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