- Develop a GPT-based universal web scraper that intelligently interacts with users, adapts to different website structures, and accurately extracts the desired information.
- Normalize and validate user-provided URLs.
- Handle URL redirections and fetch website content.
- Process and parse the HTML content into a structured DOM tree.
- Generate natural language prompts to ask users about their scraping needs.
- Process user responses to identify their preferences and guide the extraction process.
- Analyze the website's structure, layout, and metadata to identify target elements and patterns.
- Leverage GPT to process descriptive text within the website for additional context.
- Create a tailored scraper to extract the identified elements and patterns.
- Apply heuristics or machine learning models to optimize scraper performance.
- Execute the generated scraper to extract the desired information.
- Perform data normalization and cleaning as necessary.
- Allow users to provide feedback to refine the process.
- Handle different website types, structures, and formats.
- Implement rate limiting, caching, and other optimizations to improve efficiency.
- Collect user feedback and performance metrics for iterative improvements.
- Employ transfer learning and other techniques to generalize knowledge across websites.
- Data analysts, data scientists, researchers, developers, and other professionals who need to extract information from websites for various purposes.
- Easy-to-use interface that guides users through the scraping process.
- Clear instructions and examples to help users understand how to provide input and interpret results.
- Ability to handle various website structures and formats without requiring extensive user input or customization.
- Reliable and accurate extraction of the desired information.
- Robust performance, even when faced with anti-bot measures or other challenges.
- Continuous improvements and updates based on user feedback and industry trends.