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sizwinz / repository
StudySage 🧠 – An offline, AI-powered note assistant that helps students summarize notes, generate quizzes, extract handwritten text from images (OCR), and export content to PDF—all from a sleek terminal or desktop GUI.
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StudySage is a high-fidelity, privacy-first AI study assistant. It helps you transform raw notes, scanned lectures, and screenshot captures into structured study guides, concise summaries, and interactive multiple-choice question boards.
StudySage runs on a unified core engine, powering five distinct user interfaces depending on your workflow: a single-port React web app, a Streamlit page, a native desktop GUI, a Telegram chatbot, or a traditional terminal command line.
distilbart) or online via the Hugging Face Inference API.StudySage/
├── assets/ # Branding materials & images
│ └── images/ # Application logo files
├── core/ # Core Business Logic Layer (Single Source of Truth)
│ ├── __init__.py
│ ├── export_pdf.py # ReportLab PDF compilation
│ ├── io.py # Unified document loaders
│ ├── ocr_reader.py # OpenCV image preprocessing & Tesseract OCR
│ ├── quiz_gen.py # NLTK keyword-based quiz generator
│ └── summarize.py # Seq2Seq offline/online summarization engine
├── apps/ # Interfaces Layer
│ ├── api/ # FastAPI REST backend service
│ ├── web_app/ # Modern React + TypeScript (Vite) Single Page App
│ ├── streamlit_app/ # Glassmorphic Streamlit web interface
│ ├── gui/ # CustomTkinter Dark/Light desktop GUI
│ ├── cli/ # Figlet-styled interactive terminal CLI
│ └── telegram_bot/ # Asynchronous telegram chatbot daemon
├── tests/ # Test suite directory
├── config.py # Global settings & text limits
├── requirements.txt # Core Python dependencies
├── packages.txt # System package dependencies
└── README.md
# Clone the repository
git clone https://github.com/sizwinz/StudySage-Offline-Online-AI-Note-Assistant.git
cd StudySage-Offline-Online-AI-Note-Assistant
# Create and activate a virtual environment
python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate
# Install dependencies
pip install -r requirements.txt
PATH.brew install tesseract
sudo apt install tesseract-ocr
You can build the React frontend and serve it alongside the FastAPI backend on a single port (8000).
# 1) Build the static React application
cd apps/web_app
npm install
npm run build
# 2) Launch the FastAPI server from the root directory
cd ../..
python apps/api/server.py
Open http://localhost:8000/ in your browser.
| Interface | Platform | Commands |
|---|---|---|
| Vite Development Server | Web | cd apps/web_app && npm run dev (React app running on port 5173, requires FastAPI server running) |
| Streamlit Page | Web | streamlit run apps/streamlit_app/app.py |
| Desktop GUI | Desktop | python apps/gui/gui.py |
| Telegram Bot | Telegram | cd apps/telegram_bot && cp bot_config.sample.json bot_config.json (Add bot credentials and run python telegram_bot.py) |
| CLI Terminal | Shell | python apps/cli/main.py |
| Mode | Internet Required | Privacy | Processing Speed | Document Limits |
|---|---|---|---|---|
| Offline | No | Local only | Moderate (CPU/GPU) | Up to ~20,000 words |
| Online | Yes | Hugging Face API | Fast | ~800 words / 4,000 chars per call |
Ensure all application paths point to the unified configuration folder by running the pytest suite:
pytest tests/test_output_dir.py
This project is licensed under the MIT License — see the LICENSE file for details.