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| 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. | |
| --- | |
| ## 🚀 Live Demo | |
| Try the app here: | |
| 👉 **https://huggingface.co/spaces/viki77/SnapSummaryAI** | |
| --- | |
| ## ✨ 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 | |
| --- | |
| ## 🛠️ Tech Stack | |
| - Python | |
| - Streamlit – UI framework | |
| - LangChain – LLM orchestration | |
| - Groq LLMs – High-performance inference | |
| - YoutubeLoader & UnstructuredURLLoader – Content ingestion | |
| --- | |
| ## 📂 Project Structure | |
| SnapSummaryAI-GenAI | |
| │ | |
| ├── app.py Main Streamlit application | |
| ├── requirements.txt Project dependencies | |
| ├── README.md Project documentation | |
| └── .gitignore Ignored files (env, venv, cache) | |
| --- | |
| ## 🚀 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 | |
| --- | |
| ## ▶️ 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 | |
| --- | |
| ## 🔑 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 | |
| --- | |
| ## 🌐 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. | |
| --- | |
| ## ⚠️ Limitations | |
| - Does not support: | |
| - Private or restricted YouTube videos | |
| - Extremely large web pages without preprocessing | |
| - Summary length is currently fixed | |
| --- | |
| ## 📌 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 | |
| --- | |
| ## 👨💻 Author | |
| Vikrant Honbute | |
| AI / ML Engineer | GenAI & LLM Applications | |
| GitHub: https://github.com/vikrant-honbute | |
| --- | |
| ## 📜 License | |
| This project is open-source and intended for educational and demonstration purposes. | |