An AI-powered financial advisory platform that helps students make informed decisions about loans, scholarships, and financial planning.

• Document Analysis: Upload and analyze financial documents using ColPali and Gemini AI
• Personal Information Management: Store and manage user financial data
• AI-Powered Insights: Get personalized financial recommendations
• Document Search: Search through uploaded documents with natural language queries
• Chat History: Keep track of all your financial discussions and analyses

• FastAPI - Modern Python web framework
• ChromaDB - Vector database for document search
• ColPali - Multimodal document understanding
• Gemini AI - Google's AI for document analysis
• MySQL - Relational database for user data
• Perplexity API - Real-time web search

• Next.js 15 - React framework
• TypeScript - Type-safe JavaScript
• Tailwind CSS - Utility-first CSS framework
• React Markdown - Markdown rendering
• Lucide React - Beautiful icons

• Python 3.8+
• Node.js 18+
• MySQL 8.0+

1. Clone the repository

   git clone <repository-url>
   cd vthacks-2025

2. Run the setup script

   ./startup.sh

3. Configure environment variables

   # Backend environment
   cp .env.example .env
   # Edit .env with your API keys and database credentials
   
   # Frontend environment
   cd frontend
   cp .env.example .env.local
   # Edit .env.local with your API URL (default: http://localhost:8000)

4. Start the application

   # Terminal 1 - Backend
   python app/main.py
   
   # Terminal 2 - Frontend
   cd frontend
   npm run dev

5. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Docs: http://localhost:8000/docs

• POST /db/users - Create user
• GET /db/users - Get all users
• GET /db/users/{id} - Get user by ID
• PUT /db/users/{id} - Update user

• POST /documents/upload - Upload document
• POST /documents/search - Search documents
• POST /documents/search-and-analyze - Search and analyze with AI
• GET /documents/indexed - Get indexed documents

• POST /query-perplexity - Query Perplexity AI
• POST /index-text - Index text in ChromaDB
• GET /search-text - Search ChromaDB

vthacks-2025/
├── app/
│   ├── main.py              # FastAPI application
│   ├── models.py            # Pydantic models
│   ├── database.py          # Database utilities
│   ├── document_service.py  # Document processing
│   ├── model_manager.py     # ColPali model management
│   └── initial.py           # Model initialization
├── frontend/
│   ├── src/
│   │   ├── app/             # Next.js pages
│   │   ├── components/      # React components
│   │   └── services/        # API services
│   └── package.json
├── docs/                    # Sample documents
├── database_schema.sql      # Database schema
└── requirements.txt         # Python dependencies

# Install dependencies
pip install -r requirements.txt

# Run with auto-reload
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

cd frontend
npm install
npm run dev

1. Deploy your FastAPI backend to your preferred platform (Railway, Heroku, AWS, etc.)
2. Set up your database and environment variables
3. Note your backend URL (e.g., https://your-app.railway.app)

1. Update the frontend environment variable:

   cd frontend
   # Update .env.local or set in your deployment platform
   NEXT_PUBLIC_API_URL=https://your-backend-deployment-url.com

2. Deploy to your preferred platform:

   # For Vercel
   vercel --prod
   
   # For Netlify
   npm run build
   # Upload dist folder to Netlify
   
   # For Railway
   railway deploy

PERPLEXITY_API_KEY=your_perplexity_api_key
GEMINI_API_KEY=your_gemini_api_key
DB_HOST=localhost
DB_PORT=8889
DB_USER=root
DB_PASSWORD=root
DB_NAME=vthacks_2025

NEXT_PUBLIC_API_URL=http://localhost:8000

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

MIT License - see LICENSE file for details