PrompTrend: Intelligent Chat Support System

PrompTrend is an advanced chat support system that combines intent classification and contextual bandit algorithms to provide personalized recommendations and responses. The system uses BERT for intent classification and implements a contextual multi-armed bandit approach for dynamic learning from user interactions.

🌟 Key Features

• Intent classification using BERT
• Contextual bandit-based recommendation system
• Real-time user feedback processing
• Automatic question generation
• Redis caching for improved performance
• Comprehensive API documentation
• Robust error handling
• Database persistence with PostgreSQL

🛠️ Technology Stack

• Framework: FastAPI
• ML Models: BERT (Transformers), T5
• Database: PostgreSQL
• Caching: Redis
• ML Libraries: PyTorch, Scikit-learn
• Testing: Pytest

📋 Prerequisites

• Python 3.8+
• PostgreSQL
• Redis
• CUDA-compatible GPU (optional, for faster model training)

⚙️ Installation

1. Clone the repository:

git clone https://github.com/yourusername/promptrend.git
cd promptrend

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables:

python scripts/setup_env.py --env development

5. Initialize the database:

alembic upgrade head

🚀 Running the Application

1. Start the Redis server:

redis-server

2. Start the application:

uvicorn main:app --reload

The API will be available at http://localhost:8000

🧪 Testing

The project includes comprehensive tests for all components. To run the tests:

# Run all tests
pytest

# Run tests with coverage report
pytest --cov=app tests/

# Run specific test file
pytest tests/test_intent_classifier.py

📂 Project Structure

promptrend/
├── api/
│   └── routes.py          # API endpoints
├── core/
│   ├── config.py          # Configuration management
│   ├── database.py        # Database setup
│   ├── models.py          # Data models
│   └── cache.py           # Redis cache implementation
├── services/
│   ├── intent_classifier.py    # BERT classifier
│   ├── recommender.py          # Contextual bandit
│   ├── recommendation_service.py
│   ├── question_generator.py   # T5 question generator
│   └── error_handler.py        # Error handling service
├── tests/
│   ├── test_intent_classifier.py
│   ├── test_recommender.py
│   ├── test_api.py
│   └── test_integration.py 
├── scripts/
│   └── setup_env.py
├── main.py
├── requirements.txt
└── README.md

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Hugging Face for the Transformers library
• FastAPI team for the amazing framework
• The open-source community for various dependencies

📞 Contact

For questions and feedback, please create an issue in the GitHub repository.