# Call Analytics Dashboard - Backend API Flask-based REST API for the Call Analytics Dashboard. Provides endpoints for managing calls, businesses, and analytics data. ![Python](https://img.shields.io/badge/Python-3.8+-blue) ![Flask](https://img.shields.io/badge/Flask-3.0-green) ![MySQL](https://img.shields.io/badge/MySQL-8.0-orange) ## Features ### 📊 **Business Management** - List all businesses with call counts - Get detailed business information - Multi-tenant support with dynamic table routing ### 📞 **Call Management** - Get all calls with filtering (status, intent, date range) - Get specific call details with transcripts - Search calls by multiple fields - Recent calls retrieval - Webhook support for call updates ### 📈 **Analytics** - Call statistics (totals, averages, success rates) - Sentiment distribution analysis - Sales intent distribution - Trend analysis over time - Agent performance metrics - Top keywords extraction ### 🔄 **Data Export** - Export calls to JSON - Export calls to CSV - Filtered data export ## API Endpoints ### Health & Status ``` GET /health # Health check ``` ### Business Management ``` GET /list-businesses # List all businesses GET /businesses//info # Get business details ``` ### Call Management ``` GET /calls/ # Get calls with filtering GET /calls// # Get call details GET /calls//recent # Get recent calls POST /calls/search # Search calls POST /queue-calls/ # Queue calls for processing POST /process-calls/ # Process calls directly ``` ### Analytics ``` GET /analytics//stats # Overall statistics GET /analytics//sentiment # Sentiment distribution GET /analytics//intent # Intent distribution GET /analytics//trends # Trends over time GET /analytics//agents # Agent performance GET /analytics//keywords # Top keywords ``` ### Data Export ``` GET /export//calls # Export calls (JSON/CSV) ``` ### Webhooks ``` POST /webhook/call-update # Receive call updates POST /webhook/conversation-summary # Receive summaries ``` ## Database Schema ### Tables Used **Per Business (dynamic tables):** - `{bid}_calls` - Main call records - `{bid}_sarvamresponse` - Transcription data - `{bid}_call_history` - Conversation summaries **Call Record Fields:** ``` id, bid, callid, fileUrl, status, agent_name, duration, customer_id, customer_name, call_type, call_date, call_time, call_status, keywords, sentiments, emotions, summary, sand_logic_response, sales_intent, transcripts, customer_details, remarks, created_at, updated_at ``` **Status Codes:** - `0` - Unprocessed - `1` - Transcribed - `2` - Analyzed - `3` - Message Sent ## Installation ### Prerequisites - Python 3.8+ - MySQL 8.0+ - Access to voicebot_cluster database ### Setup Steps 1. **Create virtual environment:** ```bash cd dashboard-backend python3 -m venv venv source venv/bin/activate # Linux/Mac # or venv\Scripts\activate # Windows ``` 2. **Install dependencies:** ```bash pip install -r requirements.txt ``` 3. **Configure environment:** ```bash cp .env.example .env # Edit .env with your database credentials ``` 4. **Update .env file:** ```env DB_HOST=10.0.0.109 DB_USER=admin DB_PASSWORD=your-password DB_NAME=voicebot_cluster ``` 5. **Run the application:** ```bash python app.py ``` API will be available at: **http://localhost:5000** ## Configuration ### Environment Variables | Variable | Description | Default | |----------|-------------|---------| | `FLASK_ENV` | Environment (development/production) | development | | `FLASK_DEBUG` | Debug mode | True | | `FLASK_HOST` | Server host | 0.0.0.0 | | `FLASK_PORT` | Server port | 5000 | | `DB_HOST` | Database host | 10.0.0.109 | | `DB_PORT` | Database port | 3306 | | `DB_USER` | Database user | admin | | `DB_PASSWORD` | Database password | - | | `DB_NAME` | Database name | voicebot_cluster | | `CORS_ORIGINS` | Allowed CORS origins | localhost:5173 | ## Project Structure ``` dashboard-backend/ ├── app.py # Main Flask application ├── config.py # Configuration classes ├── db_handler.py # Database operations ├── analytics.py # Analytics service ├── requirements.txt # Python dependencies ├── .env.example # Environment template ├── .env # Your config (don't commit) └── README.md # This file ``` ## API Usage Examples ### Get All Businesses ```bash curl http://localhost:5000/list-businesses ``` Response: ```json [ { "bid": "6840", "name": "Business 6840", "totalCalls": 125, "callsTable": "6840_calls" } ] ``` ### Get Calls with Filtering ```bash # All calls curl "http://localhost:5000/calls/6840" # Filter by status curl "http://localhost:5000/calls/6840?status=3" # Filter by date range curl "http://localhost:5000/calls/6840?date_from=2024-11-01&date_to=2024-11-30" # Pagination curl "http://localhost:5000/calls/6840?limit=50&offset=0" ``` ### Get Call Details ```bash curl