS (Scheduled)
# ==========================================
@shared_task(bind=True, max_retries=3)
def sync_new_comments_task(self, account_id):
"""
DELTA SYNC for fetching only NEW comments since last sync.
"""
try:
account = SocialAccount.objects.select_for_update().get(
id=account_id,
platform_type='LI'
)
# Check token validity
if account.is_token_expired():
account.is_active = False
account.save()
logger.error(f"Account {account.name} token expired")
return
# Check if account has been initially synced
if not account.last_synced_at:
logger.warning(
f"Account {account.name} not initially synced. "
f"Run initial_historical_sync_task first."
)
return
logger.info(f"Starting delta sync for new comments: {account.name}")
# Fetch posts
posts_data = LinkedInService.fetch_posts(account, count=100)
total_new_comments = 0
for post_data in posts_data:
post_urn = post_data.get('id')
if not post_urn:
continue
# Get or create post record
try:
post = SocialContent.objects.get(
platform_type='LI',
content_id=post_urn
)
except SocialContent.DoesNotExist:
# This post wasn't in initial sync, create it
commentary = post_data.get('commentary', '')
created_time_ms = post_data.get('created', {}).get('time')
created_at = LinkedInService._parse_timestamp(created_time_ms)
post = SocialContent.objects.create(
platform_type='LI',
content_id=post_urn,
account=account,
text=commentary,
created_at=created_at,
content_data=post_data,
last_comment_sync_at=None
)
# Fetch only NEW comments since last sync using full URN
since_timestamp = post.last_comment_sync_at
comments_data = LinkedInService.fetch_comments_delta(
account,
post_urn,
since_timestamp=since_timestamp
)
if not comments_data:
continue
logger.info(f"Found {len(comments_data)} new comments for post {post_urn}")
latest_comment_time = since_timestamp or timezone.now()
# Save new comments
with transaction.atomic():
for c_data in comments_data:
comment_id = c_data.get('id')
if not comment_id:
continue
c_time_ms = c_data.get('created', {}).get('time')
c_time = LinkedInService._parse_timestamp(c_time_ms)
actor = c_data.get('actor', 'Unknown')
message_text = c_data.get('message', {}).get('text', '')
comment, created = SocialComment.objects.update_or_create(
platform_type='LI',
comment_id=comment_id,
defaults={
'account': account,
'content': post,
'author_name': actor,
'author_id': actor,
'text': message_text,
'created_at': c_time,
'synced_via_webhook': False,
'comment_data': c_data
}
)
if created:
total_new_comments += 1
if c_time > latest_comment_time:
latest_comment_time = c_time
# Update sync timestamp
if latest_comment_time > (post.last_comment_sync_at or timezone.make_aware(timezone.datetime(1970, 1, 1))):
post.last_comment_sync_at = latest_comment_time
post.save()
time.sleep(0.3)
# Update account sync time
account.last_synced_at = timezone.now()
account.save()
logger.info(
f"✅ Delta sync complete for {account.name}: "
f"{total_new_comments} new comments"
)
except SocialAccount.DoesNotExist:
logger.error(f"Account {account_id} not found")
except Exception as e:
logger.error(f"Delta sync error for account {account_id}: {e}")
raise self.retry(exc=e, countdown=60 * (2 ** self.request.retries))
# ==========================================
# WEBHOOK PROCESSING (Real-Time)
# ==========================================
@shared_task(bind=True, max_retries=3)
def process_webhook_comment_task(self, account_id, post_urn, comment_id):
"""
Process a single comment triggered by webhook (REAL-TIME sync).
post_urn: Full URN expected (e.g., urn:li:share:123)
"""
try:
account = SocialAccount.objects.get(id=account_id, platform_type='LI')
logger.info(f"Processing webhook comment {comment_id} for post {post_urn}")
# Fetch the specific comment (most efficient)
comment_data = LinkedInService.fetch_single_comment(account, post_urn, comment_id)
if not comment_data:
logger.warning(
f"Could not fetch comment {comment_id} for post {post_urn}. "
f"It may have been deleted."
)
return
# Get or create the post
try:
content = SocialContent.objects.get(
platform_type='LI',
content_id=post_urn
)
except SocialContent.DoesNotExist:
logger.warning(f"Post {post_urn} not found in database, creating it")
# Post doesn't exist, we need to fetch it
posts_data = LinkedInService.fetch_posts(account, count=1)
matching_post = next((p for p in posts_data if p.get('id') == post_urn), None)
if matching_post:
commentary = matching_post.get('commentary', '')
created_time_ms = matching_post.get('created', {}).get('time')
created_at = LinkedInService._parse_timestamp(created_time_ms)
content = SocialContent.objects.create(
platform_type='LI',
content_id=post_urn,
account=account,
text=commentary,
created_at=created_at,
content_data=matching_post
)
else:
logger.error(f"Could not fetch post {post_urn} from API")
return
# Parse comment data
c_time_ms = comment_data.get('created', {}).get('time')
c_time = LinkedInService._parse_timestamp(c_time_ms)
actor = comment_data.get('actor', 'Unknown')
message_text = comment_data.get('message', {}).get('text', '')
# Save or update comment
with transaction.atomic():
comment, created = SocialComment.objects.update_or_create(
platform_type='LI',
comment_id=comment_id,
defaults={
'account': account,
'content': content,
'author_name': actor,
'author_id': actor,
'text': message_text,
'created_at': c_time,
'synced_via_webhook': True,
'comment_data': comment_data
}
)
# Update post's last comment sync time
if c_time > (content.last_comment_sync_at or timezone.make_aware(timezone.datetime(1970, 1, 1))):
content.last_comment_sync_at = c_time
content.save()
action = "Created" if created else "Updated"
logger.info(f"✅ {action} comment {comment_id} via webhook")
except SocialAccount.DoesNotExist:
logger.error(f"Account {account_id} not found for webhook processing")
except Exception as e:
logger.error(f"Webhook processing error: {e}")
raise self.retry(exc=e, countdown=30 * (2 ** self.request.retries))
# ==========================================
# BULK OPERATIONS
# ==========================================
@shared_task
def sync_all_accounts_task():
"""
Wrapper to sync ALL active accounts (for scheduled Celery Beat job).
Schedule this with Celery Beat to run every 15-30 minutes.
"""
accounts = SocialAccount.objects.filter(
platform_type='LI',
is_active=True,
last_synced_at__isnull=False # Only sync accounts that have been initially synced
)
logger.info(f"Starting delta sync for {accounts.count()} LinkedIn accounts")
for account in accounts:
# Queue each account sync as separate task
sync_new_comments_task.delay(account.id)
logger.info(f"Queued delta sync tasks for {accounts.count()} accounts")
# @shared_task
# def cleanup_old_comments_task(days_to_keep=90):
# """Optional: Clean up very old comments to save database space."""
# cutoff_date = timezone.now() - timezone.timedelta(days=days_to_keep)
# deleted_count, _ = SocialComment.objects.filter(
# platform_type='LI',
# created_at__lt=cutoff_date
# ).delete()
# logger.info(f"Cleaned up {deleted_count} LinkedIn comments older than {days_to_keep} days")

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# social/tasks/meta.py
from celery import shared_task
from django.utils import timezone
from django.utils.dateparse import parse_datetime
from apps.social.models import SocialAccount, SocialContent, SocialComment
from apps.social.services.meta import MetaService, MetaAPIError
import logging
logger = logging.getLogger(__name__)
def parse_meta_timestamp(ts):
if not ts:
return timezone.now()
try:
dt = parse_datetime(ts)
return dt if dt else timezone.now()
except:
return timezone.now()
# ============================================================================
# TASK 1: HISTORICAL BACKFILL (Deep Sync)
# ============================================================================
# Use this when connecting a NEW account to get ALL past history.
# ============================================================================
@shared_task(bind=True)
def meta_historical_backfill_task(self, account_id):
"""
Deep Sync: Fetches ALL Posts and ALL Comments.
Used for "First Run" or "Full Resync".
"""
try:
account = SocialAccount.objects.get(id=account_id)
if account.platform_type not in ['META', 'FB', 'IG']:
return
if account.is_token_expired():
account.is_active = False
account.save()
return
logger.info(f"Starting DEEP HISTORICAL BACKFILL for {account.name}...")
# 1. DISCOVERY
entities = MetaService.discover_pages_and_ig(account.access_token)
# 2. FALLBACK
if not entities:
logger.info("No linked business accounts found. Backfilling User Profile.")
entities = [{
'platform': 'FB',
'native_id': 'me',
'name': account.name,
'access_token': account.access_token,
'is_permanent': False
}]
# 3. SYNC ENTITIES (Fetch ALL Posts, then ALL Comments)
total_posts_synced = 0
total_comments_synced = 0
for entity in entities:
platform = entity['platform']
entity_id = entity['native_id']
entity_token = entity['access_token']
logger.info(f"Backfilling entity: {entity['name']} ({platform})")
# 1. Fetch ALL Posts (No timestamp filter)
posts_data = MetaService.fetch_posts(entity_id, entity_token, platform)
logger.info(f" -> Found {len(posts_data)} posts")
for post in posts_data:
post_id = post['id']
# FB uses 'created_time', IG uses 'timestamp'
post_timestamp = post.get('timestamp') if platform == 'IG' else post.get('created_time')
# 2. Save Content
content_obj, created = SocialContent.objects.update_or_create(
platform_type='META',
content_id=post_id,
defaults={
'account': account,
'source_platform': platform,
'text': post.get('message') or post.get('caption', ''),
'created_at': parse_meta_timestamp(post_timestamp),
'content_data': post
}
)
if created:
total_posts_synced += 1
# Save Entity Token for Replies
if entity_id != 'me':
if 'access_token' not in content_obj.content_data:
content_obj.content_data['access_token'] = entity_token
content_obj.save()
# 3. Fetch ALL Comments for this post (No 'since' parameter)
# This ensures we get history
comments = MetaService.fetch_comments_for_post(post_id, entity_token, since_timestamp=None)
for c in comments:
c_id = c['id']
# CRITICAL: Check for duplicates
if not SocialComment.objects.filter(comment_id=c_id).exists():
author = c.get('from', {})
author_name = author.get('name') or author.get('username') or 'User'
comment_text = c.get('message') or c.get('text') or ''
SocialComment.objects.create(
account=account,
content=content_obj,
platform_type='META',
source_platform=platform,
comment_id=c_id,
author_name=author_name,
author_id=author.get('id') if isinstance(author, dict) else '',
text=comment_text,
created_at=parse_meta_timestamp(c.get('created_time')),
comment_data=c,
like_count=c.get('like_count', 0)
)
total_comments_synced += 1
# Update content bookmark to latest comment found
if content_obj.last_comment_sync_at is None or parse_meta_timestamp(c.get('created_time')) > content_obj.last_comment_sync_at:
content_obj.last_comment_sync_at = parse_meta_timestamp(c.get('created_time'))
content_obj.save()
account.last_synced_at = timezone.now()
account.save()
logger.info(f"Deep Backfill Complete: {account.name}. Posts: {total_posts_synced}, Comments: {total_comments_synced}")
except Exception as e:
logger.error(f"Backfill Failed for {account_id}: {e}", exc_info=True)
raise self.retry(exc=e, countdown=60)
# ============================================================================
# TASK 2: POLL NEW COMMENTS (Delta Sync)
# ============================================================================
# Runs automatically via Celery Beat.
# Iterates existing posts to find NEW comments only.
# Mirrors your YouTube poll_new_comments_task logic.
# ============================================================================
@shared_task
def meta_poll_new_comments_task():
"""
FAST POLLING (Delta Sync).
Runs automatically via Celery Beat.
Fetches all new comments that don't exist in database.
"""
accounts = SocialAccount.objects.filter(platform_type__in=['META'], is_active=True)
for account in accounts:
try:
logger.info(f"Polling comments for {account.name}")
# 1. Iterate through existing posts in database (Like YouTube)
# We only check posts we already know about.
videos = SocialContent.objects.filter(platform_type='META', account=account)
for video in videos:
try:
# 2. Fetch comments using timestamp filter
since_ts = video.last_comment_sync_at
token_to_use = video.content_data.get('access_token', account.access_token)
comments = MetaService.fetch_comments_for_post(video.content_id, token_to_use, since_ts)
if not comments:
continue
new_comments_count = 0
latest_comment_time = video.last_comment_sync_at
# 3. Check for duplicates using ID
for c in comments:
c_id = c['id']
if SocialComment.objects.filter(comment_id=c_id).exists():
# Skip existing comments
continue
# Parse Data
c_time = parse_meta_timestamp(c.get('created_time'))
author = c.get('from', {})
author_name = author.get('name') or author.get('username') or 'User'
comment_text = c.get('message') or c.get('text') or ''
# Save new comment
SocialComment.objects.create(
account=account,
content=video,
platform_type='META',
source_platform=video.source_platform, # Inherit from content
comment_id=c_id,
author_name=author_name,
author_id=author.get('id') if isinstance(author, dict) else '',
text=comment_text,
created_at=c_time,
comment_data=c,
like_count=c.get('like_count', 0)
)
new_comments_count += 1
# Track latest comment time for bookmarking
if c_time > latest_comment_time:
latest_comment_time = c_time
# 4. Update Content Bookmark
if new_comments_count > 0:
logger.info(f"Found {new_comments_count} new comments for post {video.content_id}")
video.last_comment_sync_at = latest_comment_time
video.save()
except Exception as e:
logger.error(f"Error syncing post {video.content_id}: {e}")
account.last_synced_at = timezone.now()
account.save()
except Exception as e:
logger.error(f"Polling Error for account {account.name}: {e}")
# ============================================================================
# TASK 3: WEBHOOK (Realtime)
# ============================================================================
@shared_task(bind=True)
def process_webhook_comment_task(self, comment_id, page_native_id, account_id):
"""
WEBHOOK TASK
Handles real-time updates.
"""
try:
# 1. Get Account
account = SocialAccount.objects.get(id=account_id)
# 2. Find specific Page Token
entities = MetaService.discover_pages_and_ig(account.access_token)
page_token = account.access_token
for entity in entities:
if entity['native_id'] == page_native_id and entity['platform'] == 'FB':
page_token = entity['access_token']
break
# 3. Fetch Comment
data = MetaService.fetch_single_comment(comment_id, page_token)
post_obj = data.get('post')
post_id = post_obj.get('id') if isinstance(post_obj, dict) else post_obj
author = data.get('from', {})
# 4. Determine Source
source_platform = 'FB'
if isinstance(author, dict) and isinstance(author.get('id'), str) and len(str(author.get('id'))) > 15:
source_platform = 'IG'
# 5. Get or Create Content
content, _ = SocialContent.objects.get_or_create(
platform_type='META',
content_id=post_id,
defaults={
'account': account,
'text': '',
'created_at': timezone.now(),
'content_data': {},
'source_platform': source_platform
}
)
# 6. Save Comment (Update_or_create allows safe re-runs)
comment_text = data.get('message') or data.get('text') or ''
author_name = author.get('name') or author.get('username') or 'User'
SocialComment.objects.update_or_create(
platform_type='META',
comment_id=data['id'],
defaults={
'account': account,
'content': content,
'source_platform': source_platform,
'author_name': author_name,
'author_id': author.get('id'),
'text': comment_text,
'created_at': parse_meta_timestamp(data.get('created_time')),
'synced_via_webhook': True,
'comment_data': data
}
)
except SocialAccount.DoesNotExist:
logger.error(f"Account {account_id} not found for Webhook")
except Exception as e:
logger.error(f"Webhook Task Failed: {e}", exc_info=True)

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# social/tasks/tiktok.py
from celery import shared_task
from django.utils import timezone
from apps.social.models import SocialAccount, SocialContent, SocialComment
from apps.social.services.tiktok import TikTokService, TikTokAPIError
import logging
import time
logger = logging.getLogger(__name__)
def parse_tiktok_timestamp(ts):
"""Convert TikTok timestamp (seconds) to datetime."""
if not ts:
return timezone.now()
# TikTok timestamps are usually seconds, check if milliseconds
if ts > 1000000000000:
ts = ts / 1000
return timezone.make_aware(timezone.datetime.utcfromtimestamp(ts))
@shared_task(bind=True)
def extract_all_comments_task(self, account_id):
"""
FULL SYNC: Fetches ALL Ads and ALL Comments.
Used for initial connection or monthly backfill.
"""
try:
account = SocialAccount.objects.get(id=account_id, platform_type='TT')
if not account.is_active:
return
logger.info(f"Starting FULL TikTok Ads sync for {account.name}")
page = 1
has_more_ads = True
while has_more_ads:
try:
# 1. Fetch Ads (Content)
data = TikTokService.fetch_ads(account, page=page)
ads = data.get('list', [])
# Check pagination
page_info = data.get('page_info', {})
total_ads = page_info.get('total', 0)
has_more_ads = (page * 20) < total_ads
page += 1
time.sleep(1)
for ad_data in ads:
ad_id = str(ad_data['ad_id'])
# Store Ad as SocialContent
content, _ = SocialContent.objects.update_or_create(
platform_type='TT',
content_id=ad_id,
defaults={
'account': account,
'text': ad_data.get('ad_name', ''),
'created_at': parse_tiktok_timestamp(ad_data.get('create_time')),
'content_data': ad_data
}
)
# 2. Fetch ALL Comments for this Ad
_sync_comments_for_content(account, content, ad_id, full_sync=True)
except TikTokAPIError as e:
logger.error(f"Error syncing page {page}: {e}")
break
except Exception as e:
logger.error(f"Full Sync Critical Error: {e}")
@shared_task
def poll_new_comments_task():
"""
DELTA SYNC: Fetches comments but stops when existing comments are found.
Run frequently (e.g., every 15 mins) via Celery Beat.
"""
accounts = SocialAccount.objects.filter(platform_type='TT', is_active=True)
for account in accounts:
# Fetch existing Ads from our DB (we don't re-fetch Ad list every poll to save API calls)
contents = SocialContent.objects.filter(account=account, platform_type='TT')
for content in contents:
try:
# Sync comments, but stop early if we hit known data
_sync_comments_for_content(account, content, content.content_id, full_sync=False)
except TikTokAPIError as e:
logger.error(f"Polling Error for Ad {content.content_id}: {e}")
def _sync_comments_for_content(account, content, ad_id, full_sync=True):
"""
Helper function to sync comments for a specific Ad.
Args:
full_sync (bool):
True -> Fetch all pages (History).
False -> Stop fetching pages once we find a comment we already have (Delta).
"""
c_page = 1
has_more_comments = True
while has_more_comments:
c_data = TikTokService.fetch_comments_for_ad(account, ad_id, page=c_page)
comments = c_data.get('list', [])
# Check pagination
page_info = c_data.get('page_info', {})
total_comments = page_info.get('total', 0)
has_more_comments = (c_page * 20) < total_comments
c_page += 1
if not comments:
break
should_stop = False
for comm in comments:
comm_id = comm.get('comment_id')
# DELTA LOGIC: If doing a poll (not full sync), check if exists
if not full_sync:
exists = SocialComment.objects.filter(
platform_type='TT',
comment_id=comm_id
).exists()
if exists:
# We found a comment we already have.
# API returns oldest -> newest usually, so we can stop.
should_stop = True
break
# Create or Update
comm_time = parse_tiktok_timestamp(comm.get('create_time'))
SocialComment.objects.update_or_create(
platform_type='TT',
comment_id=comm_id,
defaults={
'account': account,
'content': content,
'author_name': comm.get('user_name', 'Unknown'),
'text': comm.get('text', ''),
'created_at': comm_time,
'comment_data': comm
}
)
if should_stop:
break
time.sleep(0.5) # Small delay to respect rate limits

