An AI-powered application designed to upload, analyze, and query legal documents. It also generates step-by-step procedural roadmaps from natural language goals. Built for the Google Gen AI Exchange Hackathon.
[Live Demo Link] : https://legalclassifier.netlify.app/
Legal documents and official procedures are often lengthy, filled with complex jargon, and inaccessible to non-experts. This leads to users missing critical clauses, making costly errors, and spending hours trying to understand complex workflows. Our solution ensures users can quickly understand agreements, extract insights, and get a clear, actionable path for their goals.
- ✅ Multi-Format Document Analysis: Supports
.pdf,.docx, and.txtfiles, with built-in OCR to extract text even from scanned images. - 🗺️ AI-Generated Roadmaps: Converts a simple goal (e.g., "buy a flat in Bangalore") into a complete, step-by-step timeline.
- 🃏 Structured Insight Cards: Automatically identifies and categorizes information into Alerts, Favourable Terms, and Clauses to Watch.
- 💬 Context-Aware Chat Assistant: Ask follow-up questions in natural language about your uploaded document or generated roadmap.
- 🌍 Multilingual & Accessible: The entire analysis, roadmap, and chat are available in English, Tamil, Hindi, and Malayalam.
- 📤 PDF Export: Instantly download your document analysis or procedural roadmap as a clean, professional PDF.
- Frontend: React.js, Axios, Tailwind CSS
- Backend: FastAPI, PyMuPDF,
python-docx, Mammoth, Tesseract OCR - AI Models: Google Cloud's Gemini 1.5 Flash on Vertex AI
- Deployment: Render (Backend) + Netlify (Frontend)
git clone https://github.com/RunProgrammer/LegalClassifier.git
cd LegalClassifiercd backend
python -m venv venv
# On Windows
.\venv\Scripts\activate
# On macOS/Linux
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reloadcd frontend
npm install
npm run devCreate a .env file in the backend/ directory and add your Google Cloud credentials. See the backend code for required variables (e.g., GCP_PROJECT_ID and GOOGLE_APPLICATION_CREDENTIALS).
-> Embeddings & Vector DB: Implement a vector database (like Pinecone or ChromaDB) for long-term memory and querying over multiple documents.
-> User Accounts: Allow users to save and manage their document history.
-> E-Signature Integration: Connect with platforms like DocuSign for a seamless workflow.
This project is licensed under the Apache 2.0 License. See the LICENSE file for details.