Study Notes AI Agent

Study Notes AI Agent is an AI-powered Streamlit app that summarizes PDF study notes and generates exam-style quizzes.

The app allows a student to upload a PDF, extract study content, generate a clean summary, and create quiz questions from the same material. It was built to practice AI-assisted education workflows, PDF processing, prompt-based generation, and Streamlit app development.

Project Overview

This project focuses on helping students convert long study notes into useful revision material.

The workflow is simple:

1. Upload a PDF file.
2. Extract text from the PDF.
3. Generate a structured summary.
4. Generate an exam-style quiz from the extracted content.

The project uses Python, Streamlit, PDF text extraction, and OpenRouter/Gemini model access for AI-powered generation.

Learning Goals

The goal of this project was to explore how AI can assist students by transforming study material into structured summaries and exam-style revision content.

The project combines PDF processing, prompt engineering, AI model integration, and educational workflow design into a simple student-focused application.

Features

PDF Text Extraction

• Upload PDF study material
• Extract readable text from PDF files
• Use the extracted text as the source for summary and quiz generation

AI Summary Generator

• Generates clean study summaries
• Organizes content into readable sections
• Helps students revise long notes faster

Quiz Generator

• Generates exam-style questions from the PDF content
• Includes multiple-choice questions
• Includes short questions
• Uses the original study material as context

Streamlit Interface

• Simple web app interface
• PDF upload support
• Summary output section
• Quiz output section

Tech Stack

• Python
• Streamlit
• OpenRouter API
• Gemini-compatible model access
• PyPDF
• OpenAI-compatible client
• python-dotenv

AI Model

The project uses:

google/gemini-2.0-flash-001

through OpenRouter.

Project Structure

study-notes-ai-agent/
├── app.py
├── agent.py
├── ai_utils.py
├── pdf_utils.py
├── check_key.py
├── requirements.txt
├── Screenshots/
└── README.md

Installation and Setup

Clone the repository:

git clone https://github.com/Shoaibstat876/study-notes-ai-agent.git

Go to the project folder:

cd study-notes-ai-agent

Create a virtual environment:

python -m venv .venv

Activate the virtual environment on Windows:

.\.venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Create a .env file in the project root:

OPENROUTER_API_KEY=your_openrouter_api_key_here
AI_PROVIDER=openrouter

Running the App

Start the Streamlit app:

streamlit run app.py

Then open the local URL shown in the terminal.

Security

API keys are loaded from environment variables.

The .env file is not included in this repository and should never be committed to GitHub.

Before making this repository public, make sure screenshots, logs, and documentation do not expose API keys or private environment variables.

What I Practiced

• Python app structure
• Streamlit app development
• PDF text extraction
• AI API integration
• Prompt-based summarization
• Quiz generation workflow
• Environment variable handling
• Educational AI tool design

Current Status

This is a functional AI study assistant prototype.

It can extract text from PDFs, generate study summaries, and create quiz-style questions. It is not a production learning management system yet.

Future Improvements

• Add support for larger PDFs
• Add better PDF text cleaning
• Add quiz difficulty levels
• Add downloadable summary and quiz files
• Add answer key generation
• Add user authentication
• Add saved study sessions
• Add export to PDF or DOCX
• Add deployment documentation
• Add automated tests for utility functions

Author

Muhammad Shoaib Abdul Shakoor

Focused on AI automation, full-stack development, backend APIs, frontend interfaces, and practical AI-native applications.