Lecturify

Yo, this is Lecturify, a project I built to solve a problem every student has: taking messy unorganized screen captures of white boards during a lecture and trying to make sense of them later.

Lecturify uses OCR to read the messy text and an LLM to act like a strict professor, restructuring your notes into a clean, logical, connected study guide.

Features

• Handwriting to Text: Uses PaddleOCR to scrape text from images of your notes.

• AI Restructuring: Uses Microsoft's Phi-3 (via HuggingFace) to fix grammar, group related topics, and add headers.

• PDF Generation: Instantly converts the cleaned-up Markdown into a downloadable PDF.

• Web Interface: A simple, clean UI built with FastAPI and Jinja2 templates.

Tech Stack

Under the hood:

• Backend: Python, FastAPI

• AI/ML: PaddleOCR (for reading text), Transformers + Phi-3 (for main stuff) and PyTorch

• Frontend: HTML/CSS (Jinja2 Templates)

• Utilities: xhtmlpdf2 (PDF Generation), ngrok for tunneling

How to Run This

You will need a GPU if you want to run it locally !! otherwise, Colab is your friend; just open the notebook in colab and run all the cells.

1. Clone the repo

   git clone http://github.com/ZohaibHassan16/lecturify.git cd lecturify

2. Install Dependencies

It's a bit heavy so i recommend using a virtual environment

   pip install -r requirements.txt

3. Run the app

   The code is split into main.py (server) and logic.py (AI engine)

   python main.py
   

Or if you are using uvicorn directly

uvicorn main:app --reload

4. Open it

   Go to https://localhost:8000 (or whatever localhost it spits out) in your browser.