Diabetes Prediction System

A comprehensive web application for predicting diabetes likelihood based on medical input parameters. This project uses machine learning (Random Forest Classifier) and provides both Flask and FastAPI implementations with Docker support.

Project Structure

• main.py: FastAPI application providing REST API endpoints for diabetes prediction with automatic documentation.
• app.py: Flask web application with traditional web interface.
• train.py: Script to train the machine learning model using the dataset.
• models/: Directory where the trained model (diabetes_model.pkl) is saved.
• templates/: HTML files for the Flask web interface.
• static/: CSS and other static assets.
• requirements.txt: List of Python dependencies.
• Dockerfile: Docker configuration for containerized deployment.
• docker-build.bat & docker-run.bat: Windows batch scripts for Docker operations.

Prerequisites

• Python 3.8 or higher installed on your system.
• Docker (optional, for containerized deployment).

Setup Instructions

Local Development

1. Install Dependencies

   Open a terminal in the project directory and run the following command to install the required Python packages:

   pip install -r requirements.txt

2. Train the Model

   Before running the application, you must generate the machine learning model. Run the training script:

   python train.py

   This will download the dataset, train the Random Forest model, and save it to models/diabetes_model.pkl.

Running the Applications

Option 1: FastAPI with Uvicorn (Recommended)

Run the FastAPI application with Uvicorn:

uvicorn main:app --host 0.0.0.0 --port 8000 --reload

• API Documentation: Visit http://127.0.0.1:8000/docs for interactive API documentation
• Health Check: http://127.0.0.1:8000/health
• API Endpoint: POST to http://127.0.0.1:8000/predict with JSON payload

Option 2: Flask Application

Start the Flask web server:

python app.py

Access the web interface at http://127.0.0.1:5000

Docker Deployment

Using Batch Scripts (Windows)

1. Build the Docker Image

   docker-build.bat

2. Run the Container

   docker-run.bat

Manual Docker Commands

1. Build the Docker Image

   docker build -t diabetes-prediction-app .

2. Run the Container

   docker run -p 8000:8000 diabetes-prediction-app

The application will be available at http://localhost:8000

Usage

1. Enter the required patient information:
   • Number of Pregnancies
   • Glucose Level
   • Blood Pressure
   • BMI
   • Age
2. Click the "Predict" button.
3. The system will display whether the patient is "Diabetic" or "Not Diabetic" along with a confidence percentage.