This project is a web application built using the Flask framework, that serves a machine learning model for making predictions based on user input. The application is designed to be run inside a Docker container to ensure a consistent environment and easy deployment. The code and all its dependencies have been Dockerized and pushed to Dockerhub for easy access and distribution.

The application has two key endpoints:

• / : The home page of the web application.
• /predictdata : The endpoint for making predictions using the machine learning model.

1. Pull the Docker Image

   To get started with the application, the first step is to pull the Docker image from Dockerhub. Use the command below:

   docker pull iman2546/ml-proj:latest

2. Run the Docker Image

   After pulling the Docker image, you can run it using the command below:

   docker run -p 5000:5000 iman2546/ml-proj

   This command runs the Docker image and maps the container's port 5000 to your machine's port 5000. This allows you to access the web application by visiting http://localhost:5000 on your web browser.

• Flask App (app.py): This file contains the Flask web application.

  • @app.route('/'): This is the home page of the application. It renders the 'index.html' template.
  • @app.route('/predictdata',methods=['GET','POST']): This is the endpoint for making predictions with the model. If a GET request is made, it simply returns the 'home.html' template. If a POST request is made, it uses the PredictPipeline to make a prediction based on the user's input.

• Predict Pipeline (src/pipeline/predict_pipeline.py): This file contains the PredictPipeline class which is used to make predictions with the model.

• Templates: The HTML templates for the application are located in the 'templates' directory. These templates are used to generate the web pages of the application.

Visit http://localhost:5000 on your web browser to view the application. The home page simply presents the application.

To make a prediction, navigate to http://localhost:5000/predictdata. If you are making a GET request, it will present a form for you to input your data. If you are making a POST request (i.e., submitting the form), it will take the input, use it to make a prediction with the model, and then display the result on the 'home.html' template.