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# xcom/tasks.py
from celery import shared_task
from django.utils import timezone
from django.utils.dateparse import parse_datetime
from apps.social.models import SocialAccount, SocialContent, SocialComment
from apps.social.services.x import XService, XAPIError, XRateLimitError
import logging
logger = logging.getLogger(__name__)
def parse_x_timestamp(ts):
if not ts: return timezone.now()
try:
dt = parse_datetime(ts)
return dt if dt else timezone.now()
except:
return timezone.now()
@shared_task
def sync_all_accounts_periodic():
logger.info("Starting Periodic Polling for X (Twitter)...")
accounts = SocialAccount.objects.filter(platform_type='X', is_active=True)
for account in accounts:
try:
poll_new_replies_task.delay(account.id)
except Exception as e:
logger.error(f"Failed to trigger sync for account {account.id}: {e}")
@shared_task(bind=True, max_retries=6) # Allow retries for rate limits
def poll_new_replies_task(self, account_id):
"""
DELTA SYNC TASK with intelligent Rate Limit handling.
"""
try:
account = SocialAccount.objects.get(id=account_id, platform_type='X')
tweets = SocialContent.objects.filter(platform_type='X', account=account)
for tweet in tweets:
if not tweet.last_comment_sync_at:
continue # Skip unsynced tweets
try:
replies_data = XService.fetch_tweet_replies(
account,
tweet.content_id,
since_datetime=tweet.last_comment_sync_at,
owner_id=account.platform_id
)
except XRateLimitError as e:
# CRITICAL: Don't block. Retry later using ETA (Estimated Time of Arrival)
# Add a buffer of 60 seconds to ensure limit is reset
retry_time = timezone.make_aware(datetime.datetime.fromtimestamp(e.reset_at))
wait_seconds = (retry_time - timezone.now()).total_seconds() + 60
logger.warning(f"Rate limit hit. Retrying in {wait_seconds} seconds.")
raise self.retry(exc=XAPIError("Rate limit triggered"), countdown=wait_seconds)
if not replies_data:
continue
newest_timestamp_found = tweet.last_comment_sync_at
for r_data in replies_data:
# r_data['author'] is now populated by _attach_expansions
author = r_data.get('author')
if not author:
# Fallback if expansion failed (shouldn't happen)
continue
r_time = parse_x_timestamp(r_data['created_at'])
if r_time <= tweet.last_comment_sync_at:
continue
SocialComment.objects.update_or_create(
platform_type='X',
comment_id=r_data['id'],
defaults={
'account': account,
'content': tweet,
'text': r_data.get('text', ''),
'author_id': author.get('id'),
'author_name': author.get('username', 'Unknown'), # Now correctly fetched
'created_at': r_time,
'comment_data': r_data
}
)
if r_time > newest_timestamp_found:
newest_timestamp_found = r_time
if newest_timestamp_found > tweet.last_comment_sync_at:
tweet.last_comment_sync_at = newest_timestamp_found
tweet.save()
account.last_synced_at = timezone.now()
account.save()
except SocialAccount.DoesNotExist:
logger.error(f"Account {account_id} not found")
except XRateLimitError:
# Raise it again if we haven't retried enough times
raise
except Exception as e:
logger.error(f"Polling Error for account {account_id}: {e}")
@shared_task(bind=True)
def extract_all_replies_task(self, account_id):
"""
MANUAL FULL SYNC (Backfill)
"""
try:
account = SocialAccount.objects.get(id=account_id, platform_type='X')
tweets_data = XService.get_user_tweets(account)
if not tweets_data:
logger.info(f"No tweets found for account {account.name}")
return
for t_data in tweets_data:
tweet_id = t_data['id']
tweet, created = SocialContent.objects.get_or_create(
platform_type='X',
content_id=tweet_id,
defaults={
'account': account,
'text': t_data.get('text', ''),
'created_at': parse_x_timestamp(t_data['created_at']),
'content_data': t_data
}
)
# Rate limit handling for fetch inside backfill
try:
replies_data = XService.fetch_tweet_replies(
account,
tweet_id,
since_datetime=None,
owner_id=account.platform_id
)
except XRateLimitError as e:
# Retry backfill later
retry_time = timezone.make_aware(datetime.datetime.fromtimestamp(e.reset_at))
wait_seconds = (retry_time - timezone.now()).total_seconds() + 60
raise self.retry(exc=XAPIError("Rate limit triggered"), countdown=wait_seconds)
if not replies_data:
continue
for r_data in replies_data:
author = r_data.get('author')
if not author: continue
SocialComment.objects.update_or_create(
platform_type='X',
comment_id=r_data['id'],
defaults={
'account': account,
'content': tweet,
'text': r_data.get('text', ''),
'author_id': author.get('id'),
'author_name': author.get('username', 'Unknown'),
'created_at': parse_x_timestamp(r_data['created_at']),
'comment_data': r_data
}
)
if created:
tweet.last_comment_sync_at = tweet.created_at
tweet.save()
account.last_synced_at = timezone.now()
account.save()
except SocialAccount.DoesNotExist:
logger.error(f"Account {account_id} not found")
except XRateLimitError:
raise
except Exception as e:
logger.error(f"Backfill Error for account {account_id}: {e}")

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# youtube/tasks.py
from celery import shared_task
from django.utils import timezone
import datetime
from django.utils.dateparse import parse_datetime
from apps.social.models import SocialAccount, SocialContent, SocialComment
from apps.social.services.youtube import YouTubeService, YouTubeAPIError
import logging
logger = logging.getLogger(__name__)
def parse_youtube_timestamp(ts):
if not ts:
return timezone.now()
try:
dt = parse_datetime(ts)
return dt if dt else timezone.now()
except:
return timezone.now()
# social/tasks/youtube.py
@shared_task
def poll_new_comments_task():
"""
FAST POLLING (Delta Sync).
"""
accounts = SocialAccount.objects.filter(platform_type='YT', is_active=True)
for account in accounts:
try:
logger.info(f"Polling comments for {account.name}")
# Iterate through videos to check for new comments
videos = SocialContent.objects.filter(platform_type='YT', account=account)
if not videos.exists():
logger.info(f"No videos found in DB for {account.name}. Skipping poll.")
continue
logger.info(f"Scanning {videos.count()} videos for new comments...")
for video in videos:
try:
# Fetch comments
comments = YouTubeService.fetch_video_comments(account, video.content_id)
if not comments:
continue
# Log what we are doing
logger.info(f"Checking video {video.content_id} for new comments...")
new_comments_count = 0
latest_comment_time = video.last_comment_sync_at
for item in comments:
top = item['snippet']['topLevelComment']
c_id = top['id']
# Check if comment already exists in database
if SocialComment.objects.filter(platform_type='YT', comment_id=c_id).exists():
logger.debug(f"Skipping existing comment {c_id}")
continue
# Parse Time
pub_str = top['snippet']['publishedAt']
c_time = parse_youtube_timestamp(pub_str)
# Save new comment
SocialComment.objects.create(
platform_type='YT',
comment_id=c_id,
account=account,
content=video,
author_name=top['snippet'].get('authorDisplayName', 'User'),
author_id=top['snippet'].get('authorChannelId', {}).get('value'),
text=top['snippet'].get('textDisplay', ''),
created_at=c_time,
comment_data=item
)
new_comments_count += 1
# Track latest comment time
if c_time > latest_comment_time:
latest_comment_time = c_time
# Update video's last sync timestamp if we found new comments
if new_comments_count > 0:
logger.info(f"Found {new_comments_count} new comments for video {video.content_id}")
video.last_comment_sync_at = latest_comment_time
video.save()
else:
logger.debug(f"No new comments for video {video.content_id}")
except Exception as e:
logger.error(f"Error syncing video {video.content_id}: {e}")
account.last_synced_at = timezone.now()
account.save()
except Exception as e:
logger.error(f"Polling Error for account {account.name}: {e}")
@shared_task
def deep_historical_backfill_task(account_id):
"""
DEEP SYNC.
Fetches ALL videos and ALL comments.
Used for "First Run".
"""
try:
account = SocialAccount.objects.get(id=account_id, platform_type='YT')
logger.info(f"Starting Deep Backfill for {account.name}")
# 1. Get Videos
videos_data = YouTubeService.fetch_user_videos(account)
for vid_data in videos_data:
vid_id = vid_data['id']
video, created = SocialContent.objects.get_or_create(
platform_type='YT',
content_id=vid_id,
defaults={
'account': account,
'title': vid_data.get('snippet', {}).get('title', ''),
'text': vid_data.get('snippet', {}).get('description', ''),
'created_at': parse_youtube_timestamp(vid_data.get('snippet', {}).get('publishedAt')),
'content_data': vid_data
}
)
# 2. Get All Comments for this video
comments = YouTubeService.fetch_video_comments(account, vid_id)
for c_data in comments:
top = c_data['snippet']['topLevelComment']
SocialComment.objects.update_or_create(
platform_type='YT',
comment_id=c_data['id'],
defaults={
'account': account,
'content': video,
'author_name': top['snippet'].get('authorDisplayName', 'User'),
'author_id': top['snippet'].get('authorChannelId', {}).get('value'),
'text': top['snippet'].get('textDisplay', ''),
'created_at': parse_youtube_timestamp(top['snippet'].get('publishedAt')),
'comment_data': c_data
}
)
if parse_youtube_timestamp(top['snippet'].get('publishedAt')) > video.last_comment_sync_at:
video.last_comment_sync_at = parse_youtube_timestamp(top['snippet'].get('publishedAt'))
video.save()
except Exception as e:
logger.error(f"Backfill Error: {e}")

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{% extends 'layouts/base.html' %}
{% load social_filters %}
{% load social_icons %}
{% load action_icons %}
{% load star_rating %}
{% block title %}Comment Detail - {{ platform_name }}{% endblock %}
{% block page_title %}Comment Details{% endblock %}
{% block page_subtitle %}View detailed analysis from {{ platform_name }}{% endblock %}
{% block content %}
<!-- Header Actions -->
<div class="d-flex justify-content-between align-items-center mb-4">
<a href="{% url 'social:comments_list' platform_type %}" class="btn btn-outline-secondary">
<i class="bi bi-arrow-left me-2"></i>
Back to Comments
</a>
<div class="d-flex align-items-center gap-2">
<div class="text-primary">
{% social_icon platform_type size=24 %}
</div>
<span class="fw-bold">{{ platform_name }}</span>
</div>
</div>
<div class="row g-4">
<!-- Main Content -->
<div class="col-lg-8">
<div class="d-flex flex-column gap-4">
<!-- Main Comment Card -->
<div class="card">
<div class="card-body">
<div class="d-flex gap-4 mb-4">
<div class="avatar avatar-xl bg-primary flex-shrink-0 position-relative">
{{ comment.author_name|slice:":1"|upper }}
<span class="position-absolute bottom-0 end-0 bg-success border border-white rounded-circle" style="width: 12px; height: 12px;"></span>
</div>
<div class="flex-grow-1">
<div class="d-flex justify-content-between align-items-start mb-2">
<div>
<h3 class="h4 mb-1">{{ comment.author_name }}</h3>
<div class="d-flex gap-2 flex-wrap">
{% if comment.synced_via_webhook %}
<span class="badge bg-success">
<i class="bi bi-bolt-fill me-1"></i> Real-time
</span>
{% endif %}
{% if comment.ai_analysis %}
<span class="badge bg-primary">
<i class="bi bi-cpu me-1"></i> AI Analyzed
</span>
{% endif %}
{% if comment.rating %}
<span class="badge bg-warning text-dark">
<i class="bi bi-star-fill me-1"></i> {{ comment.rating }}/5
</span>
{% endif %}
</div>
</div>
<div class="text-end">
<small class="text-muted">
<i class="bi bi-calendar me-1"></i> {{ comment.created_at|date:"M d, Y H:i" }}
</small>
</div>
</div>
</div>
</div>
<div class="bg-light rounded p-4 mb-4">
<p class="mb-0">{{ comment.text }}</p>
</div>
<!-- Media -->
{% if comment.media_url %}
<div class="rounded overflow-hidden mb-4">
<img src="{{ comment.media_url }}" alt="Comment media" class="img-fluid">
</div>
{% endif %}
<!-- Engagement Stats -->
<div class="d-flex gap-3 pt-3 border-top">
<div class="d-flex align-items-center gap-3 px-4 py-3 bg-white rounded shadow-sm flex-fill">
<i class="bi bi-heart-fill text-danger fs-4"></i>
<div>
<div class="fs-4 fw-bold">{{ comment.like_count }}</div>
<div class="small text-muted fw-bold">Likes</div>
</div>
</div>
<div class="d-flex align-items-center gap-3 px-4 py-3 bg-white rounded shadow-sm flex-fill">
<i class="bi bi-chat-fill text-primary fs-4"></i>
<div>
<div class="fs-4 fw-bold">{{ comment.reply_count }}</div>
<div class="small text-muted fw-bold">Replies</div>
</div>
</div>
<div class="d-flex align-items-center gap-3 px-4 py-3 bg-white rounded shadow-sm flex-fill">
<i class="bi bi-fingerprint text-muted fs-4"></i>
<div>
<div class="small fw-bold text-monospace">{{ comment.comment_id|truncatechars:12 }}</div>
<div class="small text-muted fw-bold">ID</div>
</div>
</div>
</div>
</div>
</div>
<!-- Replies Section -->
{% if replies %}
<div class="card">
<div class="card-header">
<h5 class="mb-0">
<i class="bi bi-chat-dots me-2"></i>
Replies ({{ replies|length }})
</h5>
</div>
<div class="card-body">
<div class="d-flex flex-column gap-3">
{% for reply in replies %}
<div class="bg-light rounded p-3 border border-light">
<div class="d-flex justify-content-between align-items-start mb-2">
<div>
<div class="fw-bold d-flex align-items-center gap-2">
{{ reply.author_name }}
{% if reply.author_name == 'You' %}
<span class="badge bg-primary">You</span>
{% endif %}
</div>
<small class="text-muted">
<i class="bi bi-calendar me-1"></i> {{ reply.created_at|date:"M d, Y H:i" }}
</small>
</div>
</div>
<p class="mb-1">{{ reply.text }}</p>
<code class="small text-muted">{{ reply.reply_id|truncatechars:10 }}</code>
</div>
{% endfor %}
</div>
</div>
</div>
{% endif %}
<!-- Reply Form -->
<div class="card">
<div class="card-header bg-gradient-teal text-white">
<h5 class="mb-0">
<i class="bi bi-reply me-2"></i>
Post a Reply
</h5>
</div>
<div class="card-body">
<form method="post">
{% csrf_token %}
<div class="mb-3">
<textarea
id="reply-text"
name="text"
rows="5"
placeholder="Write your thoughtful reply here..."
class="form-control"
required></textarea>
<small class="text-muted mt-2 d-block">
<i class="bi bi-info-circle me-1"></i>
Your reply will be posted publicly to platform
</small>
</div>
<div class="d-flex gap-2">
<button type="submit" class="btn btn-primary">
<i class="bi bi-send me-2"></i>
Post Reply
</button>
<a href="{% url 'social:comments_list' platform_type %}" class="btn btn-outline-secondary">
<i class="bi bi-x-lg me-2"></i>
Cancel
</a>
</div>
</form>
</div>
</div>
</div>
</div>
<!-- Sidebar -->
<div class="col-lg-4">
<div class="d-flex flex-column gap-4">
<!-- Comment Info -->
<div class="card">
<div class="card-header">
<h5 class="mb-0">
<i class="bi bi-info-circle text-primary me-2"></i>
Comment Information
</h5>
</div>
<div class="card-body">
<div class="d-flex justify-content-between py-2 border-bottom">
<span class="small text-muted fw-bold">Platform</span>
<span class="small fw-bold">{{ platform_name }}</span>
</div>
{% if platform_type == 'META' and comment.source_platform %}
<div class="d-flex justify-content-between py-2 border-bottom">
<span class="small text-muted fw-bold">Source</span>
<span class="small fw-bold">
{% if comment.source_platform == 'FB' %}
Facebook
{% elif comment.source_platform == 'IG' %}
Instagram
{% endif %}
</span>
</div>
{% endif %}
<div class="d-flex justify-content-between py-2 border-bottom">
<span class="small text-muted fw-bold">Author</span>
<span class="small fw-bold">{{ comment.author_name }}</span>
</div>
<div class="d-flex justify-content-between py-2 border-bottom">
<span class="small text-muted fw-bold">Created</span>
<span class="small fw-bold">{{ comment.created_at|date:"M d, Y H:i" }}</span>
</div>
<div class="d-flex justify-content-between py-2 border-bottom">
<span class="small text-muted fw-bold">Replies</span>
<span class="small fw-bold">{{ replies|length }}</span>
</div>
<div class="d-flex justify-content-between py-2">
<span class="small text-muted fw-bold">Sync Method</span>
<span class="small fw-bold">
{% if comment.synced_via_webhook %}
<span class="badge bg-success">
<i class="bi bi-bolt-fill me-1"></i> Webhook
</span>
{% else %}
<span class="badge bg-primary">
<i class="bi bi-arrow-repeat me-1"></i> Polling
</span>
{% endif %}
</span>
</div>
</div>
</div>
<!-- AI Analysis -->
{% if comment.ai_analysis %}
<!-- Sentiment -->
{% if comment.ai_analysis.sentiment %}
<div class="card border-start border-4 border-success">
<div class="card-body">
<h5 class="mb-3">
<i class="bi bi-heart-pulse text-success me-2"></i>
Sentiment Analysis
</h5>
<div class="row g-2 mb-3">
<div class="col-6">
<div class="p-3 rounded text-center {% if comment.ai_analysis.sentiment.classification.en == 'positive' %}bg-success bg-opacity-10{% elif comment.ai_analysis.sentiment.classification.en == 'negative' %}bg-danger bg-opacity-10{% else %}bg-secondary bg-opacity-10{% endif %}">
<div class="small text-muted fw-bold mb-1">English</div>
<div class="h4 mb-0">{{ comment.ai_analysis.sentiment.classification.en|capfirst }}</div>
</div>
</div>
<div class="col-6">
<div class="p-3 rounded text-center {% if comment.ai_analysis.sentiment.classification.ar == 'إيجابي' %}bg-success bg-opacity-10{% elif comment.ai_analysis.sentiment.classification.ar == 'سلبي' %}bg-danger bg-opacity-10{% else %}bg-secondary bg-opacity-10{% endif %}">
<div class="small text-muted fw-bold mb-1">العربية</div>
<div class="h4 mb-0" style="direction: rtl;">{{ comment.ai_analysis.sentiment.classification.ar }}</div>
</div>
</div>
</div>
<div class="d-flex justify-content-between py-1">
<span class="small text-muted fw-bold">Sentiment Score</span>
<span class="small fw-bold">{{ comment.ai_analysis.sentiment.score|floatformat:2 }}</span>
</div>
<div class="d-flex justify-content-between py-1">
<span class="small text-muted fw-bold">Confidence</span>
<span class="small fw-bold">{{ comment.ai_analysis.sentiment.confidence|floatformat:0 }}%</span>
</div>
{% if comment.ai_analysis.sentiment.urgency_level %}
<div class="d-flex justify-content-between py-1">
<span class="small text-muted fw-bold">Urgency Level</span>
<span class="small fw-bold">
{% if comment.ai_analysis.sentiment.urgency_level.en == 'critical' %}
<span class="badge bg-danger">{{ comment.ai_analysis.sentiment.urgency_level.en }}</span>
{% elif comment.ai_analysis.sentiment.urgency_level.en == 'high' %}
<span class="badge bg-warning text-dark">{{ comment.ai_analysis.sentiment.urgency_level.en }}</span>
{% elif comment.ai_analysis.sentiment.urgency_level.en == 'medium' %}
<span class="badge bg-primary">{{ comment.ai_analysis.sentiment.urgency_level.en }}</span>
{% else %}
<span class="badge bg-success">{{ comment.ai_analysis.sentiment.urgency_level.en }}</span>
{% endif %}
</span>
</div>
{% endif %}
</div>
</div>
{% endif %}
<!-- Actionable Insights -->
{% if comment.ai_analysis.actionable_insights %}
<div class="card border-start border-4 border-warning">
<div class="card-body">
<h5 class="mb-3">
<i class="bi bi-lightbulb text-warning me-2"></i>
Actionable Insights
</h5>
{% if comment.ai_analysis.actionable_insights.primary_concern %}
<div class="mb-3 p-3 bg-light rounded">
<div class="small text-muted fw-bold mb-1">Primary Concern</div>
<div class="fw-bold mb-1">{{ comment.ai_analysis.actionable_insights.primary_concern.en }}</div>
<div class="small text-muted" style="direction: rtl;">{{ comment.ai_analysis.actionable_insights.primary_concern.ar }}</div>
</div>
{% endif %}
{% if comment.ai_analysis.actionable_insights.requires_followup is not None %}
<div class="d-flex justify-content-between py-1">
<span class="small text-muted fw-bold">Requires Follow-up</span>
<span class="small fw-bold">
{% if comment.ai_analysis.actionable_insights.requires_followup %}
<span class="badge bg-danger">Yes</span>
{% else %}
<span class="badge bg-success">No</span>
{% endif %}
</span>
</div>
{% endif %}
{% if comment.ai_analysis.actionable_insights.followup_priority %}
<div class="d-flex justify-content-between py-1">
<span class="small text-muted fw-bold">Follow-up Priority</span>
<span class="small fw-bold">
{% if comment.ai_analysis.actionable_insights.followup_priority.en == 'urgent' %}
<span class="badge bg-danger">{{ comment.ai_analysis.actionable_insights.followup_priority.en }}</span>
{% elif comment.ai_analysis.actionable_insights.followup_priority.en == 'high' %}
<span class="badge bg-warning text-dark">{{ comment.ai_analysis.actionable_insights.followup_priority.en }}</span>
{% else %}
<span class="badge bg-primary">{{ comment.ai_analysis.actionable_insights.followup_priority.en }}</span>
{% endif %}
</span>
</div>
{% endif %}
</div>
</div>
{% endif %}
<!-- Business Intelligence -->
{% if comment.ai_analysis.business_intelligence %}
<div class="card border-start border-4 border-info">
<div class="card-body">
<h5 class="mb-3">
<i class="bi bi-graph-up text-info me-2"></i>
Business Intelligence
</h5>
{% if comment.ai_analysis.business_intelligence.patient_satisfaction_score %}
<div class="text-center mb-3 p-4 rounded {% if comment.ai_analysis.business_intelligence.patient_satisfaction_score >= 80 %}bg-success bg-opacity-10{% elif comment.ai_analysis.business_intelligence.patient_satisfaction_score >= 60 %}bg-warning bg-opacity-10{% else %}bg-danger bg-opacity-10{% endif %}">
<div class="small text-muted fw-bold mb-2">Patient Satisfaction Score</div>
<div class="display-4 fw-bold {% if comment.ai_analysis.business_intelligence.patient_satisfaction_score >= 80 %}text-success{% elif comment.ai_analysis.business_intelligence.patient_satisfaction_score >= 60 %}text-warning{% else %}text-danger{% endif %}">
{{ comment.ai_analysis.business_intelligence.patient_satisfaction_score }}
</div>
</div>
{% endif %}
{% if comment.ai_analysis.business_intelligence.retention_risk %}
<div class="d-flex justify-content-between py-1">
<span class="small text-muted fw-bold">Retention Risk</span>
<span class="small fw-bold">
{% if comment.ai_analysis.business_intelligence.retention_risk.level == 'high' %}
<span class="badge bg-danger">{{ comment.ai_analysis.business_intelligence.retention_risk.level }}</span>
{% elif comment.ai_analysis.business_intelligence.retention_risk.level == 'medium' %}
<span class="badge bg-warning text-dark">{{ comment.ai_analysis.business_intelligence.retention_risk.level }}</span>
{% else %}
<span class="badge bg-success">{{ comment.ai_analysis.business_intelligence.retention_risk.level }}</span>
{% endif %}
</span>
</div>
{% endif %}
{% if comment.ai_analysis.business_intelligence.reputation_impact %}
<div class="d-flex justify-content-between py-1">
<span class="small text-muted fw-bold">Reputation Impact</span>
<span class="small fw-bold">
{% if comment.ai_analysis.business_intelligence.reputation_impact.level == 'severe' %}
<span class="badge bg-danger">{{ comment.ai_analysis.business_intelligence.reputation_impact.level }}</span>
{% elif comment.ai_analysis.business_intelligence.reputation_impact.level == 'negative' %}
<span class="badge bg-warning text-dark">{{ comment.ai_analysis.business_intelligence.reputation_impact.level }}</span>
{% elif comment.ai_analysis.business_intelligence.reputation_impact.level == 'positive' %}
<span class="badge bg-success">{{ comment.ai_analysis.business_intelligence.reputation_impact.level }}</span>
{% else %}
<span class="badge bg-primary">{{ comment.ai_analysis.business_intelligence.reputation_impact.level }}</span>
{% endif %}
</span>
</div>
{% endif %}
</div>
</div>
{% endif %}
<!-- Keywords -->
{% if comment.ai_analysis.keywords %}
<div class="card">
<div class="card-header">
<h5 class="mb-0">
<i class="bi bi-tags text-primary me-2"></i>
Keywords
</h5>
</div>
<div class="card-body">
<div class="d-flex flex-wrap gap-2">
{% for keyword in comment.ai_analysis.keywords.en %}
<span class="badge bg-primary bg-opacity-10 text-primary">{{ keyword }}</span>
{% endfor %}
</div>
<div class="d-flex flex-wrap gap-2 mt-2">
{% for keyword in comment.ai_analysis.keywords.ar %}
<span class="badge bg-warning bg-opacity-10 text-warning" style="direction: rtl;">{{ keyword }}</span>
{% endfor %}
</div>
</div>
</div>
{% endif %}
<!-- Emotions -->
{% if comment.ai_analysis.emotions %}
<div class="card">
<div class="card-header">
<h5 class="mb-0">
<i class="bi bi-emoji-smile text-primary me-2"></i>
Emotion Analysis
</h5>
</div>
<div class="card-body">
<div class="mb-3">
<div class="d-flex justify-content-between mb-1">
<span class="small fw-bold text-muted"><i class="bi bi-smile text-success me-1"></i>Joy / فرح</span>
<span class="small fw-bold">{{ comment.ai_analysis.emotions.joy|floatformat:0 }}%</span>
</div>
<div class="progress" style="height: 8px;">
<div class="progress-bar bg-success" style="width: {{ comment.ai_analysis.emotions.joy|floatformat:0 }}%"></div>
</div>
</div>
<div class="mb-3">
<div class="d-flex justify-content-between mb-1">
<span class="small fw-bold text-muted"><i class="bi bi-emoji-angry text-danger me-1"></i>Anger / غضب</span>
<span class="small fw-bold">{{ comment.ai_analysis.emotions.anger|floatformat:0 }}%</span>
</div>
<div class="progress" style="height: 8px;">
<div class="progress-bar bg-danger" style="width: {{ comment.ai_analysis.emotions.anger|floatformat:0 }}%"></div>
</div>
</div>
<div class="mb-3">
<div class="d-flex justify-content-between mb-1">
<span class="small fw-bold text-muted"><i class="bi bi-emoji-frown text-muted me-1"></i>Sadness / حزن</span>
<span class="small fw-bold">{{ comment.ai_analysis.emotions.sadness|floatformat:0 }}%</span>
</div>
<div class="progress" style="height: 8px;">
<div class="progress-bar bg-secondary" style="width: {{ comment.ai_analysis.emotions.sadness|floatformat:0 }}%"></div>
</div>
</div>
<div>
<div class="d-flex justify-content-between mb-1">
<span class="small fw-bold text-muted"><i class="bi bi-emoji-neutral text-primary me-1"></i>Fear / خوف</span>
<span class="small fw-bold">{{ comment.ai_analysis.emotions.fear|floatformat:0 }}%</span>
</div>
<div class="progress" style="height: 8px;">
<div class="progress-bar bg-primary" style="width: {{ comment.ai_analysis.emotions.fear|floatformat:0 }}%"></div>
</div>
</div>
</div>
</div>
{% endif %}
<!-- AI Summary -->
{% if comment.ai_analysis.summaries %}
<div class="card">
<div class="card-header">
<h5 class="mb-0">
<i class="bi bi-file-text text-primary me-2"></i>
AI Summary
</h5>
</div>
<div class="card-body">
<p class="mb-2 small">{{ comment.ai_analysis.summaries.en }}</p>
<p class="small" style="direction: rtl;">{{ comment.ai_analysis.summaries.ar }}</p>
</div>
</div>
{% endif %}
{% else %}
<!-- AI Not Started -->
<div class="card text-center">
<div class="card-body">
<div class="avatar avatar-xl bg-light text-muted mx-auto mb-3">
<i class="bi bi-robot fs-1"></i>
</div>
<h5 class="mb-2">AI Analysis Pending</h5>
<p class="text-muted small mb-3">
This comment is waiting for AI-powered analysis. The system will automatically analyze sentiment, topics, emotions, and generate actionable business intelligence insights.
</p>
<div class="text-start small text-muted bg-light p-3 rounded">
<strong class="text-body">Analysis will include:</strong>
<ul class="mt-2 mb-0">
<li><i class="bi bi-check-circle text-success me-2"></i>Sentiment analysis (English & Arabic)</li>
<li><i class="bi bi-check-circle text-success me-2"></i>Actionable insights & recommendations</li>
<li><i class="bi bi-check-circle text-success me-2"></i>Business intelligence metrics</li>
<li><i class="bi bi-check-circle text-success me-2"></i>Patient journey tracking</li>
<li><i class="bi bi-check-circle text-success me-2"></i>Service quality indicators</li>
</ul>
</div>
</div>
</div>
{% endif %}
</div>
</div>
</div>
{% endblock %}

