Doc Assistant

An AI-powered platform to assist healthcare professionals by providing quick and accurate analysis of medical reports, X-rays, and ECGs. Built with Flask, TensorFlow Lite, and Groq API.

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Features

• Medical Report Analyzer: Upload PDF or image files of medical reports for instant AI-generated summaries and parameter extraction.
• X-ray Analyzer: Upload chest X-ray images to detect signs of pneumonia using a quantized TFLite model.
• ECG Analyzer: Upload ECG images to classify and summarize heart conditions using a quantized TFLite model.
• User-friendly Web Interface: Simple upload forms and clear summaries for each tool.

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Demo

• Clone the repository and run locally, or deploy using Render with the provided render.yaml.

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Installation & Setup

1. Clone the repository:

   git clone https://github.com/AayushGoswami/DocAssIstant.git
   cd DocAssIstant

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   • Create a .env file in the root directory:

     GROQ_API_KEY=your_actual_groq_api_key

   • Replace your_actual_groq_api_key with your Groq API key.

4. Model files:

   • Ensure the following model files are present in utils/models/:
     • chest_xray_model_quantized.tflite
     • ecg_model_quantized.tflite

5. Run the application:

   python app.py

   • The app will be available at http://localhost:8080 (or as specified in your environment).

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Usage

• Go to the home page and select a tool from the sidebar:
  • Medical Report Analyzer: Upload a PDF or image of a medical report.
  • X-ray Analyzer: Upload a chest X-ray image (JPG/PNG).
  • ECG Analyzer: Upload an ECG image (JPG/PNG).
• The AI will process the file and display a summary and key findings.

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Deployment

• For production, use the provided render.yaml for Render.com deployment.
• The app uses Gunicorn as the WSGI server in production.

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Requirements

• Python 3.8+
• Flask
• TensorFlow (Lite)
• Groq API access
• Pillow, numpy, PyPDF2, python-dotenv, gunicorn

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Credits

• Developed by Aayush Goswami and Om Modi.
• Powered by Groq API and TensorFlow Lite models.

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License

This project is licensed under the Apache 2.0 License.

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Repository

GitHub - DocAssIstant