AutoGrader — AI-Powered Notebook Evaluation
AutoGrader is a full-stack application developed at the Vibe Hack Hackathon to automate grading of Jupyter Notebooks using LLMs. Upload a .zip of .ipynb files and a .txt rubric, and receive structured feedback and scores — instantly!
🚀 Features
- 📁 Upload multiple student notebooks in
.zip format - 📋 Upload a grading rubric in
.txt format - 💬 Uses Claude 3.5 Haiku via Rilla API for scoring and feedback
- 📊 Generates downloadable grading report as
.csv and detailed feedback .txt - 🖥️ Frontend built in React + Axios for smooth UX
- ⚡ FastAPI backend with async inference calls
🧱 Directory Structure
🔧 Backend (/backend)
1
2
3
4
5
6
7
8
9
10
| backend/
├── app.py # FastAPI server
├── graders/
│ ├── ipynb_parser.py # Notebook parser
│ ├── rubric_processor.py # Rubric parsing logic
│ └── grader.py # LLM interaction and grading logic
├── test_data/ # Sample submissions
├── templates/ # (Optional) For HTML rendering
├── static/ # Static frontend files
└── requirements.txt # Python dependencies
|
🌐 Frontend (/src)
1
2
3
4
5
6
7
8
9
| src/
├── components/
│ ├── FileUpload.js # Upload .zip and rubric
│ ├── ResultDisplay.js # Show results
├── services/
│ └── api.js # Axios for API requests
├── App.js # Main React app
├── index.js # Entry point
└── styles.css # Global styles
|
📦 API Endpoints
| Endpoint | Method | Description |
|---|
/grade/ | POST | Upload .zip + .txt rubric and get feedback |
/csv | GET | Download grading results as CSV |
/feedback | GET | Download all feedback as TXT |
/ | GET | Serves frontend (if hosted statically) |
⚙️ How It Works
- Upload: Send student notebooks + rubric via form.
- Parse: Extracts code cells from
.ipynb files. - Score: Sends rubric + code to LLM (Claude 3.5 Haiku).
- Output: Collects scores + feedback, saves as CSV & TXT.
🛠️ Technologies Used
- Frontend: React, Axios
- Backend: FastAPI, httpx, nbformat, pandas, matplotlib
- LLM API: Claude 3.5 via LiteLLM Proxy (Rilla)
- Deployment: (Locally hosted / ready for Dockerization)
🧪 Sample Output
CSV:
1
2
3
| notebook,score
student1.ipynb,88
student2.ipynb,73
|
Feedback (TXT):
1
2
3
4
5
6
7
| ===== student1.ipynb =====
Final Score: 88
What is wrong:
- Did not modularize functions
What can be improved:
- Use better variable names
|
🏁 Getting Started
📦 Install Backend Requirements
1
2
| cd backend
pip install -r requirements.txt
|
▶️ Run the Backend
1
| uvicorn app:app --reload
|
🌐 Start Frontend (if separate)
1
2
3
| cd src
npm install
npm start
|
🎯 Future Improvements
- 🔒 Authentication for teacher/student roles
- 📈 Visual plots per student performance
- 🧠 Auto-rubric generation from solution notebook
- 🧪 Support for multiple LLM providers
🏆 Built at Vibe Hack Hackathon
This project was developed in under 2 hours at the Vibe Hack Hackathon, blending GenAI and education for impact.