HealthPort AI

HealthPort AI is an intelligent medical report analysis tool that uses AI to analyze lab reports and provide detailed insights. The application features a modern, responsive interface with real-time analysis capabilities.

Features

• PDF and image upload support
• AI-powered analysis of medical reports
• Real-time progress tracking
• Modern, responsive UI
• Detailed analysis results
• Support for both text-based and scanned PDFs

Prerequisites

Before you begin, ensure you have the following installed:

For Windows:

1. Python 3.8+

   • Download from python.org
   • Make sure to check "Add Python to PATH" during installation

2. Tesseract OCR

   • Download from GitHub
   • During installation, make sure to:
     • Check "Add to PATH"
     • Note the installation path (default: C:\Program Files\Tesseract-OCR)

3. Poppler

   • Download from poppler releases
   • Extract to a folder (e.g., C:\Program Files\poppler)
   • Add the bin directory to your PATH:
     • Open System Properties > Advanced > Environment Variables
     • Add C:\Program Files\poppler\Library\bin to Path

For macOS:

1. Python 3.8+

   • Install using Homebrew: brew install python

2. Tesseract OCR

   • Install using Homebrew: brew install tesseract

3. Poppler

   • Install using Homebrew: brew install poppler

For Linux:

1. Python 3.8+

   • Install using your package manager:
     • Ubuntu/Debian: sudo apt install python3
     • Fedora: sudo dnf install python3

2. Tesseract OCR

   • Ubuntu/Debian: sudo apt install tesseract-ocr
   • Fedora: sudo dnf install tesseract

3. Poppler

   • Ubuntu/Debian: sudo apt install poppler-utils
   • Fedora: sudo dnf install poppler-utils

Installation

1. Clone the repository:

git clone https://github.com/yourusername/HealthPort-AI.git
cd HealthPort-AI

2. Set up the backend:

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Set up the frontend:

cd ../frontend
npm install

4. Create environment files:

Backend (create backend/.env):

GOOGLE_API_KEY=your_gemini_api_key
MONGO_URI=your_mongodb_uri

Frontend (create frontend/.env):

VITE_API_URL=http://localhost:8000

Running the Application

1. Start the backend server:

cd backend
venv\Scripts\activate # source venv/bin/activate On Mac: 
pip install -r requirements.txt #(if not already)
uvicorn main:app --reload

2. Start the frontend development server:

cd frontend
pnpm i #(if not already)
pnpm run dev

3. Open your browser and navigate to http://localhost:5173

Usage

1. Click the upload area or drag and drop a PDF file
2. Wait for the analysis to complete
3. View the detailed analysis results
4. Download or share the results as needed

Troubleshooting

Common Issues

1. Tesseract not found

   • Ensure Tesseract is installed and in your PATH
   • Verify installation: tesseract --version

2. Poppler not found

   • Ensure Poppler is installed and in your PATH
   • Verify installation: pdftoppm --version

3. Python package installation issues

   • Try upgrading pip: python -m pip install --upgrade pip
   • Ensure you're using the correct Python version

4. CORS errors

   • Ensure both frontend and backend servers are running
   • Check the API URL in frontend/.env

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Google Gemini API for AI analysis
• MongoDB for data storage
• FastAPI for backend framework
• React for frontend framework