CICD-Model-Deployement-AWS

Overview

This project demonstrates deploying a machine learning model as a FastAPI web service on AWS EC2 using Docker and GitHub Actions for CI/CD. The API predicts restaurant tips based on customer and bill information.

Features

• FastAPI-based REST API for tip prediction
• Model loaded from AWS S3
• Dockerized application for easy deployment
• Automated CI/CD pipeline with GitHub Actions
• Secure deployment to AWS EC2

API Endpoints

• GET / — Health check
• GET /info — Project information
• GET /view — Sample people data
• POST /add — Add two numbers
• POST /predict — Predict restaurant tip

Input Example for /predict

{
  "total_bill": 25.5,
  "sex": "Male",
  "smoker": "No",
  "day": "Sat",
  "time": "Dinner",
  "size": 3
}

Setup

1. Clone the repository

   git clone https://github.com/abhi227070/CICD-Model-Deployement-AWS.git
   cd CICD-Model-Deployement-AWS

2. Install dependencies

   pip install -r requirements.txt

3. Configure AWS credentials

   • Create a .env file:

     AWS_ACCESS_KEY=your-access-key
     AWS_SECRET_KEY=your-secret-key
     

   • Ensure your model file (tips_rf_model.pkl) is in the specified S3 bucket.

4. Run locally

   uvicorn app:app --reload

Docker Usage

1. Build Docker image

   docker build -t tip-app .

2. Run Docker container

   docker run -d -p 8000:8000 --env-file .env --name tip-app tip-app

CI/CD Pipeline

• Automated deployment to AWS EC2 via GitHub Actions (.github/workflows/deploy.yml)
• On push to main, the workflow builds and deploys the Docker container to EC2

Project Structure

.
├── app.py
├── requirements.txt
├── Dockerfile
├── .env
├── .gitignore
├── .dockerignore
├── .github/
│   └── workflows/
│       └── deploy.yml
├── README.md
└── ...

License

MIT License. See LICENSE for details.

Author

Abhijeet Maharana