This commit adds a complete, web scraping API example that demonstrates how to get structured data from any website and use it like an API using the crawl4ai library with a minimalist frontend interface.
Core Functionality
- AI-powered web scraping with plain English queries
- Dual scraping approaches: Schema-based (faster) and LLM-based (flexible)
- Intelligent schema caching for improved performance
- Custom LLM model support with API key management
- Automatic duplicate request prevention
Modern Frontend Interface
- Minimalist black-and-white design inspired by modern web apps
- Responsive layout with smooth animations and transitions
- Three main pages: Scrape Data, Models Management, API Request History
- Real-time results display with JSON formatting
- Copy-to-clipboard functionality for extracted data
- Toast notifications for user feedback
- Auto-scroll to results when scraping starts
Model Management System
- Web-based model configuration interface
- Support for any LLM provider (OpenAI, Gemini, Anthropic, etc.)
- Simplified configuration requiring only provider and API token
- Add, list, and delete model configurations
- Secure storage of API keys in local JSON files
API Request History
- Automatic saving of all API requests and responses
- Display of request history with URL, query, and cURL commands
- Duplicate prevention (same URL + query combinations)
- Request deletion functionality
- Clean, simplified display focusing on essential information
Technical Implementation
Backend (FastAPI)
- RESTful API with comprehensive endpoints
- Pydantic models for request/response validation
- Async web scraping with crawl4ai library
- Error handling with detailed error messages
- File-based storage for models and request history
Frontend (Vanilla JS/CSS/HTML)
- No framework dependencies - pure HTML, CSS, JavaScript
- Modern CSS Grid and Flexbox layouts
- Custom dropdown styling with SVG arrows
- Responsive design for mobile and desktop
- Smooth scrolling and animations
Core Library Integration
- WebScraperAgent class for orchestration
- ModelConfig class for LLM configuration management
- Schema generation and caching system
- LLM extraction strategy support
- Browser configuration with headless mode
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