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Web Service Deployment

This README provides instructions for deploying the Heart Disease Prediction Service as a web service, both with and without Docker. The service uses Gunicorn as the HTTP server for WSGI applications.

Overview

The Heart Disease Prediction Service is designed to predict the likelihood of heart disease based on various input features. This service can be deployed in two ways:

  • Directly on your local machine using Gunicorn.
  • Inside a Docker container for isolation and ease of deployment.

Folder structure

├── Dockerfile
├── Pipfile
├── Pipfile.lock
├── README.md
├── dict_vectorizer.pkl
├── predict.py
├── requirements.txt
├── rf_model.pkl
├── scaler.pkl
└── test.py

Deployment Instructions

Without Docker

  1. Start the Service with Gunicorn

    Install packages

    pip install -r requirements.txt

    Navigate to the project directory and run the following command to start the service:

    gunicorn --bind=0.0.0.0:9696 predict:app

    This command starts the Gunicorn server on port 9696, binding it to all network interfaces.

  2. Test the Service

    After starting the service, you can test it by running:

    python test.py

    Ensure test.py contains the necessary code to send requests to your service and validate responses.

With Docker

  1. Build the Docker Image

    In the project directory, build the Docker image using:

    docker build -t heart-disease-prediction-service:v1 .

    This command creates a Docker image named heart-disease-prediction-service with the tag v1.

  2. Run the Docker Container

    Start the service inside a Docker container with:

    docker run -it --rm -p 9696:9696 heart-disease-prediction-service:v1

    This command runs the Docker container and maps port 9696 of the container to port 9696 on the host, allowing you to access the service at http://localhost:9696.

  3. Test the Service

    With the service running inside Docker, test it by executing:

    python test.py

    As before, ensure test.py is properly configured to test the service.

Conclusion

Following these instructions, you can deploy the Heart Disease Prediction Service either directly on your machine or within a Docker container. Testing the service ensures it is running correctly and ready to handle prediction requests.