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apps/social/templates/social/comments_list.html Normal file
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{% extends 'layouts/base.html' %}
{% load social_filters %}
{% load social_icons %}
{% load action_icons %}
{% load star_rating %}
{% block title %}Comments - {{ platform_name }}{% endblock %}
{% block page_title %}{{ platform_name }} Comments{% endblock %}
{% block page_subtitle %}View and manage comments from {{ account.name }}{% endblock %}
{% block content %}
<!-- Header Actions -->
<div class="d-flex justify-content-between align-items-center mb-4">
<a href="{% url 'social:dashboard' %}" class="btn btn-outline-secondary">
<i class="bi bi-arrow-left me-2"></i>
Back to Dashboard
</a>
<div class="d-flex gap-2">
<a href="{% url 'social:sync' platform_type %}" class="btn btn-primary">
<i class="bi bi-arrow-repeat me-2"></i>
Sync Now
</a>
</div>
</div>
<!-- Filter Section -->
<div class="card mb-4">
<div class="card-body">
<form method="get">
<div class="row g-3">
<div class="col-lg-4">
<div class="position-relative">
<i class="bi bi-search position-absolute top-50 start-0 translate-middle-y ms-3 text-muted"></i>
<input type="text"
name="search"
placeholder="Search comments..."
value="{{ search_query|default:'' }}"
class="form-control ps-5">
</div>
</div>
<div class="col-lg-8">
<div class="d-flex gap-2 flex-wrap">
<select name="sentiment" class="form-select w-auto">
<option value="">All Sentiments</option>
<option value="positive" {% if sentiment_filter == 'positive' %}selected{% endif %}>Positive</option>
<option value="neutral" {% if sentiment_filter == 'neutral' %}selected{% endif %}>Neutral</option>
<option value="negative" {% if sentiment_filter == 'negative' %}selected{% endif %}>Negative</option>
</select>
<select name="sync_via_webhook" class="form-select w-auto">
<option value="">All Sync Methods</option>
<option value="true" {% if sync_filter == 'true' %}selected{% endif %}>Real-time Only</option>
<option value="false" {% if sync_filter == 'false' %}selected{% endif %}>Polling Only</option>
</select>
{% if platform_type == 'META' %}
<select name="source_platform" class="form-select w-auto">
<option value="">All Sources</option>
<option value="FB" {% if request.GET.source_platform == 'FB' %}selected{% endif %}>Facebook</option>
<option value="IG" {% if request.GET.source_platform == 'IG' %}selected{% endif %}>Instagram</option>
</select>
{% endif %}
</div>
</div>
</div>
<div class="d-flex justify-content-between align-items-center mt-3 pt-3 border-top">
<div class="d-flex gap-2 flex-wrap">
{% if search_query %}
<span class="badge bg-primary">
<i class="bi bi-search me-1"></i>
Search: {{ search_query|truncatechars:20 }}
<a href="{% url 'social:comments_list' platform_type %}" class="text-white ms-1">
<i class="bi bi-x-lg"></i>
</a>
</span>
{% endif %}
{% if sentiment_filter %}
<span class="badge bg-primary">
<i class="bi bi-emoji-smile me-1"></i>
Sentiment: {{ sentiment_filter|capfirst }}
<a href="?{% if search_query %}search={{ search_query }}{% endif %}{% if sync_filter %}&sync_via_webhook={{ sync_filter }}{% endif %}{% if request.GET.source_platform %}&source_platform={{ request.GET.source_platform }}{% endif %}" class="text-white ms-1">
<i class="bi bi-x-lg"></i>
</a>
</span>
{% endif %}
{% if sync_filter %}
<span class="badge bg-primary">
<i class="bi bi-arrow-repeat me-1"></i>
Sync: {% if sync_filter == 'true' %}Real-time{% else %}Polling{% endif %}
<a href="?{% if search_query %}search={{ search_query }}{% endif %}{% if sentiment_filter %}&sentiment={{ sentiment_filter }}{% endif %}{% if request.GET.source_platform %}&source_platform={{ request.GET.source_platform }}{% endif %}" class="text-white ms-1">
<i class="bi bi-x-lg"></i>
</a>
</span>
{% endif %}
</div>
<div class="d-flex gap-2">
<button type="submit" class="btn btn-primary">
<i class="bi bi-funnel me-2"></i>
Apply Filters
</button>
<a href="{% url 'social:comments_list' platform_type %}" class="btn btn-outline-secondary">
<i class="bi bi-x-lg me-2"></i>
Clear
</a>
<a href="{% url 'social:export_comments_csv' platform_type %}{% if search_query %}?search={{ search_query }}{% endif %}{% if sentiment_filter %}{% if search_query %}&{% else %}?{% endif %}sentiment={{ sentiment_filter }}{% endif %}{% if sync_filter %}{% if search_query or sentiment_filter %}&{% else %}?{% endif %}sync_via_webhook={{ sync_filter }}{% endif %}{% if request.GET.source_platform %}{% if search_query or sentiment_filter or sync_filter %}&{% else %}?{% endif %}source_platform={{ request.GET.source_platform }}{% endif %}" class="btn btn-success">
<i class="bi bi-download me-2"></i>
Export CSV
</a>
</div>
</div>
</form>
</div>
</div>
<!-- Results Info -->
{% if page_obj %}
<div class="card mb-3">
<div class="card-body d-flex justify-content-between align-items-center">
<div class="text-muted">
Showing <strong>{{ page_obj.start_index }}</strong> to <strong>{{ page_obj.end_index }}</strong> of <strong>{{ page_obj.paginator.count }}</strong> comments
</div>
<div class="small text-muted">
<i class="bi bi-info-circle me-1"></i>
Use filters to narrow down results
</div>
</div>
</div>
{% endif %}
<!-- Comments List -->
{% if page_obj %}
<div class="d-flex flex-column gap-3">
{% for comment in page_obj %}
<a href="{% url 'social:comment_detail' platform_type comment.comment_id %}" class="card text-decoration-none hover-lift">
<div class="card-body">
<div class="d-flex gap-3">
<!-- Author Avatar -->
<div class="avatar avatar-lg bg-primary flex-shrink-0">
{{ comment.author_name|slice:":1"|upper }}
</div>
<!-- Content -->
<div class="flex-grow-1">
<div class="d-flex justify-content-between mb-2">
<div>
<h5 class="mb-1">{{ comment.author_name }}</h5>
<p class="text-muted small">{{ comment.text|truncatewords:20 }}</p>
</div>
</div>
<!-- Badges -->
<div class="d-flex gap-2 flex-wrap mt-2">
{% if comment.is_reply %}
<span class="badge bg-info">
<i class="bi bi-reply me-1"></i> Reply
</span>
{% endif %}
{% if comment.is_replied %}
<span class="badge bg-success">
<i class="bi bi-check-circle me-1"></i> Replied
</span>
{% endif %}
{% if comment.synced_via_webhook %}
<span class="badge badge-soft-success">
<i class="bi bi-circle-fill me-1"></i>
Real-time
</span>
{% endif %}
{% if comment.rating %}
<span class="badge bg-warning text-dark">
<i class="bi bi-star-fill me-1"></i> {{ comment.rating }}
</span>
{% endif %}
{% if comment.ai_analysis and comment.ai_analysis.actionable_insights.requires_followup %}
<span class="badge bg-danger">
<i class="bi bi-bell me-1"></i> Follow-up
</span>
{% endif %}
</div>
</div>
<!-- Right Side Info -->
<div class="d-flex flex-column align-items-end gap-2 flex-shrink-0">
<!-- Sentiment -->
{% if comment.ai_analysis %}
<span class="badge {% if comment.ai_analysis.sentiment.classification.en == 'positive' %}bg-success{% elif comment.ai_analysis.sentiment.classification.en == 'negative' %}bg-danger{% else %}bg-secondary{% endif %}">
{{ comment.ai_analysis.sentiment.classification.en|capfirst }}
</span>
{% else %}
<span class="small text-muted">Pending</span>
{% endif %}
<!-- Score -->
{% if comment.ai_analysis and comment.ai_analysis.business_intelligence.patient_satisfaction_score %}
<span class="badge {% if comment.ai_analysis.business_intelligence.patient_satisfaction_score >= 80 %}bg-success{% elif comment.ai_analysis.business_intelligence.patient_satisfaction_score >= 60 %}bg-warning text-dark{% else %}bg-danger{% endif %}">
{{ comment.ai_analysis.business_intelligence.patient_satisfaction_score }}%
</span>
{% endif %}
<!-- Engagement -->
<div class="d-flex gap-2 small text-muted">
{% if comment.like_count > 0 %}
<span>
<i class="bi bi-heart-fill text-danger me-1"></i>
{{ comment.like_count }}
</span>
{% endif %}
{% if comment.reply_count > 0 %}
<span>
<i class="bi bi-chat-fill text-primary me-1"></i>
{{ comment.reply_count }}
</span>
{% endif %}
</div>
</div>
</div>
</div>
</a>
{% endfor %}
</div>
<!-- Pagination -->
{% if page_obj.has_other_pages %}
<nav class="mt-4" aria-label="Comments pagination">
<ul class="pagination justify-content-center">
{% if page_obj.has_previous %}
<li class="page-item">
<a class="page-link" href="?page={{ page_obj.previous_page_number }}{% if search_query %}&search={{ search_query }}{% endif %}{% if sentiment_filter %}&sentiment={{ sentiment_filter }}{% endif %}{% if sync_filter %}&sync_via_webhook={{ sync_filter }}{% endif %}{% if request.GET.source_platform %}&source_platform={{ request.GET.source_platform }}{% endif %}">
<i class="bi bi-chevron-left"></i> Previous
</a>
</li>
{% endif %}
{% for page_num in page_obj.paginator.page_range %}
{% if page_num == page_obj.number %}
<li class="page-item active">
<span class="page-link">{{ page_num }}</span>
</li>
{% else %}
<li class="page-item">
<a class="page-link" href="?page={{ page_num }}{% if search_query %}&search={{ search_query }}{% endif %}{% if sentiment_filter %}&sentiment={{ sentiment_filter }}{% endif %}{% if sync_filter %}&sync_via_webhook={{ sync_filter }}{% endif %}{% if request.GET.source_platform %}&source_platform={{ request.GET.source_platform }}{% endif %}">
{{ page_num }}
</a>
</li>
{% endif %}
{% endfor %}
{% if page_obj.has_next %}
<li class="page-item">
<a class="page-link" href="?page={{ page_obj.next_page_number }}{% if search_query %}&search={{ search_query }}{% endif %}{% if sentiment_filter %}&sentiment={{ sentiment_filter }}{% endif %}{% if sync_filter %}&sync_via_webhook={{ sync_filter }}{% endif %}{% if request.GET.source_platform %}&source_platform={{ request.GET.source_platform }}{% endif %}">
Next <i class="bi bi-chevron-right"></i>
</a>
</li>
{% endif %}
</ul>
</nav>
{% endif %}
{% else %}
<!-- Empty State -->
<div class="card text-center py-5">
<div class="card-body">
<div class="avatar avatar-xl bg-primary mx-auto mb-3">
<i class="bi bi-chat-dots"></i>
</div>
<h4 class="mb-2">No Comments Found</h4>
<p class="text-muted mb-4">
{% if search_query %}
No comments match your search criteria. Try adjusting your filters.
{% elif sync_filter %}
No comments with selected sync method.
{% else %}
No comments found for this account. Try syncing to fetch new comments.
{% endif %}
</p>
<a href="{% url 'social:sync' platform_type %}" class="btn btn-primary">
<i class="bi bi-arrow-repeat me-2"></i>
Sync Now
</a>
</div>
</div>
{% endif %}
{% endblock %}

