Study-Simplify

A comprehensive study material processing platform that transforms educational content into structured study materials through AI-powered transcription, summarization, and question generation.

🔗 Live Demo

• Frontend: https://study-simplify-three.vercel.app/
• API: https://minor-study-simplify.onrender.com

Features

Core Functionality

• Multi-format File Processing: Upload and process PDFs, PowerPoint presentations, and images
• Intelligent Transcription: Extract text content using OCR and document parsing
• AI-Powered Summarization: Generate concise summaries with keyword extraction
• Question Generation: Create both subjective and objective questions for study practice
• Interactive Quiz Mode: Take quizzes with generated multiple-choice questions

Supported File Types

• PDF documents
• PowerPoint presentations (.pptx)
• Images (JPG, PNG, JPEG) with OCR processing

Architecture

Backend (FastAPI)

The backend API provides four main endpoints:

• /transcribe - Extract text from uploaded files
• /summarize - Generate summaries and extract keywords
• /generate-subjective-questions - Create open-ended questions
• /generate-questions - Generate multiple-choice questions

Frontend (React)

The React application provides an intuitive interface with:

• Upload Page: Drag-and-drop file upload with progress tracking
• Results Page: Display transcribed text with action buttons
• Summary Page: Show generated summaries and important keywords
• Question Pages: Display subjective questions and interactive quiz interface

Technology Stack

Backend

• FastAPI: Modern, fast web framework for building APIs
• PyPDF2: PDF text extraction
• python-pptx: PowerPoint processing
• Tesseract OCR: Image text recognition
• Transformers: T5 and BERT models for NLP tasks
• scikit-learn: TF-IDF and text processing
• spaCy: Named Entity Recognition

Frontend

• React: Component-based UI framework
• React Router: Client-side routing
• Tailwind CSS: Utility-first CSS framework
• Lucide React: Icon library

AI/ML Models

• T5 Transformer: Question generation
• BERT: Question-answer evaluation
• GloVe Embeddings: Semantic similarity for distractor generation
• TF-IDF: Keyword extraction and text analysis

Installation

Backend Setup

1. Clone the repository:

2. Create a virtual environment:

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Install Tesseract OCR:

   • Ubuntu/Debian: sudo apt-get install tesseract-ocr
   • Windows: Download from GitHub
   • macOS: brew install tesseract

5. Run the setup script to download models:

python setup.py

6. Start the server:

python main.py

The API will be available at http://localhost:8000

Frontend Setup

1. In a new terminal navigate to the frontend directory:

cd study-simplify

2. Install dependencies:

npm install

3. Start the development server:

npm run dev

The application will be available at http://localhost:3000

API Documentation

Endpoints

POST /transcribe

Upload and transcribe files to extract text content.

Request: Multipart form data with file Response:

{
  "success": true,
  "transcript": "extracted text content",
  "file_type": "application/pdf",
  "message": "File transcribed successfully"
}

POST /summarize

Generate summary and extract keywords from text.

Request:

{
  "text": "input text to summarize"
}

Response:

{
  "success": true,
  "important_words": ["keyword1", "keyword2"],
  "summary": "generated summary",
  "message": "Text summarized successfully"
}

POST /generate-subjective-questions

Create subjective/open-ended questions from text.

Request:

{
  "text": "input text",
  "num_questions": 5,
  "answer_style": "all",
  "use_evaluator": true
}

POST /generate-questions

Generate objective/multiple-choice questions.

Request:

{
  "text": "input text",
  "num_questions": 5,
  "num_options": 4
}

Configuration

Environment Variables

• TESSDATA_PREFIX: Path to Tesseract data directory
• MODEL_CACHE_DIR: Directory for caching downloaded models

Model Configuration

The system automatically downloads required models on first run:

• T5-base model for question generation
• BERT model for QA evaluation
• GloVe embeddings for semantic similarity

Development

Project Structure

study-simplify/
├── main.py                 # FastAPI application
├── transcript.py           # File transcription module
├── summarize.py           # Text summarization
├── sub_q_gen/             # Subjective question generation
├── obj_q_gen/             # Objective question generation
├── setup.py               # Model setup and downloads
├── frontend/              # React application
└── debug/                 # Debug output files

Adding New Features

1. New File Types: Extend the transcript.py module
2. Custom Models: Modify model loading in respective modules
3. API Endpoints: Add new routes in main.py
4. Frontend Pages: Create new components and routes

Deployment

Backend Deployment

The backend is deployed on Render with automatic deployments from the main branch.

Frontend Deployment

The React application is deployed on Vercel with automatic deployments.

Performance Considerations

• Memory Usage: ML models require significant RAM (2-4GB recommended)
• Processing Time: Large files may take several minutes to process
• Model Caching: Models are cached after first download to improve startup time

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

For issues and questions:

• Create an issue on GitHub
• Check the API documentation at /docs endpoint
• Review debug files in the debug/ directory for troubleshooting

Acknowledgments

• Hugging Face Transformers for pre-trained models
• OpenAI for NLP research and methodologies
• FastAPI and React communities for excellent documentation