🤖 Study Buddy Matching with AI

An AI-powered backend system that intelligently pairs students with suitable study partners based on their study goals, preferred time, learning type, and personality traits. Built with FastAPI, this project enables seamless and customizable matching through a RESTful API.

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🚀 Features

• ✅ Matches students based on:
  • Study goal
  • Preferred study time
  • Study type (reading, video, group, etc.)
  • Personality traits (collaborative, adaptive, etc.)
• ⚡ FastAPI-powered backend
• 📦 JSON-based student data and config
• 📩 Simple and extendable matching logic
• 🐳 Docker support for deployment

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📁 Folder Structure

study-buddy/ ├── app/ │ ├── init.py │ ├── main.py # FastAPI endpoints │ ├── matcher.py # Matching logic │ └── pycache/ ├── data/ │ └── students.json # Sample student data ├── tests/ # (Optional) For unit tests ├── config.json # Configuration file ├── Dockerfile # For containerization ├── README.md # Project documentation ├── requirements.txt # Python dependencies └── schema.json # JSON schema for validation

📦 Requirements

Install dependencies using:

pip install -r requirements.txt

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▶️ Run the Application

Start the server with:

uvicorn app.main:app --reload

Then open in browser:

http://127.0.0.1:8000/docs

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🧪 Sample API Request

Endpoint: POST /match

Request Body:

{
  "student_id": "stu_1001",
  "goal": "Master Data Science",
  "preferred_study_time": "early_morning",
  "study_type": "reading",
  "personality": ["collaborative", "adaptive"]
}

Sample Response:

{
  "matched_with": "stu_1004",
  "compatibility_score": 87
}

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🐳 Docker Support

To build and run the Docker container:

docker build -t study-buddy .
docker run -p 8000:8000 study-buddy

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🔮 Future Improvements

• Frontend integration
• ML-based intelligent matching
• Match history and feedback loop
• Authentication system

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👩‍💻 Author

Ashritha Penke
LinkedIn
GitHub