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apps/social/templates/social/dashboard.html Normal file
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@ -0,0 +1,350 @@
{% extends 'layouts/base.html' %}
{% load social_filters %}
{% load social_icons %}
{% block title %}Dashboard - Social{% endblock %}
{% block page_title %}Social Media Dashboard{% endblock %}
{% block page_subtitle %}Manage all your social media accounts in one place{% endblock %}
{% block content %}
<!-- Statistics Cards -->
<div class="row g-4 mb-4">
<!-- Total Accounts -->
<div class="col-md-6 col-xl-3">
<div class="card hover-lift">
<div class="card-body">
<div class="d-flex justify-content-between align-items-start mb-3">
<div class="avatar avatar-lg bg-blue">
<i class="bi bi-people"></i>
</div>
<span class="badge badge-soft-success">Active</span>
</div>
<h3 class="stat-value">{{ accounts|length }}</h3>
<p class="stat-label">Connected Accounts</p>
</div>
</div>
</div>
<!-- Active Platforms -->
<div class="col-md-6 col-xl-3">
<div class="card hover-lift">
<div class="card-body">
<div class="d-flex justify-content-between align-items-start mb-3">
<div class="avatar avatar-lg bg-success">
<i class="bi bi-check-circle"></i>
</div>
<span class="badge badge-soft-info">Syncing</span>
</div>
<h3 class="stat-value">
{{ accounts|dictsort:"account.is_active"|length }}
</h3>
<p class="stat-label">Active Platforms</p>
</div>
</div>
</div>
<!-- Expired Tokens -->
<div class="col-md-6 col-xl-3">
<div class="card hover-lift">
<div class="card-body">
<div class="d-flex justify-content-between align-items-start mb-3">
<div class="avatar avatar-lg bg-red">
<i class="bi bi-exclamation-triangle"></i>
</div>
<span class="badge badge-soft-danger">Action</span>
</div>
<h3 class="stat-value">
{{ accounts|dictsort:"is_expired"|length }}
</h3>
<p class="stat-label">Expired Tokens</p>
</div>
</div>
</div>
<!-- Available Platforms -->
<div class="col-md-6 col-xl-3">
<div class="card hover-lift">
<div class="card-body">
<div class="d-flex justify-content-between align-items-start mb-3">
<div class="avatar avatar-lg bg-blue">
<i class="bi bi-plus-circle"></i>
</div>
<span class="badge badge-soft-primary">Ready</span>
</div>
<h3 class="stat-value">{{ platform_names|length }}</h3>
<p class="stat-label">Available Platforms</p>
</div>
</div>
</div>
</div>
<!-- Connected Accounts -->
{% if accounts %}
<div class="card mb-4">
<div class="card-header bg-gradient-teal text-white">
<div class="d-flex justify-content-between align-items-center">
<div>
<h5 class="card-title mb-1">
{% if selected_platform %}
{{ platform_names|get_item:selected_platform }} Account
{% else %}
Connected Accounts
{% endif %}
</h5>
<small class="opacity-75">{{ accounts|length }} account(s) connected</small>
</div>
<span class="badge bg-white text-primary fw-bold fs-6">{{ accounts|length }}</span>
</div>
</div>
<div class="card-body">
<div class="table-responsive">
<table class="table table-hover">
<thead>
<tr>
<th>Platform</th>
<th>Account Name</th>
<th>Platform ID</th>
<th>Status</th>
<th>Last Sync</th>
<th>Actions</th>
</tr>
</thead>
<tbody>
{% for platform, acc_data in accounts.items %}
<tr>
<td>
<div class="d-flex align-items-center gap-3">
<div class="avatar bg-primary" style="background: var(--platform-{{ platform }});">
{% social_icon platform %}
</div>
<div>
<div class="fw-semibold">{{ platform_names|get_item:platform }}</div>
{% if acc_data.account.is_active %}
<div class="badge badge-soft-success">
<i class="bi bi-circle-fill me-1"></i>
{% if platform == 'META' %}
Real-time
{% else %}
Polling
{% endif %}
</div>
{% endif %}
</div>
</div>
</td>
<td>
<span class="fw-semibold">{{ acc_data.account.name }}</span>
</td>
<td>
<code class="text-muted">{{ acc_data.account.platform_id|truncatechars:20 }}</code>
</td>
<td>
{% if acc_data.account.is_active %}
<span class="badge bg-success">
<i class="bi bi-circle-fill me-1"></i> Active
</span>
{% else %}
<span class="badge bg-secondary">
<i class="bi bi-circle me-1"></i> Inactive
</span>
{% endif %}
{% if acc_data.is_expired %}
<span class="badge bg-danger ms-1">
<i class="bi bi-x-circle me-1"></i> Expired
</span>
{% endif %}
</td>
<td>
{% if acc_data.account.last_synced_at %}
<span class="text-muted small">
<i class="bi bi-calendar"></i>
{{ acc_data.account.last_synced_at|date:"M d, H:i" }}
</span>
{% else %}
<span class="text-muted small">Never</span>
{% endif %}
</td>
<td>
<div class="d-flex gap-2">
<a href="{% url 'social:comments_list' platform %}" class="btn btn-primary btn-sm">
<i class="bi bi-chat-dots"></i>
</a>
<a href="{% url 'social:sync' platform %}" class="btn btn-outline-primary btn-sm">
<i class="bi bi-arrow-repeat"></i>
</a>
{% if platform in 'TT,X,YT,META' %}
<a href="{% url 'social:sync_type' platform 'full' %}" class="btn btn-outline-secondary btn-sm">
<i class="bi bi-download"></i>
</a>
{% endif %}
</div>
</td>
</tr>
{% endfor %}
</tbody>
</table>
</div>
</div>
</div>
{% endif %}
<!-- Connect Platforms Section -->
<div class="mb-4">
<h5 class="mb-3">
<i class="bi bi-plug text-primary"></i>
Connect Your Accounts
</h5>
<p class="text-muted mb-4">Connect platforms to start monitoring comments</p>
<div class="row g-4">
{% for platform_code, platform_name in platform_names.items %}
{% if not selected_platform or selected_platform == platform_code %}
<div class="col-md-6 col-xl-4">
{% if accounts|get_item:platform_code %}
<!-- Connected Platform Card -->
<div class="card border-success h-100">
<div class="card-body">
<div class="d-flex justify-content-between align-items-start mb-3">
<div class="avatar avatar-lg bg-success">
{% social_icon platform_code %}
</div>
<span class="badge bg-success">Connected</span>
</div>
<h5 class="card-title">{{ platform_name }}</h5>
<p class="text-muted small mb-3">Account is active and syncing</p>
<a href="{% url 'social:comments_list' platform_code %}" class="btn btn-primary w-100">
<i class="bi bi-chat-dots me-2"></i> View Comments
</a>
</div>
</div>
{% else %}
<!-- Unconnected Platform Card -->
<a href="{% url 'social:auth_start' platform_code %}" class="card h-100 text-decoration-none hover-lift">
<div class="card-body">
<div class="d-flex justify-content-between align-items-start mb-3">
<div class="avatar avatar-lg bg-light text-muted">
{% social_icon platform_code %}
</div>
<span class="badge bg-secondary">Connect</span>
</div>
<h5 class="card-title">{{ platform_name }}</h5>
<p class="text-muted small mb-3">Click to connect your account</p>
<div class="btn btn-primary w-100">
<i class="bi bi-plus-lg me-2"></i> Connect
</div>
</div>
</a>
{% endif %}
</div>
{% endif %}
{% endfor %}
</div>
</div>
<!-- Webhook Information -->
<div class="card">
<div class="card-header">
<h5 class="card-title mb-0">
<i class="bi bi-bolt text-warning me-2"></i>
Webhook Support Information
</h5>
</div>
<div class="card-body">
<div class="row g-3">
<div class="col-md-6">
<div class="d-flex gap-3 p-3 bg-light rounded">
<div class="avatar bg-primary">
<i class="bi bi-facebook"></i>
</div>
<div>
<h6 class="fw-bold mb-1">Meta (Facebook/Instagram)</h6>
<div class="d-flex gap-2 align-items-center">
<span class="badge bg-success">Webhooks</span>
<span class="text-muted small">Real-time updates</span>
</div>
</div>
</div>
</div>
<div class="col-md-6">
<div class="d-flex gap-3 p-3 bg-light rounded">
<div class="avatar bg-primary">
<i class="bi bi-linkedin"></i>
</div>
<div>
<h6 class="fw-bold mb-1">LinkedIn</h6>
<div class="d-flex gap-2 align-items-center">
<span class="badge bg-primary">Polling</span>
<span class="text-muted small">Periodic sync</span>
</div>
</div>
</div>
</div>
<div class="col-md-6">
<div class="d-flex gap-3 p-3 bg-light rounded">
<div class="avatar bg-danger">
<i class="bi bi-google"></i>
</div>
<div>
<h6 class="fw-bold mb-1">Google Reviews</h6>
<div class="d-flex gap-2 align-items-center">
<span class="badge bg-primary">Polling</span>
<span class="text-muted small">Periodic sync</span>
</div>
</div>
</div>
</div>
<div class="col-md-6">
<div class="d-flex gap-3 p-3 bg-light rounded">
<div class="avatar bg-dark">
<i class="bi bi-music-note-beamed"></i>
</div>
<div>
<h6 class="fw-bold mb-1">TikTok</h6>
<div class="d-flex gap-2 align-items-center">
<span class="badge bg-primary">Polling</span>
<span class="text-muted small">Periodic sync</span>
</div>
</div>
</div>
</div>
<div class="col-md-6">
<div class="d-flex gap-3 p-3 bg-dark text-white rounded">
<div class="avatar bg-white text-dark">
<i class="bi bi-twitter-x"></i>
</div>
<div>
<h6 class="fw-bold mb-1">X (Twitter)</h6>
<div class="d-flex gap-2 align-items-center">
<span class="badge bg-primary">Polling</span>
<span class="text-white-50 small">Periodic sync</span>
</div>
</div>
</div>
</div>
<div class="col-md-6">
<div class="d-flex gap-3 p-3 bg-light rounded">
<div class="avatar bg-danger">
<i class="bi bi-youtube"></i>
</div>
<div>
<h6 class="fw-bold mb-1">YouTube</h6>
<div class="d-flex gap-2 align-items-center">
<span class="badge bg-primary">Polling</span>
<span class="text-muted small">Periodic sync</span>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
{% endblock %}

163
apps/social/templatetags/ACTION_ICONS_README.md
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# Action Icons Template Tag
## Overview
The `action_icons` template tag library provides reusable SVG icons for common UI actions throughout the application.
## Usage
### Loading the Library
```django
{% load action_icons %}
```
### Using the action_icon Tag
**Correct syntax** (simple_tag):
```django
{% action_icon 'create' %}
{% action_icon 'edit' %}
{% action_icon 'delete' %}
{% action_icon 'view' %}
```
**Incorrect syntax** (will cause TemplateSyntaxError):
```django
{{ action_icon 'create' }} <!-- DON'T USE THIS -->
```
### Available Icons
| Action Name | Icon | Description |
|-------------|-------|-------------|
| `create` | ➕ Plus sign | Create/add new item |
| `edit` | ✏️ Pencil | Edit existing item |
| `delete` | 🗑️ Trash | Delete item |
| `view` | 👁️ Eye | View details |
| `save` | 💾 Floppy disk | Save changes |
| `cancel` | ✖️ X | Cancel action |
| `back` | ⬅️ Arrow | Go back |
| `download` | ⬇️ Down arrow | Download content |
| `upload` | ⬆️ Up arrow | Upload content |
| `search` | 🔍 Magnifying glass | Search |
| `filter` | 🔽 Lines | Filter results |
| `check` | ✓ Checkmark | Confirm/success |
| `warning` | ⚠️ Triangle | Warning |
| `info` | ℹ️ Circle | Information |
| `refresh` | 🔄 Arrow circle | Refresh/reload |
| `copy` | 📋 Documents | Copy to clipboard |
| `print` | 🖨️ Printer | Print content |
| `export` | ⬇️ Down arrow | Export data |
| `import` | ⬆️ Up arrow | Import data |
### Custom Size
```django
{% action_icon 'create' size=20 %}
```
Default size is 16x16 pixels.
## Example Usage
### In Button Links
```django
<a href="{% url 'items:create' %}" class="btn btn-primary">
{% action_icon 'create' %} {% trans "Add Item" %}
</a>
```
### In Action Buttons
```django
<a href="{% url 'items:edit' item.pk %}"
class="btn btn-sm btn-warning"
title="{% trans 'Edit' %}">
{% action_icon 'edit' %}
</a>
```
### In Headers
```django
<h5 class="card-title mb-0">
{% action_icon 'filter' %} {% trans "Items" %}
</h5>
```
## Technical Details
### File Location
`apps/social/templatetags/action_icons.py`
### Registration
```python
from django import template
register = template.Library()
@register.simple_tag
def action_icon(name, size=16):
"""
Return SVG icon for a given action.
"""
# Returns safe HTML string
```
### Why simple_tag?
The `action_icon` function is registered as a `simple_tag`, not a filter or template variable:
- **simple_tag**: `{% tag_name args %}` - Can process multiple arguments
- **filter**: `{{ value|filter }}` - Works on single value
- **assignment_tag**: `{% tag_name as variable %}` - Stores result in variable
For icons, a `simple_tag` is most appropriate because:
1. It returns HTML directly
2. It doesn't need a variable context
3. It takes parameters (icon name, optional size)
## Common Errors
### TemplateSyntaxError
**Error**: `Could not parse the remainder from 'action_icon 'create''`
**Cause**: Using variable syntax instead of tag syntax
```django
{{ action_icon 'create' }} <!-- WRONG -->
```
**Fix**: Use tag syntax
```django
{% action_icon 'create' %} <!-- CORRECT -->
```
### Icon Not Showing
**Cause**: Forgetting to load the template tag library
```django
{% load i18n %} <!-- Missing action_icons -->
```
**Fix**: Load the library
```django
{% load i18n action_icons %}
```
## Implementation Notes
- All icons are SVG format for scalability
- Icons use Bootstrap Icons design language
- Icons return `mark_safe()` HTML strings
- Default size matches Bootstrap button small size (16px)
- Icons can be customized with CSS for color, hover effects, etc.
## Related Files
- `apps/social/templatetags/action_icons.py` - Tag implementation
- `apps/social/templatetags/social_icons.py` - Social media icons
- `apps/social/templatetags/star_rating.py` - Star rating icons

2
apps/social/templatetags/__init__.py
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@ -1 +1 @@
# Template tags for social app
# social/templatetags

103
apps/social/templatetags/social_filters.py
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@ -1,93 +1,42 @@
"""
Custom template filters for social app
"""
# social/templatetags/social_filters.py
import json
from django import template
register = template.Library()
@register.filter
def multiply(value, arg):
"""Multiply value by arg"""
try:
return float(value) * float(arg)
except (ValueError, TypeError):
return 0
@register.filter
def add(value, arg):
"""Add arg to value"""
try:
return float(value) + float(arg)
except (ValueError, TypeError):
return 0
@register.filter
def get_sentiment_emoji(sentiment):
"""Get emoji for sentiment classification"""
emoji_map = {
'positive': '😊',
'negative': '😞',
'neutral': '😐'
}
return emoji_map.get(sentiment.lower(), '😐')
@register.filter
def lookup(dictionary, key):
"""
Lookup a key in a dictionary.
Usage: {{ stats|lookup:platform_code }}
"""
if dictionary is None:
return 0
return dictionary.get(key, 0)
@register.filter
def get_item(dictionary, key):
"""
Get an item from a dictionary using dot notation.
Usage: {{ sentiment_distribution|get_item:positive|get_item:count }}
Get an item from a dictionary using a variable key.
Usage: {{ mydict|get_item:key }}
"""
if dictionary is None:
return None
return dictionary.get(key, None)
if isinstance(dictionary, dict):
return dictionary.get(key)
return None
@register.filter
def get_sentiment(comment):
def pprint(value):
"""
Get sentiment classification from ai_analysis.
Usage: {{ comment|get_sentiment }}
Pretty print JSON/dict values.
Usage: {{ data|pprint }}
"""
if not hasattr(comment, 'ai_analysis') or not comment.ai_analysis:
return None
return comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en')
if isinstance(value, (dict, list)):
return json.dumps(value, indent=2, ensure_ascii=False)
return str(value)
@register.filter
def get_sentiment_count(sentiment_list, sentiment_type):
def percentage(value):
"""
Get count for a specific sentiment from a list of sentiment dictionaries.
Usage: {{ sentiment_distribution|get_sentiment_count:'positive' }}
Args:
sentiment_list: List of sentiment dictionaries with 'sentiment' and 'count' keys
sentiment_type: String value to match (e.g., 'positive', 'negative', 'neutral')
Returns:
Count of items matching the sentiment_type, or 0 if not found
Convert a decimal to a percentage string.
Usage: {{ 0.95|percentage }} -> 95%
"""
if not sentiment_list:
return 0
for item in sentiment_list:
if isinstance(item, dict) and item.get('sentiment') == sentiment_type:
return item.get('count', 0)
return 0
try:
if value is None:
return 'N/A'
# Convert to float if it's a string
float_value = float(value)
# Convert to percentage
percentage = float_value * 100
return f"{percentage:.0f}%"
except (ValueError, TypeError):
return str(value)

