# AI Engine Implementation - Complete

## Overview
The AI Engine app has been fully implemented with sentiment analysis capabilities, API endpoints, UI views, and integration-ready architecture.

## Components Implemented

### 1. Service Layer (`services.py`)
- **SentimentAnalysisService**: Core sentiment analysis with stub implementation
  - Language detection (English/Arabic)
  - Sentiment scoring (-1 to +1)
  - Keyword extraction
  - Entity recognition
  - Emotion detection
  - Confidence calculation
- **AIEngineService**: Facade for all AI capabilities
- Ready for integration with OpenAI, Azure, AWS, or custom ML models

### 2. Models (`models.py`)
- **SentimentResult**: Stores sentiment analysis results
  - Generic foreign key for linking to any model
  - Comprehensive fields for sentiment, keywords, entities, emotions
  - Metadata and processing information

### 3. API Layer

#### Serializers (`serializers.py`)
- `SentimentResultSerializer`: Full sentiment result serialization
- `AnalyzeTextRequestSerializer`: Text analysis request validation
- `AnalyzeTextResponseSerializer`: Analysis response formatting
- `BatchAnalyzeRequestSerializer`: Batch analysis requests
- `SentimentStatsSerializer`: Statistics aggregation

#### Views (`views.py`)
- `SentimentResultViewSet`: Read-only API for sentiment results
  - List with filters
  - Retrieve specific result
  - Statistics endpoint
- `analyze_text`: POST endpoint for single text analysis
- `analyze_batch`: POST endpoint for batch analysis
- `get_sentiment_for_object`: GET sentiment for specific object

### 4. UI Layer

#### Forms (`forms.py`)
- `AnalyzeTextForm`: Manual text analysis form
- `SentimentFilterForm`: Advanced filtering for results

#### UI Views (`ui_views.py`)
- `sentiment_list`: List view with pagination and filters
- `sentiment_detail`: Detailed sentiment result view
- `analyze_text_view`: Manual text analysis interface
- `sentiment_dashboard`: Analytics dashboard
- `reanalyze_sentiment`: Re-analyze existing results

#### Templates
- `sentiment_list.html`: Results list with statistics
- `sentiment_detail.html`: Detailed result view
- `analyze_text.html`: Text analysis form
- `sentiment_dashboard.html`: Analytics dashboard

### 5. Utilities (`utils.py`)
- Badge and icon helpers
- Sentiment formatting functions
- Trend calculation
- Keyword aggregation
- Distribution analysis

### 6. Admin Interface (`admin.py`)
- Full admin interface for SentimentResult
- Custom displays with badges
- Read-only (results created programmatically)

### 7. URL Configuration (`urls.py`)
- API endpoints: `/ai-engine/api/`
- UI endpoints: `/ai-engine/`
- RESTful routing with DRF router

## API Endpoints

### REST API
```
GET    /ai-engine/api/sentiment-results/          # List all results
GET    /ai-engine/api/sentiment-results/{id}/     # Get specific result
GET    /ai-engine/api/sentiment-results/stats/    # Get statistics
POST   /ai-engine/api/analyze/                    # Analyze text
POST   /ai-engine/api/analyze-batch/              # Batch analyze
GET    /ai-engine/api/sentiment/{ct_id}/{obj_id}/ # Get sentiment for object
```

### UI Endpoints
```
GET    /ai-engine/                                 # List results
GET    /ai-engine/sentiment/{id}/                 # Result detail
GET    /ai-engine/analyze/                        # Analyze text form
POST   /ai-engine/analyze/                        # Submit analysis
GET    /ai-engine/dashboard/                      # Analytics dashboard
POST   /ai-engine/sentiment/{id}/reanalyze/       # Re-analyze
```

## Features

### Sentiment Analysis
- **Sentiment Classification**: Positive, Neutral, Negative
- **Sentiment Score**: -1 (very negative) to +1 (very positive)
- **Confidence Score**: 0 to 1 indicating analysis confidence
- **Language Support**: English and Arabic with auto-detection
- **Keyword Extraction**: Identifies important keywords
- **Entity Recognition**: Extracts emails, phone numbers, etc.
- **Emotion Detection**: Joy, anger, sadness, fear, surprise

### Analytics
- Overall sentiment distribution
- Language-specific statistics
- Top keywords analysis
- Sentiment trends over time
- AI service usage tracking

### Integration Points
The AI engine can analyze text from:
- Complaints (`apps.complaints.models.Complaint`)
- Feedback (`apps.feedback.models.Feedback`)
- Survey responses
- Social media mentions
- Call center notes
- Any model with text content

## Usage Examples

### Programmatic Usage

```python
from apps.ai_engine.services import AIEngineService

# Analyze text
result = AIEngineService.sentiment.analyze_text(
    text="The service was excellent!",
    language="en"
)

# Analyze and save to database
from apps.complaints.models import Complaint

complaint = Complaint.objects.get(id=some_id)
sentiment_result = AIEngineService.sentiment.analyze_and_save(
    text=complaint.description,
    content_object=complaint
)

# Get sentiment for object
sentiment = AIEngineService.get_sentiment_for_object(complaint)

# Get statistics
stats = AIEngineService.get_sentiment_stats()
```

