Sentiment Analysis API

This repository contains a FastAPI application for sentiment analysis using the Hugging Face Transformers library. The application is designed to be run as a standalone service and can be deployed using Docker.

Prerequisites

• Docker installed on your machine
• Basic knowledge of Python and Docker

Getting Started

1. Clone the Repository

git clone https://github.com/yourusername/sentiment-analysis-simple.git
cd sentiment-analysis-simple

Note: Add model in the model directory

2. Build the Docker Image

Build the Docker image using the following command:

docker build -t sentiment-analysis-simple .

3. Run the Docker Container

Run the Docker container using the following command:

docker run -d -p 8000:8000 sentiment-analysis-simple

4. Access the API

The API will be accessible at http://localhost:8000. You can use tools like curl or Postman to interact with the API.

5. Example Request

To predict the sentiment of a sentence, send a POST request to the /predict endpoint with a JSON payload containing the sentence.

curl -X POST "http://localhost:8000/predict" -H "Content-Type: application/json" -d '{"sentence": "I love this product!"}'

6. Response

The API will return a JSON response with the sentiment analysis result.

{
    "request_id": "some-unique-id",
    "sentiment": [
        {
            "label": "POSITIVE",
            "score": 0.9998
        }
    ]
}

Project Structure

• app.py: Contains the FastAPI application and sentiment analysis logic.
• Dockerfile: Dockerfile to build the Docker image.
• models/: Directory containing the pre-trained model weights.
• requirements.txt: List of Python dependencies.

Conclusion

You now have a fully functional sentiment analysis API running in a Docker container. You can extend this application by adding more features or improving the model.

For any questions or issues, please open an issue in the repository.