MediSight.AI - AI Medical Report Analyzer

This full-stack web application allows users to upload a medical report PDF, receive an AI-generated analysis performed locally using a transformer model, and ask follow-up questions via a chat interface.

The core function runs entirely on your local machine. This ensures greater privacy for sensitive health records as your documents are never uploaded to external servers or third-party services.

Disclaimer: This tool is for informational purposes only and does not substitute professional medical advice. The AI-generated analysis may contain inaccuracies. Always consult a qualified healthcare provider for any health concerns or before making any decisions related to your health or treatment.

Features

• PDF Upload: Securely upload medical reports in PDF format directly within your browser.
• Enhanced Privacy: By running the AI model locally, your medical report never leaves your computer, offering significantly better privacy compared to cloud-based analysis services.
• Local AI Analysis: Get a structured analysis generated on your own machine using FreedomIntelligence/Apollo2-2B, including:
  • Overall Summary / Key Findings (Interpretive Paragraph)
  • Potential Health Risks (Bulleted)
  • Combined Dietary & Lifestyle Recommendations (Single Bulleted List)
• Patient-Friendly Language: Analysis is prompted to be understandable by patients.
• Chat Interface: Ask follow-up questions based on the analyzed report context, also processed locally.
• Modern UI: Clean interface styled with Tailwind CSS, inspired by healthcare applications.
• No External API Keys Needed: Fully functional after initial model download without requiring accounts or API keys for external services.

Project Structure

├── backend/

│ ├── main.py # FastAPI application logic (local model inference)

│ ├── requirements.txt # Python dependencies

│ └── env/ #Access_Tokens

├── frontend/

│ ├── index.html # HTML structure

│ └── script.js # JavaScript for frontend logic

└── README.md # This file

Tech Stack

• Frontend:
  • HTML5
  • CSS3 / Tailwind CSS (via CDN)
  • JavaScript (Vanilla)
  • Font Awesome (via CDN for icons)
• Backend:
  • Python 3.8+
  • FastAPI (Web framework)
  • Uvicorn (ASGI server)
  • Transformers (Hugging Face library for local models)
  • PyTorch (Backend for Transformers)
  • Accelerate (For efficient model loading across devices)
  • PDFPlumber (PDF text extraction)
• AI Model:
  • FreedomIntelligence/Apollo2-2B (Executed locally)

Setup Instructions

1. Prerequisites:

• Python 3.8+: Ensure Python is installed (python.org).
• pip: Python package installer (usually included with Python).
• Git: (Optional) For cloning the repository.
• Hardware:
  • RAM: Significant RAM required (16GB+ recommended, more might be needed depending on system).
  • GPU: Highly recommended for acceptable performance. An NVIDIA GPU with substantial VRAM (e.g., 8GB+, ideally 12GB or more) is suggested. CPU-only execution will be very slow.
  • Storage: Several gigabytes of disk space needed to download the AI model weights.

2. Backend Setup:

• Clone the repository (if applicable) or download the code.
• Navigate to the backend directory in your terminal:

  cd path/to/project/backend

• Create and activate a Python virtual environment (recommended):

  # Create
  python -m venv venv
  # Activate (macOS/Linux)
  source venv/bin/activate
  # Activate (Windows)
  .\venv\Scripts\activate

• Install Python dependencies:

  pip install -r requirements.txt

  (Note: torch installation might take time. Ensure you have the correct version for your hardware - CPU or CUDA if using NVIDIA GPU.) (Optional: If using model quantization, install bitsandbytes: pip install bitsandbytes)

3. Frontend Setup:

• No build steps are required. The frontend is ready to use.

Running the Application

1. Start the Backend Server:

• Ensure you are in the backend directory with your virtual environment activated.
• Run the FastAPI server using Uvicorn:

  uvicorn main:app --reload --host 127.0.0.1 --port 8000

  (Using 127.0.0.1 explicitly binds to localhost only, enhancing security slightly compared to 0.0.0.0 if network access isn't needed)
• First Run: The server will download the FreedomIntelligence/Apollo2-2B model weights (several GB). This may take significant time. Subsequent startups are faster.
• Keep the terminal running.

2. Access the Frontend:

• Navigate to the frontend directory in your file explorer.
• Open the index.html file directly in your web browser.

How to Use

1. Open index.html in your browser.
2. Click "Select PDF file" and choose your medical report. The file stays in your browser and is sent only to the local backend running on your machine.
3. Click "Analyze Report".
4. Wait for local processing. Performance depends on your hardware.
5. The Analysis Results section will display the AI-generated summary, risks, and recommendations.
6. Use the Ask Follow-up Questions section for further clarification.

Notes & Considerations

• Privacy: The primary advantage of this tool is privacy. Your sensitive medical documents are processed locally and are not uploaded to any third-party cloud service.
• Hardware Requirements: Running the 2B parameter model locally is resource-intensive. Performance heavily depends on your CPU, RAM, and especially GPU VRAM.
• Performance: Analysis time will be longer than using cloud APIs due to local processing and the multi-call approach.
• Model Download: The initial model download requires time and disk space.
• Accuracy: AI output is not guaranteed to be accurate or complete. Always consult a qualified healthcare professional.

Created By

Trisach Joshi

Diptak Chattopadhyay

Aniket Sahu