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🐍 Sentiment Analysis API

A Python microservice for text sentiment classification, built with FastAPI and powered by Hugging Face Transformers. The service is containerized with Docker for easy deployment and tested with Postman.

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🚀 Features

• 🔎 Sentiment Classification (Positive, Negative, Neutral)
• ⚡ FastAPI-powered REST API with automatic docs (/docs)
• 🤗 Hugging Face Transformers for state-of-the-art NLP
• 🐳 Dockerized for scalable deployment
• 🧪 Tested with Postman

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🛠️ Tech Stack

• Python 3.10+
• FastAPI
• Hugging Face Transformers
• Uvicorn (ASGI server)
• Docker

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📦 Installation & Setup

1️⃣ Clone Repository

git clone https://github.com/your-username/sentiment-analysis-api.git
cd sentiment-analysis-api

2️⃣ Create Virtual Environment

python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run Locally

uvicorn app.main:app --reload

API will be available at: 👉 http://127.0.0.1:8000

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🐳 Docker Deployment

Build Image

docker build -t sentiment-api .

Run Container

docker run -d -p 8000:8000 sentiment-api

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📡 API Endpoints

🔹 Health Check

GET /health

Response:

{"status": "ok"}

🔹 Sentiment Analysis

POST /predict

Request Body:

{
  "text": "I love using FastAPI, it's amazing!"
}

Response:

{
  "label": "positive",
  "score": 0.98
}

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📖 API Docs

FastAPI provides interactive API docs at:

• Swagger UI → http://127.0.0.1:8000/docs
• ReDoc → http://127.0.0.1:8000/redoc

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🧪 Testing with Postman

• Import the provided postman_collection.json into Postman
• Test endpoints /health and /predict

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📂 Project Structure

sentiment-analysis-api/
│── app/
│   ├── main.py          # FastAPI entry point
│   ├── models.py        # Hugging Face model loader
│   ├── schemas.py       # Pydantic schemas
│   └── utils.py         # Helper functions
│
│── tests/               # Unit tests
│── Dockerfile           # Docker configuration
│── requirements.txt     # Python dependencies
│── README.md            # Project documentation

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🚀 Future Improvements

• Add batch sentiment analysis endpoint
• Add support for multilingual models
• Integrate CI/CD pipeline for automated testing & deployment
• Add Kubernetes deployment files

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📜 License

MIT License © 2025 [Engr. Ipaye Babatunde]

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👉 Would you like me to also generate the Dockerfile + requirements.txt + main.py starter template so this README is directly usable as a working repo?