A versatile web scraping tool that extracts structured data from websites through an intuitive web interface.

• Web Interface: User-friendly UI for entering URLs and viewing results
• Smart Content Extraction: Automatically identifies and extracts:
  • Page title and headings
  • Main paragraphs
  • Links with text and URLs
  • Meta information
• Results Visualization: View extracted data in multiple formats:
  • Summary view
  • Raw JSON data
  • Structured paragraphs and links

• Python 3.6+
• Flask
• BeautifulSoup4
• Requests

pip install scrape_dat

git clone https://github.com/yourusername/scrape_dat.git
cd scrape_dat
pip install -e .

1. Start the web server:

python web_app.py

2. Open your browser and navigate to http://127.0.0.1:5000
3. Enter a URL to scrape and click "Scrape Data"
4. View the structured results

# Basic usage
scrape-dat https://example.com

# For more options
scrape-dat --help

1. The application sends a request to the specified URL
2. It parses the HTML content using BeautifulSoup
3. Various elements are extracted:
   • Title from the <title> tag
   • Headings from <h1>, <h2>, and <h3> tags
   • Paragraphs from <p> tags
   • Links from <a> tags
   • Meta information from <meta> tags
4. The extracted data is presented in a structured format

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request