xrAI - Medical X-Ray Analysis

xrAI is a modern web application designed for automated classification of chest X-rays. It uses deep learning to identify conditions such as Normal, Pneumonia, and TB.

Features

• Deep Learning Inference: Fast and accurate X-ray classification.
• Grad-CAM Visualization: View heatmaps showing which areas the AI focused on.
• Modern UI: Clean, clinical-grade interface built with Next.js.

Tech Stack

• Frontend: Next.js (React), TailwindCSS, Axios
• Backend: Flask (Python), PyTorch, OpenCV

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Getting Started

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file from the example:

   cp .env.example .env

5. Run the server:

   python app.py

Frontend Setup

1. Navigate to the frontend/my-app directory:

   cd frontend/my-app

2. Install dependencies:

   npm install

3. Create a .env.local file:

   NEXT_PUBLIC_API_URL=http://localhost:5000

4. Run the development server:

   npm run dev

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Deployment

Backend (Render)

This project is configured for deployment on Render.

• Use the backend/render.yaml blueprint.
• Ensure you have the best_xray_model.pth file in the backend directory.

Frontend (Vercel/Netlify)

• Set the NEXT_PUBLIC_API_URL environment variable to your deployed backend URL.

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Environment Variables

Backend

• PORT: The port the Flask server will run on (default: 5000).

Frontend

• NEXT_PUBLIC_API_URL: The URL of the Flask backend.