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Syed Arfan
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5fb7b67
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Parent(s):
bf8eab1
Refactor README.md for improved formatting and clarity
Browse files
README.md
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@@ -6,7 +6,7 @@ A production-ready REST API for analyzing text sentiment using transformer model
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-
##
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- **Fast sentiment analysis** using DistilBERT transformer model
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- **RESTful API** with automatic interactive documentation
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- **Input validation** with Pydantic models
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- **Sub-100ms inference** after model warm-up
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##
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- **Backend:** FastAPI (async Python web framework)
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- **ML Model:** DistilBERT via HuggingFace Transformers
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- **Containerization:** Docker
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- **API Documentation:** Auto-generated with Swagger UI
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##
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### Prerequisites
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- Docker Desktop installed
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}
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```
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##
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| Method | Endpoint | Description |
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|--------|----------|-------------|
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| POST | `/analyze` | Analyze text sentiment |
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| GET | `/health` | Kubernetes-style health endpoint |
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##
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```
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sentiment-api/
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โโโ src/
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โโโ README.md
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```
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##
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### Local Setup (without Docker)
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```bash
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pytest tests/ -v
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```
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##
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- **Base Image:** `python:3.11-slim`
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- **Image Size:** ~1.2GB (includes PyTorch + transformers)
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- **Health Check:** Configured for production monitoring
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- **Multi-stage optimized** for faster rebuilds
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##
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- **First request:** 30-60s (model download)
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- **Subsequent requests:** < 100ms
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- **Memory usage:** ~500MB (model in RAM)
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- **Concurrent requests:** 10-20 (CPU-bound)
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##
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- [ ] GPU support for faster inference
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- [ ] Model caching layer (Redis)
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@@ -128,17 +128,17 @@ pytest tests/ -v
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- [ ] Multiple model support
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- [ ] Kubernetes deployment configs
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##
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MIT License - feel free to use for your projects!
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##
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**Your Name**
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- GitHub: [@
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- LinkedIn: [
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##
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- [HuggingFace](https://huggingface.co/) for the DistilBERT model
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- [FastAPI](https://fastapi.tiangolo.com/) for the excellent framework
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## Features
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- **Fast sentiment analysis** using DistilBERT transformer model
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- **RESTful API** with automatic interactive documentation
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- **Input validation** with Pydantic models
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- **Sub-100ms inference** after model warm-up
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## Tech Stack
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- **Backend:** FastAPI (async Python web framework)
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- **ML Model:** DistilBERT via HuggingFace Transformers
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- **Containerization:** Docker
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- **API Documentation:** Auto-generated with Swagger UI
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## Quick Start
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### Prerequisites
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- Docker Desktop installed
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}
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```
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## API Endpoints
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| Method | Endpoint | Description |
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|--------|----------|-------------|
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| POST | `/analyze` | Analyze text sentiment |
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| GET | `/health` | Kubernetes-style health endpoint |
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## Project Structure
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```
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sentiment-api/
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โโโ src/
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โโโ README.md
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```
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## Development
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### Local Setup (without Docker)
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```bash
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pytest tests/ -v
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```
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## Docker Details
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- **Base Image:** `python:3.11-slim`
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- **Image Size:** ~1.2GB (includes PyTorch + transformers)
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- **Health Check:** Configured for production monitoring
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- **Multi-stage optimized** for faster rebuilds
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## Performance
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- **First request:** 30-60s (model download)
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- **Subsequent requests:** < 100ms
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- **Memory usage:** ~500MB (model in RAM)
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- **Concurrent requests:** 10-20 (CPU-bound)
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## Future Enhancements
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- [ ] GPU support for faster inference
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- [ ] Model caching layer (Redis)
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- [ ] Multiple model support
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- [ ] Kubernetes deployment configs
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## License
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MIT License - feel free to use for your projects!
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## Author
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**Your Name**
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- GitHub: [@simplyarfan](https://github.com/simplyarfan)
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- LinkedIn: [Syed Arfan Hussain](https://linkedin.com/in/syedarfan)
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## Acknowledgments
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- [HuggingFace](https://huggingface.co/) for the DistilBERT model
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- [FastAPI](https://fastapi.tiangolo.com/) for the excellent framework
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