CourseGraph 🎓

AI-powered Cornell CS & Math course planning tool that generates personalized academic timelines based on your career goals.

🚀 Live Demo

Try it now: https://gen-ai-project-gamma.vercel.app

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✨ Features

• 🤖 AI Timeline Generation - Get 3 personalized course paths (Theorist, Engineer, Balanced) tailored to your career goals
• 📊 Data-Driven Insights - Difficulty and enjoyment ratings for 158 Cornell CS & MATH courses
• 💬 Intelligent Chat Advisor - Ask questions about courses, prerequisites, and career paths
• 🗺️ Interactive Timeline Visualization - Visual course planning with prerequisite tracking
• 🎯 Career-Focused Recommendations - Paths optimized for PhD research, industry, or versatile roles
• 📚 Study Materials - Curated learning resources for each course in your timeline

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🏗️ Architecture

Deployment

• Frontend: Vercel - Global CDN, automatic deployments
• Backend: Render - Persistent API server with auto-scaling

Tech Stack

• Frontend: Next.js 14, TypeScript, React, Tailwind CSS, Three.js (3D visualizations)
• Backend: FastAPI (Python), OpenAI GPT-4o-mini
• Data: 158 courses from Cornell Course Roster API + Rate My Professors sentiment analysis

API Endpoints

• GET /api/graph - Course graph with 158 nodes and prerequisite links
• POST /api/plan-timeline - Generate 3 personalized timeline paths
• POST /api/chat - Conversational course advisor
• GET /api/study-materials/{course_code} - Curated learning resources

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🛠️ Local Development

Want to run this project locally? Follow these steps:

Prerequisites

• Python 3.12+
• Node.js 18+
• OpenAI API key

Quick Start

1. Clone the repository

   git clone https://github.com/krishjana06/gen-ai-project.git
   cd gen-ai-project

2. Set up environment variables

   Create backend/.env:

   OPENAI_API_KEY=your_openai_api_key_here
   REDDIT_CLIENT_ID=your_reddit_client_id
   REDDIT_CLIENT_SECRET=your_reddit_client_secret

   Create frontend/.env.local:

   NEXT_PUBLIC_API_URL=http://localhost:8000

3. Install dependencies

   npm run install:all

4. Run the development servers

   npm run dev

   The app will be running at:

   • Frontend: http://localhost:3000
   • Backend: http://localhost:8000

Available Commands

npm run dev              # Run both frontend and backend
npm run backend          # Run only backend (FastAPI)
npm run frontend         # Run only frontend (Next.js)
npm run kill             # Stop all servers

npm run install:all      # Install all dependencies
npm run install:backend  # Install Python packages
npm run install:frontend # Install npm packages

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📊 Data Pipeline

1. Course Data Collection

   • Cornell Course Roster API (158 CS & MATH courses)
   • Prerequisite mapping from course catalog

2. Sentiment Analysis

   • Rate My Professors reviews via Reddit API
   • Difficulty and enjoyment scores (1-10 scale)

3. AI Timeline Generation

   • OpenAI GPT-4o-mini with custom prompts
   • Prerequisite-aware course sequencing
   • Career-goal optimization

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🎯 Use Cases

For Students:

• Plan your academic journey from sophomore to senior year
• Explore different career paths (PhD vs Industry)
• Discover courses aligned with your interests
• Understand prerequisite chains

For Advisors:

• Quick timeline generation for advising sessions
• Data-backed course recommendations
• Visualize prerequisite dependencies

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🚀 Deployment

This project is deployed using modern cloud platforms:

• Frontend (Vercel):

  • Automatic deployments on push to main
  • Global CDN for fast load times worldwide
  • Environment variable: NEXT_PUBLIC_API_URL

• Backend (Render):

  • Automatic deployments on push to main
  • Free tier with 750 hours/month
  • Environment variables: OPENAI_API_KEY, CORS_ORIGINS

Deploy Your Own

Frontend (Vercel):

1. Fork this repository
2. Connect to Vercel
3. Set environment variable: NEXT_PUBLIC_API_URL=https://your-backend-url.onrender.com
4. Deploy!

Backend (Render):

1. Create a new Web Service on Render
2. Connect your GitHub repository
3. Set build command: cd backend && pip install -r requirements.txt
4. Set start command: cd backend && uvicorn app.main:app --host 0.0.0.0 --port $PORT
5. Add environment variables (see .env.example)
6. Deploy!

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📝 Project Structure

gen-ai-project/
├── backend/
│   ├── app/
│   │   ├── api/          # FastAPI endpoints
│   │   ├── services/     # Business logic (timeline planner, chat)
│   │   └── main.py       # FastAPI app entry point
│   ├── data/             # Course data JSON files
│   └── requirements.txt
├── frontend/
│   ├── src/
│   │   ├── app/          # Next.js pages
│   │   ├── components/   # React components
│   │   ├── lib/          # API client, utilities
│   │   └── stores/       # State management (Zustand)
│   └── package.json
└── README.md

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🤝 Contributing

This is a personal project, but suggestions are welcome! Feel free to:

• Open an issue for bugs or feature requests
• Submit pull requests with improvements
• Share feedback on the deployed app

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🙏 Acknowledgments

• Cornell University - Course data from official roster
• OpenAI - GPT-4o-mini for AI-powered features
• Rate My Professors - Community sentiment data

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Live Demo: https://gen-ai-project-gamma.vercel.app