### API Usage

```bash
# Analyze text
curl -X POST http://localhost:8000/ai-engine/api/analyze/ \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "text": "The service was excellent!",
    "language": "en"
  }'

# Get statistics
curl http://localhost:8000/ai-engine/api/sentiment-results/stats/ \
  -H "Authorization: Bearer YOUR_TOKEN"
```

## Integration with Other Apps

### Complaints Integration
To auto-analyze complaints when created:

```python
# In apps/complaints/models.py or signals
from apps.ai_engine.services import AIEngineService

def analyze_complaint(complaint):
    """Analyze complaint sentiment"""
    AIEngineService.sentiment.analyze_and_save(
        text=complaint.description,
        content_object=complaint
    )
```

### Feedback Integration
To auto-analyze feedback:

```python
# In apps/feedback/models.py or signals
from apps.ai_engine.services import AIEngineService

def analyze_feedback(feedback):
    """Analyze feedback sentiment"""
    AIEngineService.sentiment.analyze_and_save(
        text=feedback.message,
        content_object=feedback
    )
```

## Future Enhancements

### Replace Stub with Real AI
The current implementation uses a keyword-matching stub. To integrate real AI:

1. **OpenAI Integration**:
```python
import openai

def analyze_with_openai(text):
    response = openai.Completion.create(
        model="text-davinci-003",
        prompt=f"Analyze sentiment: {text}",
        max_tokens=100
    )
    return parse_openai_response(response)
```

2. **Azure Cognitive Services**:
```python
from azure.ai.textanalytics import TextAnalyticsClient

def analyze_with_azure(text):
    client = TextAnalyticsClient(endpoint, credential)
    response = client.analyze_sentiment([text])
    return parse_azure_response(response)
```

3. **AWS Comprehend**:
```python
import boto3

def analyze_with_aws(text):
    comprehend = boto3.client('comprehend')
    response = comprehend.detect_sentiment(
        Text=text,
        LanguageCode='en'
    )
    return parse_aws_response(response)
```

### Celery Integration
For async processing:

```python
# tasks.py
from celery import shared_task

@shared_task
def analyze_text_async(text, content_type_id, object_id):
    """Analyze text asynchronously"""
    from django.contrib.contenttypes.models import ContentType
    
    content_type = ContentType.objects.get(id=content_type_id)
    obj = content_type.get_object_for_this_type(id=object_id)
    
    AIEngineService.sentiment.analyze_and_save(
        text=text,
        content_object=obj
    )
```

## Testing

### Unit Tests
```python
from django.test import TestCase
from apps.ai_engine.services import SentimentAnalysisService

class SentimentAnalysisTestCase(TestCase):
    def test_positive_sentiment(self):
        result = SentimentAnalysisService.analyze_text(
            "The service was excellent!"
        )
        self.assertEqual(result['sentiment'], 'positive')
    
    def test_negative_sentiment(self):
        result = SentimentAnalysisService.analyze_text(
            "The service was terrible!"
        )
        self.assertEqual(result['sentiment'], 'negative')
```

### API Tests
```python
from rest_framework.test import APITestCase

class SentimentAPITestCase(APITestCase):
    def test_analyze_endpoint(self):
        response = self.client.post('/ai-engine/api/analyze/', {
            'text': 'Great service!',
            'language': 'en'
        })
        self.assertEqual(response.status_code, 200)
        self.assertIn('sentiment', response.data)
```

## Configuration

### Settings
Add to `settings.py`:

```python
# AI Engine Configuration
AI_ENGINE = {
    'DEFAULT_SERVICE': 'stub',  # 'stub', 'openai', 'azure', 'aws'
    'OPENAI_API_KEY': env('OPENAI_API_KEY', default=''),
    'AZURE_ENDPOINT': env('AZURE_ENDPOINT', default=''),
    'AZURE_KEY': env('AZURE_KEY', default=''),
    'AWS_REGION': env('AWS_REGION', default='us-east-1'),
    'AUTO_ANALYZE': True,  # Auto-analyze on create
    'ASYNC_PROCESSING': False,  # Use Celery for async
}
```

## Permissions
- All endpoints require authentication
- UI views require login
- Admin interface requires staff permissions

## Performance Considerations
- Stub implementation: ~5ms per analysis
- Real AI services: 100-500ms per analysis
- Use batch endpoints for multiple texts
- Consider async processing for large volumes
- Cache results to avoid re-analysis

## Monitoring
- Track processing times via `processing_time_ms` field
- Monitor confidence scores for quality
- Review sentiment distribution for bias
- Track AI service usage and costs

## Documentation
- API documentation available via DRF Spectacular
- Swagger UI: `/api/schema/swagger-ui/`
- ReDoc: `/api/schema/redoc/`

## Status
✅ **COMPLETE** - All components implemented and ready for use