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# Sentiment Analysis API
A production-ready REST API for analyzing text sentiment using transformer models, containerized with Docker.
## Features
- **Fast sentiment analysis** using DistilBERT transformer model
- **RESTful API** with automatic interactive documentation
- **Docker containerized** for consistent deployment
- **Health check endpoints** for production monitoring
- **Input validation** with Pydantic models
- **Sub-100ms inference** after model warm-up
## Tech Stack
- **Backend:** FastAPI (async Python web framework)
- **ML Model:** DistilBERT via HuggingFace Transformers
- **Containerization:** Docker
- **API Documentation:** Auto-generated with Swagger UI
## Quick Start
### Prerequisites
- Docker Desktop installed
- 4GB RAM minimum
### Run with Docker
```bash
# Build image
docker build -t sentiment-api:v1.0 .
# Run container
docker run -d -p 8000:8000 --name sentiment-api sentiment-api:v1.0
# View logs
docker logs sentiment-api
```
### Access API
- **Interactive Docs:** http://localhost:8000/docs
- **Health Check:** http://localhost:8000/health
## 🔌 API Usage
### Example Request
```bash
curl -X POST "http://localhost:8000/analyze" \
-H "Content-Type: application/json" \
-d '{"text": "I love this product!"}'
```
### Example Response
```json
{
"text": "I love this product!",
"sentiment": "POSITIVE",
"confidence": 0.9998,
"processing_time_ms": 45
}
```
## API Endpoints
| Method | Endpoint | Description |
|--------|----------|-------------|
| GET | `/` | Health check with version info |
| POST | `/analyze` | Analyze text sentiment |
| GET | `/health` | Kubernetes-style health endpoint |
## Project Structure
```
sentiment-api/
├── src/
│ └── main.py # FastAPI application
├── tests/ # Unit tests
├── Dockerfile # Container definition
├── .dockerignore # Docker build exclusions
├── requirements.txt # Python dependencies
└── README.md
```
## Development
### Local Setup (without Docker)
```bash
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Run locally
uvicorn src.main:app --reload
```
### Run Tests
```bash
pytest tests/ -v
```
## Docker Details
- **Base Image:** `python:3.11-slim`
- **Image Size:** ~1.2GB (includes PyTorch + transformers)
- **Health Check:** Configured for production monitoring
- **Multi-stage optimized** for faster rebuilds
## Performance
- **First request:** 30-60s (model download)
- **Subsequent requests:** < 100ms
- **Memory usage:** ~500MB (model in RAM)
- **Concurrent requests:** 10-20 (CPU-bound)
## Future Enhancements
- [ ] GPU support for faster inference
- [ ] Model caching layer (Redis)
- [ ] Rate limiting
- [ ] Authentication
- [ ] Batch inference endpoint
- [ ] Multiple model support
- [ ] Kubernetes deployment configs
## License
MIT License - feel free to use for your projects!
## Author
**Your Name**
- GitHub: [@simplyarfan](https://github.com/simplyarfan)
- LinkedIn: [Syed Arfan Hussain](https://linkedin.com/in/syedarfan)
## Acknowledgments
- [HuggingFace](https://huggingface.co/) for the DistilBERT model
- [FastAPI](https://fastapi.tiangolo.com/) for the excellent framework |