90
apps/social/templatetags/social_icons.py
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@ -4,38 +4,102 @@ from django.utils.safestring import mark_safe
register = template.Library()
SOCIAL_ICONS = {
'facebook': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
'META': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<defs>
<linearGradient id="meta-gradient" x1="0%" y1="0%" x2="100%" y2="100%">
<stop offset="0%" style="stop-color:#0081FB;stop-opacity:1" />
<stop offset="50%" style="stop-color:#0099FF;stop-opacity:1" />
<stop offset="100%" style="stop-color:#00D4FF;stop-opacity:1" />
</linearGradient>
</defs>
<path fill="url(#meta-gradient)" d="m24,18.6566c-2.4982-4.0249-5.5285-7.1014-9.0214-7.1014s-10.4786,4.6263-10.4786,15.452c0,7.9804,2.1512,9.4377,4.4413,9.4377,5.8986,0,12.1673-12.1904,15.0587-17.7883Z"/>
<path fill="url(#meta-gradient)" d="m24,18.6566c2.4982-4.0249,5.5285-7.1014,9.0214-7.1014s10.4786,4.6263,10.4786,15.452c0,7.9804-2.1512,9.4377-4.4413,9.4377-5.8986,0-12.1673-12.1904-15.0587-17.7883Z"/>
</svg>''',
'meta': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<defs>
<linearGradient id="meta-gradient2" x1="0%" y1="0%" x2="100%" y2="100%">
<stop offset="0%" style="stop-color:#0081FB;stop-opacity:1" />
<stop offset="50%" style="stop-color:#0099FF;stop-opacity:1" />
<stop offset="100%" style="stop-color:#00D4FF;stop-opacity:1" />
</linearGradient>
</defs>
<path fill="url(#meta-gradient2)" d="m24,18.6566c-2.4982-4.0249-5.5285-7.1014-9.0214-7.1014s-10.4786,4.6263-10.4786,15.452c0,7.9804,2.1512,9.4377,4.4413,9.4377,5.8986,0,12.1673-12.1904,15.0587-17.7883Z"/>
<path fill="url(#meta-gradient2)" d="m24,18.6566c2.4982-4.0249,5.5285-7.1014,9.0214-7.1014s10.4786,4.6263,10.4786,15.452c0,7.9804-2.1512,9.4377-4.4413,9.4377-5.8986,0-12.1673-12.1904-15.0587-17.7883Z"/>
</svg>''',
# Facebook (FB) - for backward compatibility
'FB': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#1877F2" d="M24 5A19 19 0 1 0 24 43A19 19 0 1 0 24 5Z"></path><path fill="#fff" d="M26.572,29.036h4.917l0.772-4.995h-5.69v-2.73c0-2.075,0.678-3.915,2.619-3.915h3.119v-4.359c-0.548-0.074-1.707-0.236-3.897-0.236c-4.573,0-7.254,2.415-7.254,7.917v3.323h-4.701v4.995h4.701v13.729C22.089,42.905,23.032,43,24,43c0.875,0,1.729-0.08,2.572-0.194V29.036z"></path>
</svg>''',
'fb': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#1877F2" d="M24 5A19 19 0 1 0 24 43A19 19 0 1 0 24 5Z"></path><path fill="#fff" d="M26.572,29.036h4.917l0.772-4.995h-5.69v-2.73c0-2.075,0.678-3.915,2.619-3.915h3.119v-4.359c-0.548-0.074-1.707-0.236-3.897-0.236c-4.573,0-7.254,2.415-7.254,7.917v3.323h-4.701v4.995h4.701v13.729C22.089,42.905,23.032,43,24,43c0.875,0,1.729-0.08,2.572-0.194V29.036z"></path>
</svg>''',
'instagram': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
# Instagram (IG) - for backward compatibility
'IG': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<g><path fill="none" stroke="#E4405F" stroke-linecap="round" stroke-linejoin="round" stroke-miterlimit="10" stroke-width="3" d="M30,11H18c-3.9,0-7,3.1-7,7v12c0,3.9,3.1,7,7,7h12c3.9,0,7-3.1,7-7V18C37,14.1,33.9,11,30,11z"></path><circle cx="31" cy="16" r="1" fill="#E4405F"></circle></g>
<g><circle cx="24" cy="24" r="6" fill="none" stroke="#E4405F" stroke-linecap="round" stroke-linejoin="round" stroke-miterlimit="10" stroke-width="3"></circle></g>
</svg>''',
'ig': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<g><path fill="none" stroke="#E4405F" stroke-linecap="round" stroke-linejoin="round" stroke-miterlimit="10" stroke-width="3" d="M30,11H18c-3.9,0-7,3.1-7,7v12c0,3.9,3.1,7,7,7h12c3.9,0,7-3.1,7-7V18C37,14.1,33.9,11,30,11z"></path><circle cx="31" cy="16" r="1" fill="#E4405F"></circle></g>
<g><circle cx="24" cy="24" r="6" fill="none" stroke="#E4405F" stroke-linecap="round" stroke-linejoin="round" stroke-miterlimit="10" stroke-width="3"></circle></g>
</svg>''',
'youtube': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
# YouTube (YT)
'YT': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#FF0000" d="M43.2,33.9c-0.4,2.1-2.1,3.7-4.2,4c-3.3,0.5-8.8,1.1-15,1.1c-6.1,0-11.6-0.6-15-1.1c-2.1-0.3-3.8-1.9-4.2-4C4.4,31.6,4,28.2,4,24c0-4.2,0.4-7.6,0.8-9.9c0.4-2.1,2.1-3.7,4.2-4C12.3,9.6,17.8,9,24,9c6.2,0,11.6,0.6,15,1.1c2.1,0.3,3.8,1.9,4.2,4c0.4,2.3,0.9,5.7,0.9,9.9C44,28.2,43.6,31.6,43.2,33.9z"></path><path fill="#FFF" d="M20 31L20 17 32 24z"></path>
</svg>''',
'yt': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#FF0000" d="M43.2,33.9c-0.4,2.1-2.1,3.7-4.2,4c-3.3,0.5-8.8,1.1-15,1.1c-6.1,0-11.6-0.6-15-1.1c-2.1-0.3-3.8-1.9-4.2-4C4.4,31.6,4,28.2,4,24c0-4.2,0.4-7.6,0.8-9.9c0.4-2.1,2.1-3.7,4.2-4C12.3,9.6,17.8,9,24,9c6.2,0,11.6,0.6,15,1.1c2.1,0.3,3.8,1.9,4.2,4c0.4,2.3,0.9,5.7,0.9,9.9C44,28.2,43.6,31.6,43.2,33.9z"></path><path fill="#FFF" d="M20 31L20 17 32 24z"></path>
</svg>''',
'linkedin': '''<svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" width="32" height="32" viewBox="0 0 48 48">
# LinkedIn (LI)
'LI': '''<svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" width="32" height="32" viewBox="0 0 48 48">
<path fill="#0288D1" d="M42,37c0,2.762-2.238,5-5,5H11c-2.761,0-5-2.238-5-5V11c0-2.762,2.239-5,5-5h26c2.762,0,5,2.238,5,5V37z"></path><path fill="#FFF" d="M12 19H17V36H12zM14.485 17h-.028C12.965 17 12 15.888 12 14.499 12 13.08 12.995 12 14.514 12c1.521 0 2.458 1.08 2.486 2.499C17 15.887 16.035 17 14.485 17zM36 36h-5v-9.099c0-2.198-1.225-3.698-3.192-3.698-1.501 0-2.313 1.012-2.707 1.99C24.957 25.543 25 26.511 25 27v9h-5V19h5v2.616C25.721 20.5 26.85 19 29.738 19c3.578 0 6.261 2.25 6.261 7.274L36 36 36 36z"></path>
</svg>''',
'li': '''<svg xmlns="http://www.w3.org/2000/svg" x="0px" y="0px" width="32" height="32" viewBox="0 0 48 48">
<path fill="#0288D1" d="M42,37c0,2.762-2.238,5-5,5H11c-2.761,0-5-2.238-5-5V11c0-2.762,2.239-5,5-5h26c2.762,0,5,2.238,5,5V37z"></path><path fill="#FFF" d="M12 19H17V36H12zM14.485 17h-.028C12.965 17 12 15.888 12 14.499 12 13.08 12.995 12 14.514 12c1.521 0 2.458 1.08 2.486 2.499C17 15.887 16.035 17 14.485 17zM36 36h-5v-9.099c0-2.198-1.225-3.698-3.192-3.698-1.501 0-2.313 1.012-2.707 1.99C24.957 25.543 25 26.511 25 27v9h-5V19h5v2.616C25.721 20.5 26.85 19 29.738 19c3.578 0 6.261 2.25 6.261 7.274L36 36 36 36z"></path>
</svg>''',
'tiktok': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
# TikTok (TT)
'TT': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#212121" fill-rule="evenodd" d="M10.904,6h26.191C39.804,6,42,8.196,42,10.904v26.191 C42,39.804,39.804,42,37.096,42H10.904C8.196,42,6,39.804,6,37.096V10.904C6,8.196,8.196,6,10.904,6z" clip-rule="evenodd"></path><path fill="#ec407a" fill-rule="evenodd" d="M29.208,20.607c1.576,1.126,3.507,1.788,5.592,1.788v-4.011 c-0.395,0-0.788-0.041-1.174-0.123v3.157c-2.085,0-4.015-0.663-5.592-1.788v8.184c0,4.094-3.321,7.413-7.417,7.413 c-1.528,0-2.949-0.462-4.129-1.254c1.347,1.376,3.225,2.23,5.303,2.23c4.096,0,7.417-3.319,7.417-7.413L29.208,20.607L29.208,20.607 z M30.657,16.561c-0.805-0.879-1.334-2.016-1.449-3.273v-0.516h-1.113C28.375,14.369,29.331,15.734,30.657,16.561L30.657,16.561z M19.079,30.832c-0.45-0.59-0.693-1.311-0.692-2.053c0-1.873,1.519-3.391,3.393-3.391c0.349,0,0.696,0.053,1.029,0.159v-4.1 c-0.389-0.053-0.781-0.076-1.174-0.068v3.191c-0.333-0.106-0.68-0.159-1.03-0.159c-1.874,0-3.393,1.518-3.393,3.391 C17.213,29.127,17.972,30.274,19.079,30.832z" clip-rule="evenodd"></path><path fill="#fff" fill-rule="evenodd" d="M28.034,19.63c1.576,1.126,3.507,1.788,5.592,1.788v-3.157 c-1.164-0.248-2.194-0.856-2.969-1.701c-1.326-0.827-2.281-2.191-2.561-3.788h-2.923v16.018c-0.007,1.867-1.523,3.379-3.393,3.379 c-1.102,0-2.081-0.525-2.701-1.338c-1.107-0.558-1.866-1.705-1.866-3.029c0-1.873,1.519-3.391,3.393-3.391 c0.359,0,0.705,0.056,1.03,0.159V21.38c-4.024,0.083-7.26,3.369-7.26,7.411c0,2.018,0.806,3.847,2.114,5.183 c1.18,0.792,2.601,1.254,4.129,1.254c4.096,0,7.417-3.319,7.417-7.413L28.034,19.63L28.034,19.63z" clip-rule="evenodd"></path>
</svg>''',
'tt': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#212121" fill-rule="evenodd" d="M10.904,6h26.191C39.804,6,42,8.196,42,10.904v26.191 C42,39.804,39.804,42,37.096,42H10.904C8.196,42,6,39.804,6,37.096V10.904C6,8.196,8.196,6,10.904,6z" clip-rule="evenodd"></path><path fill="#ec407a" fill-rule="evenodd" d="M29.208,20.607c1.576,1.126,3.507,1.788,5.592,1.788v-4.011 c-0.395,0-0.788-0.041-1.174-0.123v3.157c-2.085,0-4.015-0.663-5.592-1.788v8.184c0,4.094-3.321,7.413-7.417,7.413 c-1.528,0-2.949-0.462-4.129-1.254c1.347,1.376,3.225,2.23,5.303,2.23c4.096,0,7.417-3.319,7.417-7.413L29.208,20.607L29.208,20.607 z M30.657,16.561c-0.805-0.879-1.334-2.016-1.449-3.273v-0.516h-1.113C28.375,14.369,29.331,15.734,30.657,16.561L30.657,16.561z M19.079,30.832c-0.45-0.59-0.693-1.311-0.692-2.053c0-1.873,1.519-3.391,3.393-3.391c0.349,0,0.696,0.053,1.029,0.159v-4.1 c-0.389-0.053-0.781-0.076-1.174-0.068v3.191c-0.333-0.106-0.68-0.159-1.03-0.159c-1.874,0-3.393,1.518-3.393,3.391 C17.213,29.127,17.972,30.274,19.079,30.832z" clip-rule="evenodd"></path><path fill="#fff" fill-rule="evenodd" d="M28.034,19.63c1.576,1.126,3.507,1.788,5.592,1.788v-3.157 c-1.164-0.248-2.194-0.856-2.969-1.701c-1.326-0.827-2.281-2.191-2.561-3.788h-2.923v16.018c-0.007,1.867-1.523,3.379-3.393,3.379 c-1.102,0-2.081-0.525-2.701-1.338c-1.107-0.558-1.866-1.705-1.866-3.029c0-1.873,1.519-3.391,3.393-3.391 c0.359,0,0.705,0.056,1.03,0.159V21.38c-4.024,0.083-7.26,3.369-7.26,7.411c0,2.018,0.806,3.847,2.114,5.183 c1.18,0.792,2.601,1.254,4.129,1.254c4.096,0,7.417-3.319,7.417-7.413L28.034,19.63L28.034,19.63z" clip-rule="evenodd"></path>
</svg>''',
'twitter': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#1DA1F2" d="M42,12.429c-1.323,0.586-2.746,0.977-4.247,1.162c1.526-0.906,2.7-2.351,3.251-4.058c-1.428,0.837-3.01,1.452-4.693,1.776C34.967,9.884,33.05,9,30.926,9c-4.08,0-7.387,3.278-7.387,7.32c0,0.572,0.067,1.129,0.193,1.67c-6.138-0.308-11.582-3.226-15.224-7.654c-0.64,1.082-1,2.349-1,3.686c0,2.541,1.301,4.778,3.285,6.096c-1.211-0.037-2.351-0.374-3.349-0.914c0,0.022,0,0.055,0,0.086c0,3.551,2.547,6.508,5.923,7.181c-0.617,0.169-1.269,0.263-1.941,0.263c-0.477,0-0.942-0.054-1.392-0.135c0.94,2.902,3.667,5.023,6.898,5.086c-2.528,1.96-5.712,3.134-9.174,3.134c-0.598,0-1.183-0.034-1.761-0.104C9.268,36.786,13.152,38,17.321,38c13.585,0,21.017-11.156,21.017-20.834c0-0.317-0.01-0.633-0.025-0.945C39.763,15.197,41.013,13.905,42,12.429"></path>
# X (Twitter)
'X': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#000000" d="M42,12.429c-1.323,0.586-2.746,0.977-4.247,1.162c1.526-0.906,2.7-2.351,3.251-4.058c-1.428,0.837-3.01,1.452-4.693,1.776C34.967,9.884,33.05,9,30.926,9c-4.08,0-7.387,3.278-7.387,7.32c0,0.572,0.067,1.129,0.193,1.67c-6.138-0.308-11.582-3.226-15.224-7.654c-0.64,1.082-1,2.349-1,3.686c0,2.541,1.301,4.778,3.285,6.096c-1.211-0.037-2.351-0.374-3.349-0.914c0,0.022,0,0.055,0,0.086c0,3.551,2.547,6.508,5.923,7.181c-0.617,0.169-1.269,0.263-1.941,0.263c-0.477,0-0.942-0.054-1.392-0.135c0.94,2.902,3.667,5.023,6.898,5.086c-2.528,1.96-5.712,3.134-9.174,3.134c-0.598,0-1.183-0.034-1.761-0.104C9.268,36.786,13.152,38,17.321,38c13.585,0,21.017-11.156,21.017-20.834c0-0.317-0.01-0.633-0.025-0.945C39.763,15.197,41.013,13.905,42,12.429"></path>
</svg>''',
'x': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#000000" d="M42,12.429c-1.323,0.586-2.746,0.977-4.247,1.162c1.526-0.906,2.7-2.351,3.251-4.058c-1.428,0.837-3.01,1.452-4.693,1.776C34.967,9.884,33.05,9,30.926,9c-4.08,0-7.387,3.278-7.387,7.32c0,0.572,0.067,1.129,0.193,1.67c-6.138-0.308-11.582-3.226-15.224-7.654c-0.64,1.082-1,2.349-1,3.686c0,2.541,1.301,4.778,3.285,6.096c-1.211-0.037-2.351-0.374-3.349-0.914c0,0.022,0,0.055,0,0.086c0,3.551,2.547,6.508,5.923,7.181c-0.617,0.169-1.269,0.263-1.941,0.263c-0.477,0-0.942-0.054-1.392-0.135c0.94,2.902,3.667,5.023,6.898,5.086c-2.528,1.96-5.712,3.134-9.174,3.134c-0.598,0-1.183-0.034-1.761-0.104C9.268,36.786,13.152,38,17.321,38c13.585,0,21.017-11.156,21.017-20.834c0-0.317-0.01-0.633-0.025-0.945C39.763,15.197,41.013,13.905,42,12.429"></path>
</svg>''',
'google': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#4285F4" d="M43.611,20.083H42V20H24v8h11.303c-1.649,4.657-6.08,8-11.303,8c-6.627,0-12-5.373-12-12c0-6.627,5.373-12,12-12c3.059,0,5.842,1.154,7.961,3.039l5.657-5.657C34.046,6.053,29.268,4,24,4C12.955,4,4,12.955,4,24c0,11.045,8.955,20,20,20c11.045,0,20-8.955,20-20C44,22.659,43.862,21.35,43.611,20.083z"></path><path fill="#FF3D00" d="M6.306,14.691l6.571,4.819C14.655,15.108,18.961,12,24,12c3.059,0,5.842,1.154,7.961,3.039l5.657-5.657C34.046,6.053,29.268,4,24,4C16.318,4,9.656,8.337,6.306,14.691z"></path><path fill="#34A853" d="M24,44c5.166,0,9.86-1.977,13.409-5.192l-6.19-5.238C29.211,35.091,26.715,36,24,36c-5.202,0-9.619-3.317-11.283-7.946l-6.522,5.025C9.505,39.556,16.227,44,24,44z"></path><path fill="#FBBC05" d="M6.306,33.309l6.571-4.819c-1.222-3.467-1.222-7.485,0-10.981L6.306,12.691C4.833,15.683,4,19.729,4,24S4.833,32.317,6.306,33.309z"></path><path fill="#EA4335" d="M43.611,20.083H42V20H24v8h11.303c-0.792,2.237-2.231,4.166-4.087,5.571c0.001-0.001,0.002-0.001,0.003-0.002l6.19,5.238C36.971,39.205,44,34,44,24C44,22.659,43.862,21.35,43.611,20.083z"></path>
# Google (GO)
'GO': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#4285F4" d="M43.611,20.083H42V20H24v8h11.303c-1.649,4.657-6.08,8-11.303,8c-6.627,0-12-5.373-12-12c0-6.627,5.373-12,12-12c3.059,0,5.842,1.154,7.961,3.039l5.657-5.657C34.046,6.053,29.268,4,24,4C12.955,4,4,12.955,4,24c0,11.045,8.955,20,20c11.045,0,20-8.955,20-20C44,22.659,43.862,21.35,43.611,20.083z"></path><path fill="#FF3D00" d="M6.306,14.691l6.571,4.819C14.655,15.108,18.961,12,24,12c3.059,0,5.842,1.154,7.961,3.039l5.657-5.657C34.046,6.053,29.268,4,24,4C16.318,4,9.656,8.337,6.306,14.691z"></path><path fill="#34A853" d="M24,44c5.166,0,9.86-1.977,13.409-5.192l-6.19-5.238C29.211,35.091,26.715,36,24,36c-5.202,0-9.619-3.317-11.283-7.946l-6.522,5.025C9.505,39.556,16.227,44,24,44z"></path><path fill="#FBBC05" d="M6.306,33.309l6.571-4.819c-1.222-3.467-1.222-7.485,0-10.981L6.306,12.691C4.833,15.683,4,19.729,4,24S4.833,32.317,6.306,33.309z"></path><path fill="#EA4335" d="M43.611,20.083H42V20H24v8h11.303c-0.792,2.237-2.231,4.166-4.087,5.571c0.001-0.001,0.002-0.001,0.003-0.002l6.19,5.238C36.971,39.205,44,34,44,24C44,22.659,43.862,21.35,43.611,20.083z"></path>
</svg>''',
'go': '''<svg xmlns="http://www.w3.org/2000/svg" width="32" height="32" viewBox="0 0 48 48">
<path fill="#4285F4" d="M43.611,20.083H42V20H24v8h11.303c-1.649,4.657-6.08,8-11.303,8c-6.627,0-12-5.373-12-12c0-6.627,5.373-12,12-12c3.059,0,5.842,1.154,7.961,3.039l5.657-5.657C34.046,6.053,29.268,4,24,4C12.955,4,4,12.955,4,24c0,11.045,8.955,20,20c11.045,0,20-8.955,20-20C44,22.659,43.862,21.35,43.611,20.083z"></path><path fill="#FF3D00" d="M6.306,14.691l6.571,4.819C14.655,15.108,18.961,12,24,12c3.059,0,5.842,1.154,7.961,3.039l5.657-5.657C34.046,6.053,29.268,4,24,4C16.318,4,9.656,8.337,6.306,14.691z"></path><path fill="#34A853" d="M24,44c5.166,0,9.86-1.977,13.409-5.192l-6.19-5.238C29.211,35.091,26.715,36,24,36c-5.202,0-9.619-3.317-11.283-7.946l-6.522,5.025C9.505,39.556,16.227,44,24,44z"></path><path fill="#FBBC05" d="M6.306,33.309l6.571-4.819c-1.222-3.467-1.222-7.485,0-10.981L6.306,12.691C4.833,15.683,4,19.729,4,24S4.833,32.317,6.306,33.309z"></path><path fill="#EA4335" d="M43.611,20.083H42V20H24v8h11.303c-0.792,2.237-2.231,4.166-4.087,5.571c0.001-0.001,0.002-0.001,0.003-0.002l6.19,5.238C36.971,39.205,44,34,44,24C44,22.659,43.862,21.35,43.611,20.083z"></path>
</svg>'''
}
@register.simple_tag
def social_icon(platform):
"""Return SVG icon for a given social media platform."""
icon = SOCIAL_ICONS.get(platform.lower(), SOCIAL_ICONS.get('facebook'))
return mark_safe(icon)
def social_icon(platform, size=32):
"""Return SVG icon for a given social media platform.
Args:
platform: The platform code (e.g., 'META', 'FB', 'IG', 'YT', etc.)
size: Icon size in pixels (default: 32)
"""
icon_svg = SOCIAL_ICONS.get(platform.upper(), SOCIAL_ICONS.get(platform.lower(), SOCIAL_ICONS.get('FB')))
# Replace width and height attributes with the specified size
icon_svg = icon_svg.replace('width="32"', f'width="{size}"').replace('height="32"', f'height="{size}"')
return mark_safe(icon_svg)

0
apps/social/tests/__init__.py
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209
apps/social/tests/test_analysis.py
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@ -1,209 +0,0 @@
"""
Test script for AI comment analysis using OpenRouter.
"""
import os
import sys
import django
# Setup Django
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
django.setup()
from apps.social.models import SocialMediaComment
from apps.social.services.openrouter_service import OpenRouterService
from apps.social.services.analysis_service import AnalysisService
from django.utils import timezone
from datetime import timedelta
def test_openrouter_service():
"""Test the OpenRouter service."""
print("\n" + "="*60)
print("Testing OpenRouter Service")
print("="*60)
service = OpenRouterService()
# Check if configured
if not service.is_configured():
print("❌ OpenRouter service not configured. Please set OPENROUTER_API_KEY in .env")
return False
print("✅ OpenRouter service configured")
# Test single comment analysis
test_comments = [
{'id': 1, 'text': 'This is an amazing video! I learned so much.'},
{'id': 2, 'text': 'Terrible content, waste of time.'},
{'id': 3, 'text': 'Okay video, could be better.'}
]
print(f"\nAnalyzing {len(test_comments)} test comments...")
result = service.analyze_comments(test_comments)
if result.get('success'):
print("✅ Analysis successful!")
for analysis in result.get('analyses', []):
print(f"\nComment {analysis.get('comment_id')}:")
print(f" Sentiment: {analysis.get('sentiment')}")
print(f" Score: {analysis.get('sentiment_score')}")
print(f" Confidence: {analysis.get('confidence')}")
print(f" Keywords: {analysis.get('keywords')}")
print(f" Topics: {analysis.get('topics')}")
return True
else:
print(f"❌ Analysis failed: {result.get('error')}")
return False
def test_analysis_service():
"""Test the Analysis service."""
print("\n" + "="*60)
print("Testing Analysis Service")
print("="*60)
service = AnalysisService()
# Get statistics
print("\nGetting analysis statistics...")
stats = service.get_analysis_statistics(days=30)
print(f"\n✅ Statistics retrieved:")
print(f" Total comments: {stats['total_comments']}")
print(f" Analyzed: {stats['analyzed_comments']}")
print(f" Unanalyzed: {stats['unanalyzed_comments']}")
print(f" Analysis rate: {stats['analysis_rate']:.2f}%")
print(f" Sentiment distribution: {stats['sentiment_distribution']}")
print(f" Average confidence: {stats['average_confidence']:.4f}")
# Get top keywords
print("\nGetting top keywords...")
keywords = service.get_top_keywords(limit=10, days=30)
print(f"✅ Top {len(keywords)} keywords:")
for i, kw in enumerate(keywords[:5], 1):
print(f" {i}. {kw['keyword']} ({kw['count']} mentions)")
return True
def test_database_queries():
"""Test database queries for analyzed comments."""
print("\n" + "="*60)
print("Testing Database Queries")
print("="*60)
# Count total comments
total = SocialMediaComment.objects.count()
print(f"\nTotal comments in database: {total}")
# Count analyzed comments (those with ai_analysis populated)
analyzed_count = 0
for comment in SocialMediaComment.objects.all():
if comment.ai_analysis and comment.ai_analysis != {}:
analyzed_count += 1
print(f"Analyzed comments: {analyzed_count}")
# Count unanalyzed comments
unanalyzed_count = total - analyzed_count
print(f"Unanalyzed comments: {unanalyzed_count}")
# Get recent analyzed comments
recent_analyzed = SocialMediaComment.objects.all()
# Filter for analyzed comments and sort by scraped_at
analyzed_list = []
for comment in recent_analyzed:
if comment.ai_analysis and comment.ai_analysis != {}:
analyzed_list.append(comment)
analyzed_list.sort(key=lambda x: x.scraped_at or timezone.now(), reverse=True)
recent_analyzed = analyzed_list[:5]
print(f"\nRecent analyzed comments:")
for comment in recent_analyzed:
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en', 'N/A') if comment.ai_analysis else 'N/A'
confidence = comment.ai_analysis.get('sentiment', {}).get('confidence', 0) if comment.ai_analysis else 0
print(f" - {comment.platform}: {sentiment} (confidence: {confidence})")
return True
def test_pending_analysis():
"""Test analyzing pending comments."""
print("\n" + "="*60)
print("Testing Pending Comment Analysis")
print("="*60)
service = AnalysisService()
# Get count of pending comments (using ai_analysis check)
pending_count = 0
for comment in SocialMediaComment.objects.all():
if not comment.ai_analysis or comment.ai_analysis == {}:
pending_count += 1
print(f"\nPending comments to analyze: {pending_count}")
if pending_count == 0:
print("ℹ️ No pending comments to analyze")
return True
# Analyze a small batch (limit to 5 for testing)
print(f"\nAnalyzing up to 5 pending comments...")
result = service.analyze_pending_comments(limit=5)
if result.get('success'):
print(f"✅ Analysis complete:")
print(f" Analyzed: {result['analyzed']}")
print(f" Failed: {result['failed']}")
print(f" Skipped: {result['skipped']}")
return True
else:
print(f"❌ Analysis failed: {result.get('error')}")
return False
def main():
"""Run all tests."""
print("\n" + "="*60)
print("AI Comment Analysis Test Suite")
print("="*60)
results = []
# Test 1: OpenRouter Service
results.append(('OpenRouter Service', test_openrouter_service()))
# Test 2: Analysis Service
results.append(('Analysis Service', test_analysis_service()))
# Test 3: Database Queries
results.append(('Database Queries', test_database_queries()))
# Test 4: Pending Analysis
results.append(('Pending Analysis', test_pending_analysis()))
# Summary
print("\n" + "="*60)
print("Test Summary")
print("="*60)
for test_name, passed in results:
status = "✅ PASSED" if passed else "❌ FAILED"
print(f"{test_name}: {status}")
all_passed = all(result[1] for result in results)
print("\n" + "="*60)
if all_passed:
print("✅ All tests passed!")
else:
print("❌ Some tests failed")
print("="*60 + "\n")
return all_passed
if __name__ == '__main__':
main()

