marwan/HH

Marwan Alwali 226dc414cd update ai services

2025-12-24 14:10:18 +03:00

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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

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

# 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:

# 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:

# 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:

OpenAI Integration:

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)

Azure Cognitive Services:

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)

AWS Comprehend:

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:

# 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

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

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:

# 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

9.7 KiB Raw Blame History