http://localhost:5000/calls/6840/CALL-2024-001 ``` ### Search Calls ```bash curl -X POST http://localhost:5000/calls/search \ -H "Content-Type: application/json" \ -d '{ "bid": "6840", "query": "product inquiry", "limit": 50 }' ``` ### Get Analytics ```bash # Overall statistics curl "http://localhost:5000/analytics/6840/stats" # Sentiment distribution curl "http://localhost:5000/analytics/6840/sentiment" # Intent distribution curl "http://localhost:5000/analytics/6840/intent" # Trends (last 7 days) curl "http://localhost:5000/analytics/6840/trends?days=7" # Agent performance curl "http://localhost:5000/analytics/6840/agents" # Top keywords curl "http://localhost:5000/analytics/6840/keywords?limit=20" ``` ### Export Data ```bash # Export as JSON curl "http://localhost:5000/export/6840/calls" # Export as CSV curl "http://localhost:5000/export/6840/calls?format=csv" > calls.csv ``` ## Production Deployment ### Using Gunicorn ```bash # Install gunicorn (already in requirements.txt) pip install gunicorn # Run with 4 workers gunicorn -w 4 -b 0.0.0.0:5000 app:app # With gevent for async support gunicorn -w 4 -k gevent -b 0.0.0.0:5000 app:app ``` ### Using Systemd Service Create `/etc/systemd/system/dashboard-api.service`: ```ini [Unit] Description=Call Analytics Dashboard API After=network.target [Service] User=www-data WorkingDirectory=/var/www/html/sara/dashboard-backend Environment="PATH=/var/www/html/sara/dashboard-backend/venv/bin" ExecStart=/var/www/html/sara/dashboard-backend/venv/bin/gunicorn \ -w 4 -k gevent -b 0.0.0.0:5000 app:app [Install] WantedBy=multi-user.target ``` Enable and start: ```bash sudo systemctl enable dashboard-api sudo systemctl start dashboard-api sudo systemctl status dashboard-api ``` ### Nginx Reverse Proxy Add to nginx config: ```nginx server { listen 80; server_name dashboard-api.yourdomain.com; location / { proxy_pass http://127.0.0.1:5000; proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; } } ``` ## Error Handling All endpoints return consistent error responses: ```json { "error": "Error message", "message": "Human-readable description" } ``` HTTP Status Codes: - `200` - Success - `400` - Bad Request - `404` - Not Found - `500` - Internal Server Error ## Logging Logs are written to: - Console (stdout) - File: `dashboard_api.log` (if configured) Log format: ``` 2024-11-25 10:30:45 - db_handler - INFO - Database connection established ``` ## Security Considerations 1. **Environment Variables**: Never commit `.env` file 2. **CORS**: Configure allowed origins properly 3. **SQL Injection**: Uses parameterized queries 4. **Rate Limiting**: Enable in production 5. **Authentication**: Add JWT/OAuth if needed ## Troubleshooting ### Database Connection Errors ```bash # Test database connection mysql -h 10.0.0.109 -u admin -p voicebot_cluster ``` ### Port Already in Use ```bash # Kill process on port 5000 lsof -ti:5000 | xargs kill -9 ``` ### Module Not Found ```bash # Reinstall dependencies pip install -r requirements.txt ``` ### CORS Errors Update CORS_ORIGINS in `.env`: ```env CORS_ORIGINS=http://localhost:5173,https://yourdomain.com ``` ## Performance Optimization ### Enable Caching ```env CACHE_ENABLED=True CACHE_TYPE=redis CACHE_REDIS_HOST=localhost ``` ### Database Indexing Recommended indexes: ```sql ALTER TABLE 6840_calls ADD INDEX idx_status (status); ALTER TABLE 6840_calls ADD INDEX idx_call_date (call_date); ALTER TABLE 6840_calls ADD INDEX idx_sales_intent (sales_intent); ALTER TABLE 6840_calls ADD INDEX idx_callid (callid); ``` ### Connection Pooling Uses PyMySQL connection pooling automatically. ## Integration with Frontend Update frontend API URL in `dashboard-frontend/src/services/api.js`: ```javascript const API_BASE_URL = 'http://your-server:5000'; ``` ## Testing ### Manual Testing ```bash # Health check curl http://localhost:5000/health # Test endpoints curl http://localhost:5000/list-businesses curl "http://localhost:5000/calls/6840?limit=5" ``` ### Load Testing (with Apache Bench) ```bash ab -n 1000 -c 10 http://localhost:5000/health ``` ## Monitoring ### Health Check Endpoint ```bash # Continuous monitoring watch -n 5 'curl -s http://localhost:5000/health' ``` ### Logs Monitoring ```bash tail -f dashboard_api.log ``` ## Future Enhancements - [ ] JWT Authentication - [ ] Role-based access control - [ ] WebSocket support for real-time updates - [ ] Redis caching layer - [ ] API rate limiting - [ ] Comprehensive test suite - [ ] API documentation (Swagger/OpenAPI) - [ ] Data validation with Marshmallow - [ ] Background task queue (Celery) - [ ] Metrics and monitoring (Prometheus) ## License Part of the Post Call Analysis System. ## Support For issues or questions, contact the development team. --- **Built with Flask + PyMySQL + Python 3.8+**