198
apps/social/tests/test_celery.py
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#!/usr/bin/env python
"""
Test script to verify Celery configuration and task registration.
Run this script to test if Celery is properly set up.
"""
import os
import sys
import django
# Setup Django
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
django.setup()
from celery import current_app
from apps.social import tasks
import redis
def test_redis_connection():
"""Test Redis connection."""
print("=" * 60)
print("Testing Redis Connection...")
print("=" * 60)
try:
r = redis.Redis(host='localhost', port=6379, db=0)
r.ping()
print("✅ Redis is running and accessible")
return True
except redis.ConnectionError as e:
print(f"❌ Redis connection failed: {e}")
print(" Make sure Redis server is running: sudo systemctl start redis")
return False
except Exception as e:
print(f"❌ Unexpected error: {e}")
return False
def test_celery_config():
"""Test Celery configuration."""
print("\n" + "=" * 60)
print("Testing Celery Configuration...")
print("=" * 60)
try:
# Check broker URL
broker_url = current_app.conf.broker_url
print(f"✅ Broker URL: {broker_url}")
# Check result backend
result_backend = current_app.conf.result_backend
print(f"✅ Result Backend: {result_backend}")
# Check timezone
timezone = current_app.conf.timezone
print(f"✅ Timezone: {timezone}")
# Check task serialization
task_serializer = current_app.conf.task_serializer
print(f"✅ Task Serializer: {task_serializer}")
return True
except Exception as e:
print(f"❌ Configuration error: {e}")
return False
def test_task_registration():
"""Test if tasks are registered."""
print("\n" + "=" * 60)
print("Testing Task Registration...")
print("=" * 60)
registered_tasks = []
# Define expected tasks
expected_tasks = [
'apps.social.tasks.scrape_youtube_comments',
'apps.social.tasks.scrape_facebook_comments',
'apps.social.tasks.scrape_instagram_comments',
'apps.social.tasks.scrape_all_platforms',
'apps.social.tasks.analyze_pending_comments',
'apps.social.tasks.analyze_recent_comments',
'apps.social.tasks.analyze_platform_comments',
]
# Get registered tasks
all_tasks = current_app.tasks.keys()
for task_name in expected_tasks:
if task_name in all_tasks:
print(f"✅ {task_name}")
registered_tasks.append(task_name)
else:
print(f"❌ {task_name} - NOT REGISTERED")
print(f"\nTotal registered: {len(registered_tasks)}/{len(expected_tasks)}")
return len(registered_tasks) == len(expected_tasks)
def test_schedules():
"""Test Celery Beat schedules."""
print("\n" + "=" * 60)
print("Testing Celery Beat Schedules...")
print("=" * 60)
try:
schedules = current_app.conf.beat_schedule
if not schedules:
print("❌ No schedules defined")
return False
print(f"✅ {len(schedules)} schedules defined:\n")
for name, config in schedules.items():
task = config.get('task', 'N/A')
schedule = config.get('schedule', 'N/A')
print(f" • {name}")
print(f" Task: {task}")
print(f" Schedule: {schedule}")
print()
return True
except Exception as e:
print(f"❌ Schedule error: {e}")
return False
def test_task_import():
"""Test if tasks module can be imported."""
print("\n" + "=" * 60)
print("Testing Task Module Import...")
print("=" * 60)
try:
# Check if all task functions exist
task_functions = [
'scrape_youtube_comments',
'scrape_facebook_comments',
'scrape_instagram_comments',
'scrape_all_platforms',
'analyze_pending_comments',
'analyze_recent_comments',
'analyze_platform_comments',
]
for func_name in task_functions:
if hasattr(tasks, func_name):
func = getattr(tasks, func_name)
print(f"✅ {func_name}")
else:
print(f"❌ {func_name} - NOT FOUND")
return True
except Exception as e:
print(f"❌ Import error: {e}")
return False
def main():
"""Run all tests."""
print("\n" + "=" * 60)
print("CELERY SETUP TEST")
print("=" * 60)
results = {
'Redis Connection': test_redis_connection(),
'Celery Configuration': test_celery_config(),
'Task Registration': test_task_registration(),
'Schedules': test_schedules(),
'Task Module Import': test_task_import(),
}
# Summary
print("\n" + "=" * 60)
print("TEST SUMMARY")
print("=" * 60)
passed = sum(1 for result in results.values() if result)
total = len(results)
for test_name, result in results.items():
status = "✅ PASS" if result else "❌ FAIL"
print(f"{test_name}: {status}")
print("\n" + "=" * 60)
print(f"Overall: {passed}/{total} tests passed")
print("=" * 60)
if passed == total:
print("\n🎉 All tests passed! Celery is properly configured.")
print("\nNext steps:")
print("1. Start Redis (if not running): sudo systemctl start redis")
print("2. Start Celery Worker: celery -A PX360 worker --loglevel=info")
print("3. Start Celery Beat: celery -A PX360 beat --loglevel=info")
print("4. Or run both together: celery -A PX360 worker --beat --loglevel=info")
return 0
else:
print("\n⚠️ Some tests failed. Please check the errors above.")
return 1
if __name__ == '__main__':
sys.exit(main())

59
apps/social/tests/test_celery_config.py
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@ -1,59 +0,0 @@
#!/usr/bin/env python
"""
Test script to verify Celery Beat configuration is correct.
"""
import os
import sys
import django
# Set Django settings
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
django.setup()
from celery.schedules import crontab
from config.celery import app
def test_celery_config():
"""Test that Celery configuration is valid."""
print("Testing Celery Beat configuration...")
print("-" * 60)
# Check beat_schedule
beat_schedule = app.conf.beat_schedule
print(f"Total scheduled tasks: {len(beat_schedule)}")
print()
all_valid = True
for task_name, task_config in beat_schedule.items():
print(f"Task: {task_name}")
print(f" - Target: {task_config['task']}")
schedule = task_config['schedule']
# Check if schedule is properly configured
if isinstance(schedule, (int, float)):
print(f" - Schedule: {schedule} seconds")
elif isinstance(schedule, crontab):
print(f" - Schedule: Crontab(hour={schedule._hour}, minute={schedule._minute})")
elif isinstance(schedule, dict):
print(f" - ❌ ERROR: Schedule is a dict (should be crontab object)")
print(f" Dict contents: {schedule}")
all_valid = False
else:
print(f" - ⚠️ WARNING: Unknown schedule type: {type(schedule)}")
all_valid = False
print()
print("-" * 60)
if all_valid:
print("✅ All Celery schedules are properly configured!")
print("\nYou can now run:")
print(" celery -A PX360 worker --beat --loglevel=info")
return 0
else:
print("❌ Some Celery schedules are misconfigured!")
return 1
if __name__ == '__main__':
sys.exit(test_celery_config())

163
apps/social/tests/test_google_reviews.py
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@ -1,163 +0,0 @@
"""
Test script for Google Reviews scraper.
This script demonstrates how to use the Google Reviews scraper to extract reviews
from a specified Google My Business account locations.
"""
import os
import sys
import django
# Setup Django
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
django.setup()
from apps.social.scrapers import GoogleReviewsScraper
from django.conf import settings
def test_google_reviews_scraper():
"""
Test the Google Reviews scraper with configuration from Django settings.
"""
# Configuration - pulled from settings/base.py via Django settings
credentials_file = getattr(settings, 'GOOGLE_CREDENTIALS_FILE', 'client_secret.json')
token_file = getattr(settings, 'GOOGLE_TOKEN_FILE', 'token.json')
if not os.path.exists(credentials_file):
print("❌ ERROR: GOOGLE_CREDENTIALS_FILE not found")
print(f"\nExpected file: {credentials_file}")
print("\nPlease download your client_secret.json from Google Cloud Console:")
print("1. Go to https://console.cloud.google.com/")
print("2. Create a new project or select existing")
print("3. Enable Google My Business API")
print("4. Create OAuth 2.0 credentials")
print("5. Download client_secret.json")
return
print("=" * 80)
print("⭐ GOOGLE REVIEWS SCRAPER TEST")
print("=" * 80)
# Initialize scraper
print(f"\n📝 Initializing Google Reviews scraper...")
scraper_config = {
'credentials_file': credentials_file,
'token_file': token_file
}
try:
scraper = GoogleReviewsScraper(scraper_config)
print("✅ Scraper initialized successfully")
except Exception as e:
print(f"❌ Error initializing scraper: {e}")
return
# Scrape reviews
print(f"\n🚀 Starting to scrape Google Reviews...")
print(" - Maximum reviews per location: 100")
print(" - All locations will be scraped")
print()
try:
reviews = scraper.scrape_comments(max_reviews_per_location=100)
if not reviews:
print("⚠️ No reviews found")
print("\nPossible reasons:")
print(" - No locations associated with your Google My Business account")
print(" - Locations have no reviews")
print(" - Invalid credentials or insufficient permissions")
print(" - API rate limit reached")
return
print(f"✅ Successfully scraped {len(reviews)} reviews!")
# Display sample reviews
print("\n" + "=" * 80)
print("📊 SAMPLE REVIEWS (showing first 5)")
print("=" * 80)
for i, review in enumerate(reviews[:5], 1):
print(f"\n--- Review {i} ---")
print(f"ID: {review['comment_id']}")
print(f"Author: {review['author']}")
print(f"Published: {review['published_at']}")
print(f"Location: {review['raw_data']['location_display_name']}")
print(f"Rating: {review['raw_data'].get('star_rating', 'N/A')}")
print(f"Reply: {'Yes' if review['reply_count'] > 0 else 'No'}")
print(f"Text: {review['comments'][:100]}...")
if review.get('raw_data', {}).get('reply_comment'):
print(f"Business Reply: {review['raw_data']['reply_comment'][:100]}...")
# Statistics
print("\n" + "=" * 80)
print("📈 STATISTICS")
print("=" * 80)
print(f"Total reviews: {len(reviews)}")
print(f"Unique reviewers: {len(set(r['author'] for r in reviews))}")
# Location distribution
print("\nReviews by Location:")
location_stats = {}
for review in reviews:
location = review['raw_data']['location_display_name'] or 'Unknown'
location_stats[location] = location_stats.get(location, 0) + 1
for location, count in sorted(location_stats.items()):
print(f" - {location}: {count} reviews")
# Rating distribution
print("\nRating Distribution:")
rating_stats = {}
for review in reviews:
rating = review['raw_data'].get('star_rating', 'N/A')
rating_stats[rating] = rating_stats.get(rating, 0) + 1
for rating, count in sorted(rating_stats.items()):
print(f" - {rating} stars: {count} reviews")
# Reply statistics
reviews_with_replies = sum(1 for r in reviews if r['reply_count'] > 0)
print(f"\nReviews with business replies: {reviews_with_replies} ({reviews_with_replies/len(reviews)*100:.1f}%)")
# Save to CSV
import pandas as pd
df = pd.DataFrame(reviews)
csv_filename = 'google_reviews_export.csv'
# Add readable columns
df['location_name'] = df['raw_data'].apply(lambda x: x.get('location_display_name', ''))
df['star_rating'] = df['raw_data'].apply(lambda x: x.get('star_rating', ''))
df['has_reply'] = df['reply_count'].apply(lambda x: 'Yes' if x > 0 else 'No')
df.to_csv(csv_filename, index=False)
print(f"\n💾 Reviews saved to: {csv_filename}")
# Query by location example
print("\n" + "=" * 80)
print("🔍 QUERY BY LOCATION")
print("=" * 80)
print("You can query reviews by location using the raw_data field:")
print("\nExample SQL query:")
print(" SELECT * FROM social_socialmediacomment")
print(" WHERE platform = 'google_reviews'")
print(" AND json_extract(raw_data, '$.location_display_name') = 'Your Location Name';")
print("\nExample Django query:")
print(" from social.models import SocialMediaComment")
print(" location_reviews = SocialMediaComment.objects.filter(")
print(" platform='google_reviews',")
print(" raw_data__location_display_name='Your Location Name'")
print(" )")
except Exception as e:
print(f"❌ Error scraping Google Reviews: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_google_reviews_scraper()

120
apps/social/tests/test_linkedin.py
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"""
Test script for LinkedIn comment scraper.
This script demonstrates how to use the LinkedIn scraper to extract comments
from a specified organization's posts.
"""
import os
import sys
import django
# Setup Django
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
django.setup()
from apps.social.scrapers import LinkedInScraper
from django.conf import settings
def test_linkedin_scraper():
"""
Test the LinkedIn scraper with configuration from Django settings.
"""
# Configuration - pulled from settings/base.py via Django settings
access_token = getattr(settings, 'LINKEDIN_ACCESS_TOKEN', None)
organization_id = getattr(settings, 'LINKEDIN_ORGANIZATION_ID', 'urn:li:organization:1337')
if not access_token:
print("❌ ERROR: LINKEDIN_ACCESS_TOKEN not found in environment variables")
print("\nPlease set LINKEDIN_ACCESS_TOKEN in your .env file:")
print("LINKEDIN_ACCESS_TOKEN=your_linkedin_access_token_here")
print("\nTo get an access token:")
print("1. Go to https://www.linkedin.com/developers/")
print("2. Create an application")
print("3. Get your access token from the OAuth 2.0 flow")
return
print("=" * 80)
print("💼 LINKEDIN COMMENT SCRAPER TEST")
print("=" * 80)
# Initialize scraper
print(f"\n📝 Initializing LinkedIn scraper for {organization_id}...")
scraper_config = {
'access_token': access_token,
'organization_id': organization_id
}
try:
scraper = LinkedInScraper(scraper_config)
print("✅ Scraper initialized successfully")
except Exception as e:
print(f"❌ Error initializing scraper: {e}")
return
# Scrape comments
print(f"\n🚀 Starting to scrape comments from organization posts...")
print(" - Maximum posts: 50")
print(" - Maximum comments per post: 100")
print()
try:
comments = scraper.scrape_comments(
organization_id=organization_id,
max_posts=50,
max_comments_per_post=100
)
if not comments:
print("⚠️ No comments found")
print("\nPossible reasons:")
print(" - Organization has no public posts")
print(" - No comments found on posts")
print(" - Invalid access token or organization ID")
print(" - API rate limit reached")
return
print(f"✅ Successfully scraped {len(comments)} comments!")
# Display sample comments
print("\n" + "=" * 80)
print("📊 SAMPLE COMMENTS (showing first 5)")
print("=" * 80)
for i, comment in enumerate(comments[:5], 1):
print(f"\n--- Comment {i} ---")
print(f"ID: {comment['comment_id']}")
print(f"Author: {comment['author']}")
print(f"Published: {comment['published_at']}")
print(f"Post ID: {comment['post_id']}")
print(f"Likes: {comment['like_count']}")
print(f"Text: {comment['comments'][:100]}...")
if comment.get('raw_data'):
print(f"Raw Data: {str(comment['raw_data'])[:80]}...")
# Statistics
print("\n" + "=" * 80)
print("📈 STATISTICS")
print("=" * 80)
print(f"Total comments: {len(comments)}")
print(f"Unique authors: {len(set(c['author'] for c in comments))}")
print(f"Total likes on all comments: {sum(c['like_count'] for c in comments)}")
# Save to CSV
import pandas as pd
df = pd.DataFrame(comments)
csv_filename = f"{organization_id.replace('urn:li:organization:', '')}_linkedin_comments.csv"
df.to_csv(csv_filename, index=False)
print(f"\n💾 Comments saved to: {csv_filename}")
except Exception as e:
print(f"❌ Error scraping LinkedIn: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_linkedin_scraper()

324
apps/social/tests/test_scraping.py
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#!/usr/bin/env python
"""
Test script for social media comment scraper.
Tests both manual scraping and Celery tasks.
"""
import os
import sys
import django
from datetime import datetime, timedelta
# Setup Django
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
django.setup()
from apps.social.services import CommentService
from apps.social.models import SocialMediaComment
from apps.social import tasks
def print_separator(title=""):
"""Print a visual separator."""
print("\n" + "=" * 70)
if title:
print(f" {title}")
print("=" * 70)
print()
def test_manual_scraping():
"""Test manual scraping from all platforms."""
print_separator("TEST 1: MANUAL SCRAPING")
try:
service = CommentService()
# Test YouTube
print("1. Testing YouTube scraping...")
youtube_comments = service.scrape_youtube(save_to_db=True)
print(f" ✓ Fetched {len(youtube_comments)} YouTube comments")
print(f" Note: Run again to see new vs updated counts")
# Test Facebook
print("\n2. Testing Facebook scraping...")
try:
facebook_comments = service.scrape_facebook(save_to_db=True)
print(f" ✓ Fetched {len(facebook_comments)} Facebook comments")
except Exception as e:
print(f" ✗ Facebook scraping failed: {e}")
# Test Instagram
print("\n3. Testing Instagram scraping...")
try:
instagram_comments = service.scrape_instagram(save_to_db=True)
print(f" ✓ Fetched {len(instagram_comments)} Instagram comments")
except Exception as e:
print(f" ✗ Instagram scraping failed: {e}")
# Verify database
print("\n4. Verifying database...")
total_comments = SocialMediaComment.objects.count()
youtube_count = SocialMediaComment.objects.filter(platform='youtube').count()
facebook_count = SocialMediaComment.objects.filter(platform='facebook').count()
instagram_count = SocialMediaComment.objects.filter(platform='instagram').count()
print(f" Total comments in database: {total_comments}")
print(f" - YouTube: {youtube_count}")
print(f" - Facebook: {facebook_count}")
print(f" - Instagram: {instagram_count}")
# Show sample comment
if total_comments > 0:
latest = SocialMediaComment.objects.first()
print(f"\n Latest comment:")
print(f" Platform: {latest.platform}")
print(f" Author: {latest.author}")
print(f" Comment: {latest.comments[:100]}...")
print(f" Likes: {latest.like_count}")
print("\n ✓ Manual scraping test completed successfully!")
print(" ℹ Check logs for new vs updated comment counts")
return True
except Exception as e:
print(f"\n ✗ Error in manual scraping test: {e}")
import traceback
traceback.print_exc()
return False
def test_single_platform():
"""Test scraping a single platform (YouTube)."""
print_separator("TEST 2: SINGLE PLATFORM SCRAPING")
try:
service = CommentService()
print("Scraping YouTube only...")
print("Running TWICE to test duplicate prevention...")
# First run
print("\nFirst run (initial scrape):")
comments1 = service.scrape_youtube(save_to_db=True)
print(f"✓ Fetched {len(comments1)} comments")
# Second run (should show duplicates)
print("\nSecond run (duplicate prevention):")
comments2 = service.scrape_youtube(save_to_db=True)
print(f"✓ Fetched {len(comments2)} comments")
print(" Check logs above - should show '0 new, X updated'")
return True
except Exception as e:
print(f"✗ Error: {e}")
return False
def test_celery_task():
"""Test creating and running a Celery task."""
print_separator("TEST 3: CELERY TASK EXECUTION")
try:
print("1. Creating a Celery task for YouTube scraping...")
# Queue the task using .delay()
result = tasks.scrape_youtube_comments.delay()
print(f" ✓ Task queued with ID: {result.id}")
print(f" ℹ Task status: {result.status}")
# Wait for task to complete (with timeout)
print("\n2. Waiting for task to complete (up to 30 seconds)...")
timeout = 30
elapsed = 0
while not result.ready() and elapsed < timeout:
import time
time.sleep(2)
elapsed += 2
print(f" Waiting... ({elapsed}s)")
if result.ready():
if result.successful():
task_result = result.get()
print(f"\n3. Task completed successfully!")
print(f" ✓ Task result: {task_result}")
if isinstance(task_result, dict):
total = task_result.get('total', 0)
comments = task_result.get('comments', [])
print(f" ✓ Total comments scraped: {total}")
elif isinstance(task_result, list):
print(f" ✓ Comments scraped: {len(task_result)}")
print("\n ✓ Celery task test completed successfully!")
return True
else:
print(f"\n ✗ Task failed!")
print(f" Error: {result.result}")
return False
else:
print(f"\n ⚠ Task did not complete within {timeout} seconds")
print(f" ℹ Task status: {result.status}")
print(f" ℹ This is normal if Celery worker is not running")
print(f" ℹ Start Celery worker: celery -A config worker --loglevel=info")
return False
except Exception as e:
print(f" ✗ Error in Celery task test: {e}")
import traceback
traceback.print_exc()
return False
def test_celery_all_platforms_task():
"""Test Celery task for scraping all platforms."""
print_separator("TEST 4: CELERY ALL PLATFORMS TASK")
try:
print("1. Creating a Celery task for scraping all platforms...")
# Queue the task
result = tasks.scrape_all_platforms.delay()
print(f" ✓ Task queued with ID: {result.id}")
# Check task status without waiting (as this takes longer)
print(f"\n2. Task status: {result.status}")
if result.ready():
if result.successful():
task_result = result.get()
print(f" ✓ Task completed successfully!")
print(f" ✓ Result: {task_result}")
else:
print(f" ✗ Task failed: {result.result}")
else:
print(f" ℹ Task is still running (or worker not started)")
print(f" ℹ This task scrapes all platforms and may take longer")
print(f" ℹ Check Celery logs for progress")
print("\n ✓ All platforms task queued successfully!")
return True
except Exception as e:
print(f" ✗ Error: {e}")
import traceback
traceback.print_exc()
return False
def show_celery_info():
"""Show Celery task information."""
print_separator("CELERY INFORMATION")
try:
print("\nChecking Celery configuration...")
# Try to get task info (this requires Celery to be running)
from celery import current_app
# Show registered tasks
registered_tasks = current_app.tasks
print(f"\nRegistered tasks: {len(registered_tasks)}")
# Show comment scraper tasks
scraper_tasks = [t for t in registered_tasks.keys() if 'tasks' in t.lower()]
if scraper_tasks:
print("\nScraper tasks:")
for task_name in sorted(scraper_tasks):
print(f" ✓ {task_name}")
# Show beat schedules
schedules = current_app.conf.beat_schedule
if schedules:
print(f"\nCelery Beat schedules: {len(schedules)}")
for name, config in schedules.items():
task = config.get('task', 'N/A')
schedule = config.get('schedule', 'N/A')
print(f" • {name}")
print(f" Task: {task}")
print(f" Schedule: {schedule}")
except Exception as e:
print(f"Error getting Celery info: {e}")
print("ℹ This is normal if Celery is not running")
print("ℹ Start Celery: celery -A config worker --beat --loglevel=info")
def show_latest_comments():
"""Show latest comments from database."""
print_separator("LATEST COMMENTS IN DATABASE")
try:
comments = SocialMediaComment.objects.order_by('-scraped_at')[:10]
if not comments.exists():
print("No comments found in database.")
return
for i, comment in enumerate(comments, 1):
print(f"\n{i}. Platform: {comment.platform.upper()}")
print(f" Author: {comment.author or 'Anonymous'}")
print(f" Comment: {comment.comments[:80]}{'...' if len(comment.comments) > 80 else ''}")
print(f" Likes: {comment.like_count} | Scraped: {comment.scraped_at}")
except Exception as e:
print(f"Error fetching comments: {e}")
def main():
"""Run all tests."""
print("\n" + "=" * 70)
print(" SOCIAL MEDIA COMMENT SCRAPER - TEST SUITE (CELERY)")
print("=" * 70)
print("\nThis script will test the scraper functionality with Celery.")
print("Make sure you have:")
print(" 1. Configured your .env file with API keys")
print(" 2. Run database migrations: python manage.py migrate")
print(" 3. (Optional) Redis running: sudo systemctl start redis")
print(" 4. (Optional) Celery worker running: celery -A PX360 worker --loglevel=info")
input("\nPress Enter to start testing...")
# Run tests
results = {
'Manual Scraping': test_manual_scraping(),
'Single Platform': test_single_platform(),
'Celery Task': test_celery_task(),
'All Platforms Task': test_celery_all_platforms_task(),
}
# Show Celery info
show_celery_info()
# Show latest comments
show_latest_comments()
# Summary
print_separator("TEST SUMMARY")
passed = sum(1 for v in results.values() if v)
total = len(results)
for test_name, passed_test in results.items():
status = "✓ PASSED" if passed_test else "✗ FAILED"
print(f"{status}: {test_name}")
print(f"\nTotal: {passed}/{total} tests passed")
print_separator()
print("Testing complete!")
print("\nNext steps:")
print(" - View comments in Django Admin: http://localhost:8000/admin/")
print(" - Check logs: tail -f logs/commentscraper.log")
print(" - Start Celery worker: celery -A config worker --loglevel=info")
print(" - Start Celery Beat: celery -A config beat --loglevel=info")
print(" - Or run both: celery -A config worker --beat --loglevel=info")
print(" - View Celery schedules: python -c 'from config.celery import app; print(app.conf.beat_schedule)'")
print()
if __name__ == '__main__':
main()

