Medical-Report-Analyzer

Medical Report Analyzer is a web-based tool that allows users to upload medical PDFs, extract their content, and generate AI-powered insights using the Groq LLaMA3-70B model. Built with Python (Flask) and deployed on Vercel, it provides a smooth, fast, and privacy-friendly experience for quick medical data understanding.

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✨ Features

• 🔍 Extracts disease diagnosis from uploaded reports
• 🧑 Detects patient details like name, age, gender
• 👨‍⚕️ Identifies doctor/hospital information
• 🧠 Uses LLaMA3-70B via Groq API for natural language analysis
• 💡 Provides patient instructions and next steps
• 🖥️ Clean and responsive frontend
• 🚀 Deployed on Vercel

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📹 Demo

new.medical.report.mp4

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📁 Project Structure

Medical-report-analyzer-ai/ 
├── api/
│   └── index.py # ✅ Your Flask backend 
│ 
├── templates/
│   └── index.html # ✅ Your HTML frontend 
│ 
├── requirements.txt 
├── vercel.json 
├── .gitignore

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📦 Installation (for local dev)

git clone https://github.com/Poovarasan46/Medical-Report-Analyzer.git
cd Medical-Report-Analyzer
cd api
pip install -r requirements.txt
python index.py
Then open: http://127.0.0.1:5000

🔐 Environment Variables (Vercel)

On Vercel Dashboard:

• GROQ_API_KEY – your Groq API key

The .env file is not uploaded to GitHub for security.

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🚀 Deployment Steps

1. Push code to GitHub
2. Connect repo to Vercel
3. In Project Settings → Environment Variables, add your_api_key
4. Deploy ✅

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🛠️ Tech Stack

• Python + Flask – Backend framework for serving the app
• HTML + JS + PDF.js – Frontend interface and PDF processing
• Vercel – Hosting and deployment platform
• Groq API (LLaMA3-70B) – LLM for medical report analysis

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🤝 Acknowledgements

• Groq
• Vercel
• OpenAI's Chat Completions API Format
• You — for reading this 👀