README.md

metadata

title: SnapSummaryAI
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: docker
app_file: app.py
pinned: false

SnapSummaryAI – YouTube & Web Summarizer 🚀

SnapSummaryAI is a lightweight Generative AI-powered summarization app that extracts and summarizes content from YouTube videos and web URLs into concise, readable summaries using Groq LLMs and LangChain.
Built with Streamlit, it is designed for fast inference, clean UX, and easy deployment on Hugging Face Spaces.

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🚀 Live Demo

Try the app here:
👉 https://huggingface.co/spaces/viki77/SnapSummaryAI

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✨ Features

• URL-based summarization
  • YouTube videos (non-private, non-shorts)
  • Articles and general web pages
• Fast inference using Groq-hosted LLMs
• Context-aware summaries (up to 300 words)
• Clean Streamlit user interface
• Secure API key handling (user-provided, not hardcoded)
• Hugging Face Spaces compatible

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🛠️ Tech Stack

• Python
• Streamlit – UI framework
• LangChain – LLM orchestration
• Groq LLMs – High-performance inference
• YoutubeLoader & UnstructuredURLLoader – Content ingestion

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📂 Project Structure

SnapSummaryAI-GenAI │
├── app.py Main Streamlit application
├── requirements.txt Project dependencies
├── README.md Project documentation
└── .gitignore Ignored files (env, venv, cache)

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🚀 How It Works

1. The user provides:
   • A Groq API key
   • A YouTube or website URL
2. The application:
   • Validates inputs
   • Loads content using the appropriate loader
   • Sends extracted text to a Groq-hosted LLM via LangChain
3. A concise AI-generated summary is displayed in the UI

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▶️ Running Locally

Clone the repository
git clone https://github.com/vikrant-honbute/SnapSummaryAI-GenAI.git
cd SnapSummaryAI-GenAI

Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate (macOS/Linux)
venv\Scripts\activate (Windows)

Install dependencies
pip install -r requirements.txt

Run the application
streamlit run app.py

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🔑 API Key Handling

• The Groq API key is entered via the Streamlit sidebar
• The key is not stored, not logged, and not committed
• Safe for local use, public demos, and Hugging Face Spaces

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🌐 Deployment (Hugging Face Spaces)

SnapSummaryAI is fully compatible with Hugging Face Spaces.

Deployment steps:

1. Create a new Space and select Streamlit
2. Connect this GitHub repository
3. Ensure app.py and requirements.txt are present
4. Launch the Space

Users can securely provide their own Groq API key from the UI.

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⚠️ Limitations

• Does not support:
  • Private or restricted YouTube videos
  • Extremely large web pages without preprocessing
• Summary length is currently fixed

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📌 Future Improvements

• Streaming summaries (token-by-token)
• Chapter-wise summaries for long videos
• Multi-language summarization
• Chat-style conversational memory
• UI theming and dark mode

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👨‍💻 Author

Vikrant Honbute
AI / ML Engineer | GenAI & LLM Applications
GitHub: https://github.com/vikrant-honbute

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📜 License

This project is open-source and intended for educational and demonstration purposes.