119
apps/social/tests/test_twitter.py
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@ -1,119 +0,0 @@
"""
Test script for Twitter/X comment scraper.
This script demonstrates how to use the Twitter scraper to extract replies
from a specified user's tweets.
"""
import os
import sys
import django
# Setup Django
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'config.settings')
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
django.setup()
from apps.social.scrapers import TwitterScraper
from django.conf import settings
def test_twitter_scraper():
"""
Test the Twitter scraper with configuration from Django settings.
"""
# Configuration - pulled from settings/base.py via Django settings
bearer_token = getattr(settings, 'TWITTER_BEARER_TOKEN', None)
username = getattr(settings, 'TWITTER_USERNAME', 'elonmusk')
if not bearer_token:
print("❌ ERROR: TWITTER_BEARER_TOKEN not found in environment variables")
print("\nPlease set TWITTER_BEARER_TOKEN in your .env file:")
print("TWITTER_BEARER_TOKEN=your_twitter_bearer_token_here")
print("\nTo get a bearer token:")
print("1. Go to https://developer.twitter.com/en/portal/dashboard")
print("2. Create a project and app")
print("3. Get your bearer token from the Keys and tokens section")
return
print("=" * 80)
print("🐦 TWITTER/X COMMENT SCRAPER TEST")
print("=" * 80)
# Initialize scraper
print(f"\n📝 Initializing Twitter scraper for @{username}...")
scraper_config = {
'bearer_token': bearer_token,
'username': username
}
try:
scraper = TwitterScraper(scraper_config)
print("✅ Scraper initialized successfully")
except Exception as e:
print(f"❌ Error initializing scraper: {e}")
return
# Scrape comments
print(f"\n🚀 Starting to scrape replies from @{username}...")
print(" - Maximum tweets: 50")
print(" - Maximum replies per tweet: 100")
print()
try:
comments = scraper.scrape_comments(
username=username,
max_tweets=50,
max_replies_per_tweet=100
)
if not comments:
print("⚠️ No comments found")
print("\nPossible reasons:")
print(" - User has no public tweets")
print(" - No replies found on tweets")
print(" - API rate limit reached")
return
print(f"✅ Successfully scraped {len(comments)} comments!")
# Display sample comments
print("\n" + "=" * 80)
print("📊 SAMPLE COMMENTS (showing first 5)")
print("=" * 80)
for i, comment in enumerate(comments[:5], 1):
print(f"\n--- Comment {i} ---")
print(f"ID: {comment['comment_id']}")
print(f"Author: {comment['author']}")
print(f"Published: {comment['published_at']}")
print(f"Original Tweet ID: {comment['post_id']}")
print(f"Likes: {comment['like_count']}")
print(f"Text: {comment['comments'][:100]}...")
if comment.get('raw_data'):
print(f"Original Tweet: {comment['raw_data'].get('original_tweet_text', 'N/A')[:80]}...")
# Statistics
print("\n" + "=" * 80)
print("📈 STATISTICS")
print("=" * 80)
print(f"Total comments: {len(comments)}")
print(f"Unique authors: {len(set(c['author'] for c in comments))}")
print(f"Total likes on all comments: {sum(c['like_count'] for c in comments)}")
# Save to CSV
import pandas as pd
df = pd.DataFrame(comments)
csv_filename = f"{username}_twitter_comments.csv"
df.to_csv(csv_filename, index=False)
print(f"\n💾 Comments saved to: {csv_filename}")
except Exception as e:
print(f"❌ Error scraping Twitter: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_twitter_scraper()

641
apps/social/ui_views.py
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@ -1,641 +0,0 @@
"""
Social Media UI views - Server-rendered templates for social media monitoring
"""
from django.contrib import messages
from django.contrib.auth.decorators import login_required
from django.core.paginator import Paginator
from django.db.models import Q, Count, Avg, Sum
from django.http import JsonResponse
from django.shortcuts import render, redirect
from django.views.decorators.http import require_http_methods
from .models import SocialMediaComment, SocialPlatform
@login_required
def social_comment_list(request):
"""
Social media comments list view with advanced filters and pagination.
Features:
- Server-side pagination
- Advanced filters (platform, sentiment, date range, etc.)
- Search by comment text, author
- Export capability
"""
# Base queryset
queryset = SocialMediaComment.objects.all()
# Apply filters from request
platform_filter = request.GET.get('platform')
if platform_filter:
queryset = queryset.filter(platform=platform_filter)
sentiment_filter = request.GET.get('sentiment')
if sentiment_filter:
# Filter by sentiment in ai_analysis JSONField
queryset = queryset.filter(
ai_analysis__sentiment__classification__en=sentiment_filter
)
analyzed_filter = request.GET.get('analyzed')
if analyzed_filter == 'true':
queryset = queryset.exclude(ai_analysis__isnull=True).exclude(ai_analysis={})
elif analyzed_filter == 'false':
queryset = queryset.filter(ai_analysis__isnull=True) | queryset.filter(ai_analysis={})
# Date range filters
date_from = request.GET.get('date_from')
if date_from:
queryset = queryset.filter(published_at__gte=date_from)
date_to = request.GET.get('date_to')
if date_to:
queryset = queryset.filter(published_at__lte=date_to)
# Minimum likes
min_likes = request.GET.get('min_likes')
if min_likes:
queryset = queryset.filter(like_count__gte=min_likes)
# Search
search_query = request.GET.get('search')
if search_query:
queryset = queryset.filter(
Q(comments__icontains=search_query) |
Q(author__icontains=search_query) |
Q(comment_id__icontains=search_query)
)
# Ordering
order_by = request.GET.get('order_by', '-published_at')
queryset = queryset.order_by(order_by)
# Pagination
page_size = int(request.GET.get('page_size', 25))
paginator = Paginator(queryset, page_size)
page_number = request.GET.get('page', 1)
page_obj = paginator.get_page(page_number)
# Get platform choices
platforms = SocialPlatform.choices
# Calculate statistics from queryset (using ai_analysis)
total_comments = queryset.count()
analyzed_comments = 0
positive_count = 0
negative_count = 0
neutral_count = 0
for comment in queryset:
if comment.ai_analysis:
analyzed_comments += 1
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en', 'neutral')
if sentiment == 'positive':
positive_count += 1
elif sentiment == 'negative':
negative_count += 1
else:
neutral_count += 1
stats = {
'total': total_comments,
'analyzed': analyzed_comments,
'unanalyzed': total_comments - analyzed_comments,
'positive': positive_count,
'negative': negative_count,
'neutral': neutral_count,
}
# Add platform-specific counts
for platform_code, platform_name in platforms:
stats[platform_code] = SocialMediaComment.objects.filter(platform=platform_code).count()
context = {
'page_obj': page_obj,
'comments': page_obj.object_list,
'stats': stats,
'platforms': platforms,
'filters': request.GET,
}
return render(request, 'social/social_comment_list.html', context)
@login_required
def social_comment_detail(request, pk):
"""
Social media comment detail view.
Features:
- Full comment details
- Raw data view
- AI analysis results
- Keywords and topics
- Entities extracted
"""
from django.shortcuts import get_object_or_404
comment = get_object_or_404(SocialMediaComment, pk=pk)
context = {
'comment': comment,
}
return render(request, 'social/social_comment_detail.html', context)
@login_required
def social_platform(request, platform):
"""
Platform-specific social media comments view.
Features:
- Filtered comments for specific platform
- Platform-specific branding and metrics
- Time-based filtering
- Platform-specific trends
"""
# Validate platform
valid_platforms = [choice[0] for choice in SocialPlatform.choices]
if platform not in valid_platforms:
messages.error(request, f"Invalid platform: {platform}")
return redirect('social:social_comment_list')
# Base queryset filtered by platform
queryset = SocialMediaComment.objects.filter(platform=platform)
# Apply additional filters
sentiment_filter = request.GET.get('sentiment')
if sentiment_filter:
queryset = queryset.filter(
ai_analysis__sentiment__classification__en=sentiment_filter
)
date_from = request.GET.get('date_from')
if date_from:
queryset = queryset.filter(published_at__gte=date_from)
date_to = request.GET.get('date_to')
if date_to:
queryset = queryset.filter(published_at__lte=date_to)
search_query = request.GET.get('search')
if search_query:
queryset = queryset.filter(
Q(comments__icontains=search_query) |
Q(author__icontains=search_query)
)
# Time-based view filter
time_filter = request.GET.get('time_filter', 'all')
from datetime import datetime, timedelta
if time_filter == 'today':
queryset = queryset.filter(published_at__date=datetime.now().date())
elif time_filter == 'week':
queryset = queryset.filter(published_at__gte=datetime.now() - timedelta(days=7))
elif time_filter == 'month':
queryset = queryset.filter(published_at__gte=datetime.now() - timedelta(days=30))
# Ordering
order_by = request.GET.get('order_by', '-published_at')
queryset = queryset.order_by(order_by)
# Pagination
page_size = int(request.GET.get('page_size', 25))
paginator = Paginator(queryset, page_size)
page_number = request.GET.get('page', 1)
page_obj = paginator.get_page(page_number)
# Platform-specific statistics (using ai_analysis)
total_comments = queryset.count()
analyzed_comments = 0
positive_count = 0
negative_count = 0
neutral_count = 0
sentiment_scores = []
for comment in queryset:
if comment.ai_analysis:
analyzed_comments += 1
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en', 'neutral')
score = comment.ai_analysis.get('sentiment', {}).get('score', 0)
if sentiment == 'positive':
positive_count += 1
elif sentiment == 'negative':
negative_count += 1
else:
neutral_count += 1
if score:
sentiment_scores.append(score)
avg_sentiment = sum(sentiment_scores) / len(sentiment_scores) if sentiment_scores else 0
stats = {
'total': total_comments,
'analyzed': analyzed_comments,
'positive': positive_count,
'negative': negative_count,
'neutral': neutral_count,
'avg_sentiment': float(avg_sentiment),
'total_likes': int(queryset.aggregate(total=Sum('like_count'))['total'] or 0),
'total_replies': int(queryset.aggregate(total=Sum('reply_count'))['total'] or 0),
}
# Platform name for display
platform_display = dict(SocialPlatform.choices).get(platform, platform)
# Platform color for styling
platform_colors = {
'facebook': '#1877F2',
'instagram': '#C13584',
'youtube': '#FF0000',
'twitter': '#1DA1F2',
'linkedin': '#0077B5',
'tiktok': '#000000',
'google': '#4285F4',
}
platform_color = platform_colors.get(platform, '#6c757d')
context = {
'page_obj': page_obj,
'comments': page_obj.object_list,
'stats': stats,
'platform': platform,
'platform_display': platform_display,
'platform_color': platform_color,
'time_filter': time_filter,
'filters': request.GET,
}
return render(request, 'social/social_platform.html', context)
@login_required
def social_analytics(request):
"""
Social media analytics dashboard.
Features:
- Sentiment distribution
- Platform distribution
- Daily trends
- Top keywords
- Top topics
- Engagement metrics
"""
queryset = SocialMediaComment.objects.all()
# Platform filter
platform_filter = request.GET.get('platform')
if platform_filter:
queryset = queryset.filter(platform=platform_filter)
# Apply date range filter
from datetime import datetime, timedelta
# Check for custom date range first
start_date = request.GET.get('start_date')
end_date = request.GET.get('end_date')
if start_date and end_date:
# Custom date range specified
queryset = queryset.filter(published_at__gte=start_date, published_at__lte=end_date)
else:
# Fall back to preset date range (backwards compatibility)
date_range = int(request.GET.get('date_range', 30))
days_ago = datetime.now() - timedelta(days=date_range)
queryset = queryset.filter(published_at__gte=days_ago)
# Sentiment distribution (from ai_analysis)
sentiment_counts = {'positive': 0, 'negative': 0, 'neutral': 0}
sentiment_scores = {'positive': [], 'negative': [], 'neutral': []}
for comment in queryset:
if comment.ai_analysis:
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en', 'neutral')
score = comment.ai_analysis.get('sentiment', {}).get('score', 0)
if sentiment in sentiment_counts:
sentiment_counts[sentiment] += 1
if score:
sentiment_scores[sentiment].append(score)
sentiment_dist = []
for sentiment, count in sentiment_counts.items():
scores = sentiment_scores[sentiment]
avg_score = sum(scores) / len(scores) if scores else 0
sentiment_dist.append({
'sentiment': sentiment,
'count': count,
'avg_sentiment_score': avg_score
})
# Platform distribution (add platform_display manually) - using ai_analysis
platform_dist = []
for platform in SocialPlatform.choices:
platform_code = platform[0]
platform_name = platform[1]
platform_data = queryset.filter(platform=platform_code)
if platform_data.exists():
# Calculate avg sentiment from ai_analysis
sentiment_scores = []
for comment in platform_data:
if comment.ai_analysis:
score = comment.ai_analysis.get('sentiment', {}).get('score', 0)
if score:
sentiment_scores.append(score)
avg_sentiment = sum(sentiment_scores) / len(sentiment_scores) if sentiment_scores else 0
platform_dist.append({
'platform': platform_code,
'platform_display': platform_name,
'count': platform_data.count(),
'avg_sentiment': float(avg_sentiment),
'total_likes': int(platform_data.aggregate(total=Sum('like_count'))['total'] or 0),
'total_replies': int(platform_data.aggregate(total=Sum('reply_count'))['total'] or 0),
})
# Daily trends (from ai_analysis)
from collections import defaultdict
daily_data = defaultdict(lambda: {'count': 0, 'positive': 0, 'negative': 0, 'neutral': 0, 'total_likes': 0})
for comment in queryset:
if comment.published_at:
day = comment.published_at.date()
daily_data[day]['count'] += 1
daily_data[day]['total_likes'] += comment.like_count
if comment.ai_analysis:
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en', 'neutral')
if sentiment in ['positive', 'negative', 'neutral']:
daily_data[day][sentiment] += 1
daily_trends = [
{
'day': day,
**stats
}
for day, stats in sorted(daily_data.items())
]
# Top keywords (from ai_analysis)
all_keywords = []
for comment in queryset.exclude(ai_analysis__isnull=True).exclude(ai_analysis={}):
keywords = comment.ai_analysis.get('keywords', {}).get('en', [])
all_keywords.extend(keywords)
from collections import Counter
keyword_counts = Counter(all_keywords)
top_keywords = [{'keyword': k, 'count': v} for k, v in keyword_counts.most_common(20)]
# Top topics (from ai_analysis)
all_topics = []
for comment in queryset.exclude(ai_analysis__isnull=True).exclude(ai_analysis={}):
topics = comment.ai_analysis.get('topics', {}).get('en', [])
all_topics.extend(topics)
topic_counts = Counter(all_topics)
top_topics = [{'topic': k, 'count': v} for k, v in topic_counts.most_common(10)]
# Top entities (from ai_analysis)
all_entities = []
for comment in queryset.exclude(ai_analysis__isnull=True).exclude(ai_analysis={}):
entities = comment.ai_analysis.get('entities', [])
for entity in entities:
if isinstance(entity, dict):
text_en = entity.get('text', {}).get('en', entity.get('text'))
if text_en:
all_entities.append(text_en)
entity_counts = Counter(all_entities)
top_entities = [{'entity': k, 'count': v} for k, v in entity_counts.most_common(15)]
# Overall statistics (from ai_analysis)
total_comments = queryset.count()
analyzed_comments = 0
for comment in queryset:
if comment.ai_analysis:
analyzed_comments += 1
# Engagement metrics
engagement_metrics = {
'avg_likes': float(queryset.aggregate(avg=Avg('like_count'))['avg'] or 0),
'avg_replies': float(queryset.aggregate(avg=Avg('reply_count'))['avg'] or 0),
'total_likes': int(queryset.aggregate(total=Sum('like_count'))['total'] or 0),
'total_replies': int(queryset.aggregate(total=Sum('reply_count'))['total'] or 0),
}
context = {
'sentiment_distribution': sentiment_dist,
'platform_distribution': platform_dist,
'daily_trends': daily_trends,
'top_keywords': top_keywords,
'top_topics': top_topics,
'top_entities': top_entities,
'total_comments': total_comments,
'analyzed_comments': analyzed_comments,
'unanalyzed_comments': total_comments - analyzed_comments,
'engagement_metrics': engagement_metrics,
'date_range': int(request.GET.get('date_range', 30)),
'start_date': start_date,
'end_date': end_date,
}
return render(request, 'social/social_analytics.html', context)
@login_required
@require_http_methods(["POST"])
def social_scrape_now(request):
"""
Trigger manual scraping for a platform.
"""
platform = request.POST.get('platform')
if not platform:
messages.error(request, "Please select a platform.")
return redirect('social:social_analytics')
try:
# Trigger Celery task for scraping
from .tasks import scrape_platform_comments
task = scrape_platform_comments.delay(platform)
messages.success(
request,
f"Scraping task initiated for {platform}. Task ID: {task.id}"
)
except Exception as e:
messages.error(request, f"Error initiating scraping: {str(e)}")
return redirect('social:social_analytics')
@login_required
def social_export_csv(request):
"""Export social media comments to CSV"""
import csv
from django.http import HttpResponse
from datetime import datetime
# Get filtered queryset (reuse list view logic)
queryset = SocialMediaComment.objects.all()
# Apply filters
platform_filter = request.GET.get('platform')
if platform_filter:
queryset = queryset.filter(platform=platform_filter)
sentiment_filter = request.GET.get('sentiment')
if sentiment_filter:
queryset = queryset.filter(
ai_analysis__sentiment__classification__en=sentiment_filter
)
date_from = request.GET.get('date_from')
if date_from:
queryset = queryset.filter(published_at__gte=date_from)
date_to = request.GET.get('date_to')
if date_to:
queryset = queryset.filter(published_at__lte=date_to)
# Create CSV response
response = HttpResponse(content_type='text/csv')
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
response['Content-Disposition'] = f'attachment; filename="social_comments_{timestamp}.csv"'
writer = csv.writer(response)
writer.writerow([
'ID', 'Platform', 'Comment ID', 'Author', 'Comment',
'Published At', 'Scraped At', 'Sentiment', 'Sentiment Score',
'Confidence', 'Likes', 'Replies', 'Keywords', 'Topics'
])
for comment in queryset:
# Extract data from ai_analysis
sentiment = None
sentiment_score = None
confidence = None
keywords = []
topics = []
if comment.ai_analysis:
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en')
sentiment_score = comment.ai_analysis.get('sentiment', {}).get('score')
confidence = comment.ai_analysis.get('sentiment', {}).get('confidence')
keywords = comment.ai_analysis.get('keywords', {}).get('en', [])
topics = comment.ai_analysis.get('topics', {}).get('en', [])
writer.writerow([
comment.id,
comment.get_platform_display(),
comment.comment_id,
comment.author,
comment.comments,
comment.published_at,
comment.scraped_at,
sentiment,
sentiment_score,
confidence,
comment.like_count,
comment.reply_count,
', '.join(keywords),
', '.join(topics),
])
return response
@login_required
def social_export_excel(request):
"""Export social media comments to Excel"""
import openpyxl
from django.http import HttpResponse
from datetime import datetime
# Get filtered queryset
queryset = SocialMediaComment.objects.all()
# Apply filters
platform_filter = request.GET.get('platform')
if platform_filter:
queryset = queryset.filter(platform=platform_filter)
sentiment_filter = request.GET.get('sentiment')
if sentiment_filter:
queryset = queryset.filter(
ai_analysis__sentiment__classification__en=sentiment_filter
)
date_from = request.GET.get('date_from')
if date_from:
queryset = queryset.filter(published_at__gte=date_from)
date_to = request.GET.get('date_to')
if date_to:
queryset = queryset.filter(published_at__lte=date_to)
# Create workbook
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "Social Media Comments"
# Headers
headers = [
'ID', 'Platform', 'Comment ID', 'Author', 'Comment',
'Published At', 'Scraped At', 'Sentiment', 'Sentiment Score',
'Confidence', 'Likes', 'Replies', 'Keywords', 'Topics', 'Entities'
]
ws.append(headers)
# Data rows
for comment in queryset:
# Extract data from ai_analysis
sentiment = None
sentiment_score = None
confidence = None
keywords = []
topics = []
entities_text = []
if comment.ai_analysis:
sentiment = comment.ai_analysis.get('sentiment', {}).get('classification', {}).get('en')
sentiment_score = comment.ai_analysis.get('sentiment', {}).get('score')
confidence = comment.ai_analysis.get('sentiment', {}).get('confidence')
keywords = comment.ai_analysis.get('keywords', {}).get('en', [])
topics = comment.ai_analysis.get('topics', {}).get('en', [])
entities = comment.ai_analysis.get('entities', [])
for entity in entities:
if isinstance(entity, dict):
text_en = entity.get('text', {}).get('en', entity.get('text'))
if text_en:
entities_text.append(text_en)
ws.append([
comment.id,
comment.get_platform_display(),
comment.comment_id,
comment.author,
comment.comments,
comment.published_at.strftime('%Y-%m-%d %H:%M:%S') if comment.published_at else '',
comment.scraped_at.strftime('%Y-%m-%d %H:%M:%S') if comment.scraped_at else '',
sentiment,
sentiment_score,
confidence,
comment.like_count,
comment.reply_count,
', '.join(keywords),
', '.join(topics),
', '.join(entities_text),
])
# Create response
response = HttpResponse(
content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
)
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
response['Content-Disposition'] = f'attachment; filename="social_comments_{timestamp}.xlsx"'
wb.save(response)
return response

