ExamAI Platform

An intelligent exam preparation platform powered by RAG (Retrieval-Augmented Generation) technology, supporting IIT-JEE, NEET, and EAMCET exam preparation.

This is a monorepo containing both the frontend (Next.js) and backend (FastAPI) in a single Git repository for easier development and deployment.

🚀 Quick Start

Run Both Frontend and Backend Together

Easiest Method - Using npm:

npm run dev

Alternative Methods:

• Windows Batch: Double-click start.bat
• PowerShell: Run .\start.ps1

For detailed setup instructions, see STARTUP_GUIDE.md

📋 Features

• AI-Powered Question Generation: Generate exam questions using advanced AI models
• Multiple Exam Types: Support for IIT-JEE, NEET, and EAMCET
• Document Upload: Upload study materials for RAG-based question generation
• Performance Analytics: Track your progress and performance over time
• Subscription Management: Flexible subscription plans
• Multi-Model Support: Choose from various AI models for question generation
• Question Caching: Fast question delivery with intelligent caching
• Google OAuth: Sign in with your Google account

🛠️ Tech Stack

Frontend

• Next.js 16 - React framework
• TypeScript - Type-safe development
• Tailwind CSS - Utility-first CSS framework
• Framer Motion - Animation library
• Recharts - Data visualization
• NextAuth - Authentication

Backend

• FastAPI - Modern Python web framework
• PostgreSQL - Relational database
• SQLAlchemy - ORM
• Pinecone - Vector database for RAG
• OpenAI/Groq - AI model providers
• Redis - Caching layer
• bcrypt - Password hashing

📁 Project Structure

Exam/
├── backend/                 # FastAPI backend
│   ├── main.py             # Main application entry
│   ├── database.py         # Database models and setup
│   ├── rag_service.py      # RAG implementation
│   ├── model_service.py    # AI model management
│   ├── requirements.txt    # Python dependencies
│   └── .env               # Environment variables
├── exam-app/               # Next.js frontend
│   ├── src/
│   │   ├── app/           # Next.js app directory
│   │   └── components/    # React components
│   ├── package.json       # Node dependencies
│   └── next.config.js     # Next.js configuration
├── package.json           # Root package.json for running both servers
├── start.bat             # Windows batch startup script
├── start.ps1             # PowerShell startup script
└── STARTUP_GUIDE.md      # Detailed startup instructions

🔧 Installation

Prerequisites

• Python 3.8+
• Node.js 16+
• PostgreSQL
• Redis (optional, for caching)

Setup

1. Clone the repository

   git clone <repository-url>
   cd Exam

2. Install dependencies

   # Install root dependencies
   npm install
   
   # Install backend dependencies
   cd backend
   pip install -r requirements.txt
   cd ..
   
   # Install frontend dependencies
   cd exam-app
   npm install
   cd ..

3. Configure environment variables

   Create a .env file in the backend folder:

   DATABASE_URL=postgresql://user:password@localhost:5432/examai
   PINECONE_API_KEY=your_pinecone_api_key
   OPENAI_API_KEY=your_openai_api_key
   REDIS_URL=redis://localhost:6379
   PORT=8000

4. Run the application

   npm run dev

   The application will be available at:

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

📖 Documentation

• Startup Guide - Detailed instructions for running the application
• Git Guide - Monorepo workflow and Git best practices
• Troubleshooting - Common issues and solutions
• PRD Compliance Check - Product requirements compliance
• Migration Guide - Database migration information

🔄 Git Workflow

This is a monorepo - both frontend and backend are in one Git repository.

# Quick start
git add .
git commit -m "feat: Your feature description"
git push origin master

# Or use the helper script
git-commit.bat

For detailed Git workflows, see GIT_GUIDE.md

🎯 Usage

1. Sign Up/Login: Create an account or sign in with Google
2. Upload Study Materials: Upload PDFs for RAG-based question generation
3. Generate Questions: Select subject, difficulty, and exam type
4. Take Exams: Practice with AI-generated questions
5. Track Progress: View your performance analytics
6. Manage Subscription: Upgrade for premium features

🔑 API Endpoints

Authentication

• POST /auth/signup - Register new user
• POST /auth/login - Login user
• POST /auth/google-signin - Google OAuth login

Question Generation

• POST /generate-questions - Generate exam questions
• GET /models - Get available AI models
• GET /exam-types - Get supported exam types

Document Management

• POST /upload-document - Upload study material
• GET /documents - List uploaded documents

Performance

• GET /performance/analytics - Get performance analytics
• GET /performance/subject-wise - Subject-wise performance

Subscription

• GET /subscription/plans - Get subscription plans
• POST /subscription/subscribe - Subscribe to a plan

For complete API documentation, visit http://localhost:8000/docs after starting the backend.

🧪 Testing

Backend Tests

cd backend
pytest

Frontend Tests

cd exam-app
npm test

🚢 Deployment

Backend Deployment

The backend can be deployed to platforms like:

• Render
• Heroku
• AWS EC2
• Google Cloud Run

Frontend Deployment

The frontend can be deployed to:

• Vercel (recommended for Next.js)
• Netlify
• AWS Amplify

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License.

👥 Authors

• Your Name - Initial work

🙏 Acknowledgments

• OpenAI for GPT models
• Pinecone for vector database
• FastAPI and Next.js communities

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Need Help? Check out the STARTUP_GUIDE.md for detailed setup instructions.