Webpage Summarizer using Hugging Face

This project is a Python-based tool that scrapes textual content from a webpage and generates a concise summary using a Large Language Model (LLM) from Hugging Face. It automates the full pipeline—from text extraction and cleaning to summarization—using a simple function call with a webpage URL.

⚙️ Tech Stack

• Python
• BeautifulSoup – Web scraping and HTML parsing
• Hugging Face Transformers – Text summarization using pre-trained LLMs
• Google Colab or Kaggle – Notebook-based execution environment

🚀 How It Works

1. The user provides a webpage URL.
2. The script fetches and parses the HTML content.
3. Irrelevant elements (scripts, styles, etc.) are removed and the text is cleaned.
4. A pre-trained Hugging Face summarization model (e.g., Mistral 7B) generates a concise summary of the webpage content.

📦 Example Usage

display_summary("https://example.com")

🧾 Output

• A short, coherent, and readable summary of the webpage’s main content.

📌 Notes

• Designed for educational and prototyping purposes.
• Works best on text-heavy webpages (articles, blogs, documentation).
• Model choice can be swapped easily depending on available compute resources.