47
apps/social/urls.py
View File

@ -1,34 +1,31 @@
"""
URL configuration for Social Media app
"""
from django.urls import path, include
from rest_framework.routers import DefaultRouter
from .views import SocialMediaCommentViewSet
from . import ui_views
# social/urls.py - Unified URLs for all platforms
from django.urls import path
from django.views.decorators.csrf import csrf_exempt
from . import views
app_name = 'social'
# API Router
router = DefaultRouter()
router.register(r'api/comments', SocialMediaCommentViewSet, basename='social-comment-api')
urlpatterns = [
# UI Views - Specific paths first
path('', ui_views.social_comment_list, name='social_comment_list'),
path('analytics/', ui_views.social_analytics, name='social_analytics'),
path('scrape/', ui_views.social_scrape_now, name='social_scrape_now'),
# Dashboard
path('', views.dashboard, name='dashboard'),
path('dashboard/<str:platform_type>/', views.dashboard, name='dashboard_platform'),
# Export Views - Must come before catch-all patterns
path('export/csv/', ui_views.social_export_csv, name='social_export_csv'),
path('export/excel/', ui_views.social_export_excel, name='social_export_excel'),
# Comments
path('<str:platform_type>/comments/', views.comments_list, name='comments_list'),
path('<str:platform_type>/comments/export/', views.export_comments_csv, name='export_comments_csv'),
path('<str:platform_type>/comment/<str:comment_id>/', views.comment_detail, name='comment_detail'),
# Platform-specific view
path('<str:platform>/', ui_views.social_platform, name='social_platform'),
# Sync
path('<str:platform_type>/sync/', views.manual_sync, name='sync'),
path('<str:platform_type>/sync/<str:sync_type>/', views.manual_sync, name='sync_type'),
# Comment detail view - Must be LAST to avoid conflicts
path('comment/<int:pk>/', ui_views.social_comment_detail, name='social_comment_detail'),
# OAuth
path('auth/<str:platform_type>/', views.auth_start, name='auth_start'),
path('callback/<str:platform_type>/', views.auth_callback, name='auth_callback'),
# API Routes
path('', include(router.urls)),
# Webhooks
path('webhooks/META/', csrf_exempt(views.meta_webhook), name='meta_webhook'),
# LinkedIn Webhook Path
path('webhooks/linkedin/', csrf_exempt(views.linkedin_webhook), name='linkedin_webhook'),
]

29
apps/social/utils/__init__.py Normal file
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@ -0,0 +1,29 @@
# Social Utils - All Platform Utils
# This module contains utility constants and classes for all social platforms
from .linkedin import LinkedInConstants
from .google import SCOPES, API_VERSION_MYBUSINESS, API_VERSION_ACCOUNT_MGMT
from .meta import BASE_GRAPH_URL, META_SCOPES
from .tiktok import TikTokConstants
from .x import XConfig
from .youtube import YOUTUBE_SCOPES, YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION
__all__ = [
# LinkedIn
'LinkedInConstants',
# Google
'SCOPES',
'API_VERSION_MYBUSINESS',
'API_VERSION_ACCOUNT_MGMT',
# Meta
'BASE_GRAPH_URL',
'META_SCOPES',
# TikTok
'TikTokConstants',
# X
'XConfig',
# YouTube
'YOUTUBE_SCOPES',
'YOUTUBE_API_SERVICE_NAME',
'YOUTUBE_API_VERSION',
]

7
apps/social/utils/google.py Normal file
View File

@ -0,0 +1,7 @@
# social/utils/google.py
SCOPES = ['https://www.googleapis.com/auth/business.manage']
API_VERSION_MYBUSINESS = 'v4'
API_VERSION_ACCOUNT_MGMT = 'v1'

28
apps/social/utils/linkedin.py Normal file
View File

@ -0,0 +1,28 @@
class LinkedInConstants:
# API Configuration
# Using the versioned base path for Marketing/Community Management APIs
API_VERSION = "202411" # Latest version (YYYYMM format)
BASE_URL = "https://api.linkedin.com/rest"
# Authentication URLs
AUTH_URL = "https://www.linkedin.com/oauth/v2/authorization"
TOKEN_URL = "https://www.linkedin.com/oauth/v2/accessToken"
# Scopes
SCOPES = [
"r_organization_social",
"w_organization_social",
"rw_organization_admin"
]
# Rate Limiting
MAX_RETRIES = 3
RATE_LIMIT_SLEEP = 60 # seconds
# Pagination
DEFAULT_PAGE_SIZE = 50
MAX_PAGE_SIZE = 100
# Sync Configuration
INITIAL_SYNC_COMMENT_LIMIT = 200 # Comments per post for initial sync
DELTA_SYNC_INTERVAL = 900 # 15 minutes in secondsv

19
apps/social/utils/meta.py Normal file
View File

@ -0,0 +1,19 @@
# social/utils/meta.py - RECOMMENDED CONFIGURATION
# Use v24.0 (Latest) but keep it stable
BASE_GRAPH_URL = "https://graph.facebook.com/v24.0"
BASE_AUTH_URL = "https://www.facebook.com/v24.0"
# CRITICAL: Keep ALL scopes from Block 1
META_SCOPES = [
"pages_manage_engagement", # Reply to comments
"pages_read_engagement", # Read comments/reactions
"pages_show_list", # REQUIRED: To discover pages and get Page Access Tokens
"pages_read_user_content", # Read user content
"instagram_basic", # IG Basic info
"instagram_manage_comments", # Manage IG comments
"public_profile", # User profile info
]

27
apps/social/utils/tiktok.py Normal file
View File

@ -0,0 +1,27 @@
# social/utils/tiktok.py
class TikTokConstants:
# Business API Base URL (v1.3 is current stable)
BASE_URL = "https://business-api.tiktok.com/open_api/v1.3/"
# Scopes: Must match your App configuration in TikTok Business Center
# Needs permission for Ads and Comments
SCOPES = "user.info.basic,ad.read,comment.manage"
ENDPOINTS = {
# Auth portal for Business accounts
"AUTH": "https://business-api.tiktok.com/portal/auth",
# Token exchange
"TOKEN": "oauth2/access_token/",
# To get advertiser details
"USER_INFO": "user/info/",
# To fetch Ads (which act as our 'Content')
"AD_LIST": "ad/get/",
# To list comments on Ads
"COMMENT_LIST": "comment/list/",
# To reply to comments on Ads
"COMMENT_REPLY": "comment/reply/",
}
#TikTok Business/Marketing API. This implementation strictly supports Ad Comments (Paid Ads),
# as organic video comment management is not supported by the official API.

16
apps/social/utils/x.py Normal file
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@ -0,0 +1,16 @@
# xcom/utils.py
class XConfig:
BASE_URL = "https://api.twitter.com/2"
AUTH_URL = "https://twitter.com/i/oauth2/authorize"
TOKEN_URL = "https://api.twitter.com/2/oauth2/token"
SCOPES = [
"tweet.read",
"tweet.write",
"users.read",
"offline.access"
]
SEARCH_RECENT_URL = "tweets/search/recent"
SEARCH_ALL_URL = "tweets/search/all"

8
apps/social/utils/youtube.py Normal file
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@ -0,0 +1,8 @@
# youtube/utils.py
YOUTUBE_SCOPES = [
'https://www.googleapis.com/auth/youtube.readonly',
'https://www.googleapis.com/auth/youtube.force-ssl'
]
YOUTUBE_API_SERVICE_NAME = 'youtube'
YOUTUBE_API_VERSION = 'v3'

1140
apps/social/views.py
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84
config/celery.py
View File

@ -77,49 +77,61 @@ app.conf.beat_schedule = {
# Scraping schedules
'scrape-youtube-hourly': {
'task': 'social.tasks.scrape_youtube_comments',
'schedule': 3600.0, # Every hour (in seconds)
},
'scrape-facebook-every-6-hours': {
'task': 'social.tasks.scrape_facebook_comments',
'schedule': 6 * 3600.0, # Every 6 hours
},
'scrape-instagram-daily': {
'task': 'social.tasks.scrape_instagram_comments',
'schedule': crontab(hour=8, minute=0), # Daily at 8:00 AM
},
'scrape-twitter-every-2-hours': {
'task': 'social.tasks.scrape_twitter_comments',
'schedule': 2 * 3600.0, # Every 2 hours
},
'scrape-linkedin-daily': {
'task': 'social.tasks.scrape_linkedin_comments',
'schedule': crontab(hour=9, minute=0), # Daily at 9:00 AM
},
'scrape-google-reviews-daily': {
'task': 'social.tasks.scrape_google_reviews',
'schedule': crontab(hour=10, minute=0), # Daily at 10:00 AM
},
# ==========================================
# Social Media Platform Sync Tasks
# ==========================================
# LinkedIn - Sync comments every 30 minutes
'sync-linkedin-comments-every-30-mins': {
'task': 'apps.social.tasks.linkedin.sync_all_accounts_task',
'schedule': 1800.0, # 30 minutes in seconds
},
# Commented out - individual platform tasks provide sufficient coverage
# 'scrape-all-platforms-daily': {
# 'task': 'social.tasks.scrape_all_platforms',
# 'schedule': crontab(hour=2, minute=0), # Daily at 2:00 AM
# },
# Google Reviews - Sync daily at midnight
'sync-google-reviews-daily': {
'task': 'apps.social.tasks.google.sync_all_accounts_periodic',
'schedule': crontab(hour=0, minute=0),
},
# Analysis schedules
'analyze-comments-fallback': {
'task': 'social.tasks.analyze_pending_comments',
'schedule': 30 * 60.0, # Every 30 minutes
'kwargs': {'limit': 100},
# Meta (Facebook/Instagram) - Poll new comments every 30 minutes
'sync-meta-comments-every-30-mins': {
'task': 'apps.social.tasks.meta.meta_poll_new_comments_task',
'schedule': 1800.0, # 30 minutes in seconds
},
# TikTok - Poll new comments every 30 minutes
'sync-tiktok-comments-every-30-mins': {
'task': 'apps.social.tasks.tiktok.poll_new_comments_task',
'schedule': 1800.0, # 30 minutes in seconds
},
# X (Twitter) - Sync all accounts every 30 minutes
'sync-x-comments-every-30-mins': {
'task': 'apps.social.tasks.x.sync_all_accounts_periodic',
'schedule': 1800.0, # 30 minutes in seconds
},
# YouTube - Poll new comments every 30 minutes
'sync-youtube-comments-every-30-mins': {
'task': 'apps.social.tasks.youtube.poll_new_comments_task',
'schedule': 1800.0, # 30 minutes in seconds
},
# ==========================================
# AI Analysis Tasks
# ==========================================
# AI Analysis - Daily check for unanalyzed comments at 2:00 AM
'ai-analysis-daily': {
'task': 'apps.social.tasks.ai.analyze_pending_comments_task',
'schedule': crontab(hour=2, minute=0),
},
}
app.conf.timezone = 'Asia/Riyadh'
@app.task(bind=True, ignore_result=True)
def debug_task(self):
"""Debug task to test Celery is working."""

69
config/settings/base.py
View File

@ -338,7 +338,7 @@ SLA_DEFAULTS = {
}
# AI Configuration (LiteLLM with OpenRouter)
OPENROUTER_API_KEY = env('OPENROUTER_API_KEY', default='')
OPENROUTER_API_KEY = env('OPENROUTER_API_KEY', default='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')
AI_MODEL = env('AI_MODEL', default='z-ai/glm-4.5-air:free')
AI_TEMPERATURE = env.float('AI_TEMPERATURE', default=0.3)
AI_MAX_TOKENS = env.int('AI_MAX_TOKENS', default=500)
@ -431,31 +431,54 @@ TENANT_ISOLATION_LEVEL = 'strict'
# Social Media API Configuration
YOUTUBE_API_KEY = env('YOUTUBE_API_KEY', default='AIzaSyAem20etP6GkRNMmCyI1pRJF7v8U_xDyMM')
YOUTUBE_CHANNEL_ID = env('YOUTUBE_CHANNEL_ID', default='UCKoEfCXsm4_cQMtqJTvZUVQ')
FACEBOOK_PAGE_ID = env('FACEBOOK_PAGE_ID', default='938104059393026')
FACEBOOK_ACCESS_TOKEN = env('FACEBOOK_ACCESS_TOKEN', default='EAATrDf0UAS8BQWSKbljCUDMbluZBbxZCSWLJkZBGIviBtK8IQ7FDHfGQZBHHm7lsgLhZBL2trT3ZBGPtsWRjntFWQovhkhx726ZBexRZCKitEMhxAiZBmls7uX946432k963Myl6aYBzJzwLhSyygZAFOGP7iIIZANVf6GtLlvAnWn0NXRwZAYR0CNNUwCEEsZAAc')
INSTAGRAM_ACCOUNT_ID = env('INSTAGRAM_ACCOUNT_ID', default='17841431861985364')
INSTAGRAM_ACCESS_TOKEN = env('INSTAGRAM_ACCESS_TOKEN', default='EAATrDf0UAS8BQWSKbljCUDMbluZBbxZCSWLJkZBGIviBtK8IQ7FDHfGQZBHHm7lsgLhZBL2trT3ZBGPtsWRjntFWQovhkhx726ZBexRZCKitEMhxAiZBmls7uX946432k963Myl6aYBzJzwLhSyygZAFOGP7iIIZANVf6GtLlvAnWn0NXRwZAYR0CNNUwCEEsZAAc')
# Twitter/X Configuration
TWITTER_BEARER_TOKEN = env('TWITTER_BEARER_TOKEN', default=None)
TWITTER_USERNAME = env('TWITTER_USERNAME', default=None)
#social media settings
# LinkedIn Configuration
LINKEDIN_ACCESS_TOKEN = env('LINKEDIN_ACCESS_TOKEN', default=None)
LINKEDIN_ORGANIZATION_ID = env('LINKEDIN_ORGANIZATION_ID', default=None)
# LINKEDIN API CREDENTIALS
LINKEDIN_CLIENT_ID = '78eu5csx68y5bn'
LINKEDIN_CLIENT_SECRET ='WPL_AP1.Ek4DeQDXuv4INg1K.mGo4CQ=='
LINKEDIN_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/LI/'
LINKEDIN_WEBHOOK_VERIFY_TOKEN = "your_random_secret_string_123"
# Google Reviews Configuration
GOOGLE_CREDENTIALS_FILE = env('GOOGLE_CREDENTIALS_FILE', default='client_secret.json')
GOOGLE_TOKEN_FILE = env('GOOGLE_TOKEN_FILE', default='token.json')
GOOGLE_LOCATIONS = env.list('GOOGLE_LOCATIONS', default=[])
# OpenRouter Configuration for AI Comment Analysis
OPENROUTER_API_KEY = env('OPENROUTER_API_KEY', default='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')
OPENROUTER_MODEL = env('OPENROUTER_MODEL', default='google/gemma-3-27b-it:free')
ANALYSIS_BATCH_SIZE = env.int('ANALYSIS_BATCH_SIZE', default=2)
ANALYSIS_ENABLED = env.bool('ANALYSIS_ENABLED', default=True)
# YOUTUBE API CREDENTIALS
# Ensure this matches your Google Cloud Console settings
YOUTUBE_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'yt_client_secrets.json'
YOUTUBE_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/YT/'
# Google REVIEWS Configuration
# Ensure you have your client_secrets.json file at this location
GMB_CLIENT_SECRETS_FILE = BASE_DIR / 'secrets' / 'gmb_client_secrets.json'
GMB_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/GO/'
# X API Configuration
X_CLIENT_ID = 'your_client_id'
X_CLIENT_SECRET = 'your_client_secret'
X_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/X/'
# TIER CONFIGURATION
# Set to True if you have Enterprise Access
# Set to False for Free/Basic/Pro
X_USE_ENTERPRISE = False
# --- TIKTOK CONFIG ---
TIKTOK_CLIENT_KEY = 'your_client_key'
TIKTOK_CLIENT_SECRET = 'your_client_secret'
TIKTOK_REDIRECT_URI = 'http://127.0.0.1:8000/social/callback/TT/'
# --- META API CONFIG ---
META_APP_ID = '1229882089053768'
META_APP_SECRET = 'b80750bd12ab7f1c21d7d0ca891ba5ab'
META_REDIRECT_URI = 'https://micha-nonparabolic-lovie.ngrok-free.dev/social/callback/META/'
META_WEBHOOK_VERIFY_TOKEN = 'random_secret_string_khanfaheed123456'

8
config/settings/dev.py
View File

@ -5,7 +5,7 @@ from .base import * # noqa
DEBUG = True
ALLOWED_HOSTS = ['localhost', '127.0.0.1', '0.0.0.0']
ALLOWED_HOSTS = ['localhost', '127.0.0.1', '0.0.0.0','.ngrok-free.app','micha-nonparabolic-lovie.ngrok-free.dev']
# Database - Use PostgreSQL even in dev for consistency
# Override with SQLite if needed for quick local testing
@ -16,6 +16,12 @@ ALLOWED_HOSTS = ['localhost', '127.0.0.1', '0.0.0.0']
# )
# }
CSRF_TRUSTED_ORIGINS = [
"https://*.ngrok-free.app",
"https://*.ngrok.app",
"https://micha-nonparabolic-lovie.ngrok-free.dev"
]
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',

102
requirements.txt
View File

@ -1,116 +1,50 @@
httpx==0.28.1
amqp==5.3.1
anyio==4.12.1
asgiref==3.11.0
asttokens==3.0.1
attrs==25.4.0
billiard==4.2.4
cachetools==6.2.4
celery==5.6.2
certifi==2025.11.12
certifi==2026.1.4
charset-normalizer==3.4.4
click==8.3.1
click-didyoumean==0.3.1
click-plugins==1.1.1.2
click-repl==0.3.0
coverage==7.13.1
cron-descriptor==2.0.6
decorator==5.2.1
Django==6.0
django-environ==0.12.0
cron_descriptor==2.0.6
Django==5.2.10
django-celery-beat==2.8.1
django-crontab==0.7.1
django-extensions==4.1
django-filter==25.1
django-stubs==5.2.8
django-stubs-ext==5.2.8
django-timezone-field==7.2.1
djangorestframework==3.16.1
djangorestframework-stubs==3.16.6
djangorestframework_simplejwt==5.5.1
drf-spectacular==0.29.0
et_xmlfile==2.0.0
executing==2.2.1
google-api-core==2.28.1
google-api-python-client==2.187.0
google-auth==2.41.1
google-api-core==2.29.0
google-api-python-client==2.188.0
google-auth==2.47.0
google-auth-httplib2==0.3.0
google-auth-oauthlib==1.2.3
google-auth-oauthlib==1.2.4
googleapis-common-protos==1.72.0
greenlet==3.3.0
gunicorn==23.0.0
h11==0.16.0
httpcore==1.0.9
httplib2==0.31.0
httpx==0.28.1
httplib2==0.31.2
idna==3.11
inflection==0.5.1
iniconfig==2.3.0
ipython==9.9.0
ipython_pygments_lexers==1.1.1
jedi==0.19.2
jsonschema==4.25.1
jsonschema-specifications==2025.9.1
kombu==5.6.2
matplotlib-inline==0.2.1
numpy==2.4.0
oauthlib==3.3.1
openpyxl==3.1.5
outcome==1.3.0.post0
packaging==25.0
pandas==2.3.3
parso==0.8.5
pexpect==4.9.0
pillow==12.1.0
pluggy==1.6.0
packaging==26.0
prompt_toolkit==3.0.52
proto-plus==1.27.0
protobuf==6.33.2
psycopg2-binary==2.9.11
ptyprocess==0.7.0
pure_eval==0.2.3
pyasn1==0.6.1
protobuf==6.33.4
pyasn1==0.6.2
pyasn1_modules==0.4.2
pyee==13.0.0
Pygments==2.19.2
PyJWT==2.10.1
pyparsing==3.3.1
PySocks==1.7.1
pytest==9.0.2
pytest-cov==7.0.0
pytest-django==4.11.1
pyparsing==3.3.2
python-crontab==3.3.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.1
pytz==2025.2
PyYAML==6.0.3
redis==7.1.0
referencing==0.37.0
reportlab==4.4.7
requests==2.32.5
requests-oauthlib==2.0.0
rpds-py==0.30.0
rsa==4.9.1
ruff==0.14.10
six==1.17.0
sniffio==1.3.1
sortedcontainers==2.4.0
sqlparse==0.5.5
stack-data==0.6.3
traitlets==5.14.3
trio==0.32.0
trio-websocket==0.12.2
tweepy==4.16.0
types-PyYAML==6.0.12.20250915
types-requests==2.32.4.20250913
typing_extensions==4.15.0
tzdata==2025.3
tzlocal==5.3.1
ua-parser==0.18.0
uritemplate==4.2.0
user-agents==2.2.0
urllib3==2.6.2
urllib3==2.6.3
vine==5.1.0
wcwidth==0.2.14
webdriver-manager==4.0.2
websocket-client==1.9.0
whitenoise==6.11.0
wsproto==1.3.2
yt-dlp==2025.12.8
wcwidth==0.3.1
rich==13.9.4

2
templates/layouts/partials/sidebar.html
View File

@ -423,7 +423,7 @@
<!-- Social Media -->
<li class="nav-item">
<a class="nav-link {% if 'social' in request.path %}active{% endif %}"
href="{% url 'social:social_comment_list' %}">
href="{% url 'social:dashboard' %}">
<i class="bi bi-chat-dots"></i>
{% trans "Social Media" %}
</a>