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Pavani Verma
Pavani Verma

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AI Study Notes Simplifier: Making Study Sessions Easier with Open-Source AI πŸ“šπŸ€–

#devchallenge #hf26challenge

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission πŸ¦₯

AI Study Notes Simplifier: Making Study Sessions Easier with Open-Source AI πŸ“šπŸ€–

The Problem

Students often spend a lot of time understanding difficult study notes, preparing summaries, and creating revision material. Doing these tasks manually can make studying more time-consuming than necessary.

I wanted to build a simple tool that helps students turn their notes into easier learning material.

My Solution

I built AI Study Notes Simplifier, a Python web application that uses an open-source AI model to help students understand their notes.

Features

  • Simple Explanations: Rewrites study notes in easier language.
  • Short Summaries: Produces a concise summary of the provided notes.
  • Revision Points: Generates important points for quick revision.
  • Quiz Questions: Creates practice questions with answers.

Technology Stack

  • Python
  • Streamlit
  • Hugging Face Transformers
  • FLAN-T5 Small (google/flan-t5-small)

Why Open-Source AI Matters

This project uses an open-source model through Hugging Face Transformers instead of relying on a paid, hosted AI API.

After downloading the model, users can run inference locally on a compatible computer. This can reduce dependence on external AI services and give users more control over their study notes.

The initial model download requires an internet connection. The model's answers may also be incomplete or incorrect, so students should verify important information.

How to Run the Project

  1. Download or clone the repository.
  2. Install the dependencies using pip install -r requirements.txt.
  3. Run the application using python -m streamlit run app.py.
  4. Paste your notes, select a task, and click Simplify my notes.

Project Links

GitHub Repository: https://github.com/pavaniverma18-blip/ai-study-notes-simplifier

Demo: Add a screenshot or a short screen recording of the working application.

What I Learned

Building this project helped me explore how Python applications can integrate open-source language models. I also learned how Streamlit can be used to create an interactive interface without building a separate frontend.

What's Next?

I would like to add PDF uploads, improve the handling of longer notes, and introduce better ways to verify AI-generated answers.

This is an early prototype, and I plan to improve it through testing and feedback.

Built as part of the Hacktoberfest 2026 Open-Source AI Challenge.

#devchallenge #hf26challenge #Hacktoberfest #Python #OpenSourceAI

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