A GMU November Hackathon Project

ClassyAI is a lightweight Flask-based backend + HTML/JS frontend tool that recommends the best professors for a student’s upcoming classes. Using a mock dataset of realistic professor ratings and an AI explanation generator, ClassyAI helps students build smarter schedules quickly.

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🚀 Features

🔍 Professor Recommendation • Enter classes (e.g., “CS 112”, “MATH 113”) • Backend filters professors from a dataset • Ranks them by rating, difficulty, and number of reviews

🤖 AI-Generated Explanation • Uses the OpenAI API to explain why those professors were recommended • Provides human-like reasoning in plain English

🗂 Mock Dataset Included • 30+ realistic professors • Ratings, difficulty score, and review counts

🧪 API Endpoint • POST /recommend • Accepts JSON: " { "courses": ["CS 112", "MATH 113"] }" • Returns ranked professors + AI explanation

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🧰 Tech Stack

Backend: Python, Flask, OpenAI API Frontend: HTML, CSS, JavaScript Dataset: JSON (mock_professors.json) Tools: Thunder Client / Postman for testing

📁 Project Structure /Hackathon Project │── app.py # Flask backend + AI integration │── requirements.txt # Python dependencies │── index.html # Simple frontend │── /data │ └── mock_professors.json │── README.md # (this file) │── .gitignore # prevents venv from uploading

▶️ How to Run the Backend (Local)

1. Install dependencies pip install -r requirements.txt

2. Set your OpenAI API Key Mac/Linux: export OPENAI_API_KEY=your-key-here Windows CMD: set OPENAI_API_KEY=your-key-here

3. Start the server python app.py

   The backend runs at: http://localhost:5000

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⚡ Testing the API (Thunder Client) 1. Open VS Code → Thunder Client 2. Click New Request 3. Set method to POST 4. URL: http://localhost:5000/recommend 5. Click Body → JSON 6. Paste: { "courses": ["CS 112", "MATH 113"] } 7. Send → You get ranked professors + explanation.

🎨 Frontend

The frontend (index.html) allows students to: • Enter classes • Click “Generate Recommendations” • Display ranked professors + AI explanation

Uses simple fetch() calls to connect to the backend.

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👥 Team Roles

Backend • Flask API • Professor dataset • Ranking logic • OpenAI for explanations • Endpoint with Thunder Client

Frontend • HTML/CSS/JS UI • Fetch() request to backend • Output & layout

Dataset + UX • mock_professors.json • UI text, tested flows

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🏁 Future Improvements • Real RateMyProfessors scraping • Real class scheduling integration • Login system • Save favorite professors • Graphical dashboard

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📜 License

MIT License Free to use, modify, and expand.