LearnDocker

This project demonstrates how to wrap a machine learning model in a FastAPI web application and Dockerize it for easy deployment. The application performs sentiment analysis using Hugging Face's transformers library, allowing users to analyze text via a simple API call.

Project Purpose

The goal of this project is to learn how to:

• Build a basic API using FastAPI.
• Integrate a pre-trained machine learning model for sentiment analysis.
• Dockerize the application for deployment in a containerized environment.

How to Build and Run

1. Build the Docker image:

   docker build -t fastapi-sentiment .

2. Run the Docker container:

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

3. Test the API:

   Open a browser or use a tool like curl or Postman to access:

   http://127.0.0.1:8000/analyze?input_string=I+love+this+app
   

   You will receive a JSON response with the sentiment and score.

Learnings

• Docker: Containerized the FastAPI app for easy and scalable deployment.
• FastAPI: Created a simple web API for machine learning model inference.
• Hugging Face transformers: Integrated pre-trained NLP models for real-time inference.