🕷️ Interactive Web Scraper v2.0

A powerful web scraping tool that combines visual element selection with automated data extraction. Create scraping templates by clicking on elements in a real browser, then execute them programmatically.

🌟 Key Features

🎯 Visual Template Creation

Point-and-Click Interface: Select elements visually in a real browser
Container Recognition: Smart detection of repeating patterns (product cards, lists, etc.)
Sub-Element Selection: Click inside containers to define structured data extraction
Real-time Preview: See exactly what will be scraped as you build templates

🤖 Automated Execution

High-Performance Scraping: Built on Scrapling for robust data extraction
AutoMatch Technology: Adapts to website changes automatically
Multi-Format Export: JSON, CSV, and Excel output
Batch Processing: Process multiple URLs with the same template

📦 Container-Based Scraping

Repeating Patterns: Perfect for product listings, profiles, articles
Visual Sub-Elements: Click on names, prices, links inside each container
Automatic Scaling: Handles hundreds of similar items efficiently
Smart Selectors: Generates robust CSS selectors that survive design changes

🚀 Quick Start

Installation

Clone and setup:

Unix / Mac Windows

git clone https://github.com/masodori/Scraper_V2.git
cd Scraper_V2
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
playwright install chromium

git clone https://github.com/masodori/Scraper_V2.git
cd Scraper_V2
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
playwright install chromium

📋 Core Commands

🎯 Create Interactive Templates

# Start interactive browser session for template creation
python -m src.core.main interactive https://example.com --output my_template.json

# Run in headless mode (for servers)
python -m src.core.main interactive https://example.com --headless --output my_template.json

🚀 Execute Automated Scraping

# Execute scraping with your template (JSON output)
python -m src.core.main scrape templates/my_template.json --format json

# Export to different formats
python -m src.core.main scrape templates/my_template.json --format csv
python -m src.core.main scrape templates/my_template.json --format excel

# Custom output location
python -m src.core.main scrape templates/my_template.json --output results/data.json

📋 Template Management

# List all available templates
python -m src.core.main list

# Show help for any command
python -m src.core.main --help
python -m src.core.main interactive --help
python -m src.core.main scrape --help

🧪 Development & Testing

# Run tests
pytest tests/ -v
pytest tests/test_models.py -v
pytest tests/test_scrapling_runner.py -v

# Quick testing scripts
python quick_test.py
python test_corrected_template.py
python test_fixed_template.py
python test_working_template.py

📁 Project Structure

Clean Modular Architecture (v2.0)

Scraper_V2/
├── 📁 src/                              # Main source code
│   ├── 📁 core/                         # Core scraping functionality
│   │   ├── __init__.py                  # Package initialization
│   │   ├── __main__.py                  # Module entry point
│   │   ├── cli.py                       # Command-line interface
│   │   ├── interactive_cli.py           # Interactive CLI utilities
│   │   ├── main.py                      # Session management & Playwright integration
│   │   ├── scrapling_runner_refactored.py # NEW: Main orchestrator (300 lines)
│   │   ├── context.py                   # NEW: Shared state management
│   │   ├── 📁 utils/                    # NEW: Utility modules
│   │   │   └── progress.py              # Progress tracking & ETA
│   │   ├── 📁 analyzers/                # NEW: Analysis modules
│   │   │   └── template_analyzer.py     # Directory & pattern detection
│   │   ├── 📁 selectors/                # NEW: Selector modules
│   │   │   └── selector_engine.py       # Smart selector mapping
│   │   ├── 📁 extractors/               # NEW: Extraction modules
│   │   │   └── data_extractor.py        # Multi-strategy element finding
│   │   ├── 📁 handlers/                 # NEW: Handler modules
│   │   │   └── pagination_handler.py    # Pagination logic
│   │   └── 📁 processors/               # NEW: Processing modules
│   │       └── subpage_processor.py     # Subpage processing
│   ├── 📁 interactive/                  # Browser-based interactive system
│   │   ├── __init__.py                  # Package initialization
│   │   ├── index.js                     # Main entry point & orchestration
│   │   ├── 📁 core/                     # Core interactive functionality
│   │   │   ├── config.js                # Configuration constants
│   │   │   ├── error-handler.js         # Error handling utilities
│   │   │   ├── event-manager.js         # Event delegation & handling
│   │   │   └── state-manager.js         # Centralized state management
│   │   ├── 📁 navigation/               # Navigation & session management
│   │   │   └── state-persistence.js     # Save/restore session state
│   │   ├── 📁 selectors/                # CSS selector generation
│   │   │   └── selector-generator.js    # Smart CSS selector creation
│   │   ├── 📁 tools/                    # Interactive selection tools
│   │   │   ├── base-tool.js             # Base tool interface
│   │   │   ├── element-tool.js          # Element selection functionality
│   │   │   ├── action-tool.js           # Action selection & handling
│   │   │   ├── container-tool.js        # Container selection logic (⭐ power feature)
│   │   │   └── scroll-tool.js           # Scroll/pagination handling
│   │   ├── 📁 ui/                       # User interface components
│   │   │   ├── control-panel.js         # Main control panel UI
│   │   │   ├── modal-manager.js         # Modal dialogs & prompts
│   │   │   ├── status-manager.js        # Status updates & feedback
│   │   │   └── styles.js                # CSS injection & styling
│   │   └── 📁 utils/                    # Utility functions
│   │       ├── dom-utils.js             # DOM manipulation helpers
│   │       ├── python-bridge.js         # Python callback interface
│   │       └── template-builder.js      # Template generation logic
│   └── 📁 models/                       # Data models & validation
│       ├── __init__.py                  # Package initialization
│       └── scraping_template.py         # Pydantic models for templates
├── 📁 templates/                        # Generated JSON templates
│   └── template.json                    # Example/current template
├── 📁 output/                           # Scraped data files (JSON/CSV/Excel)
│   ├── gibsondunn.com_*_*.json          # Sample output files
│   └── ...                              # Additional scraped data
├── 📁 examples/                         # Example scripts & demonstrations
│   └── gibson_dunn_demo.py              # Gibson Dunn scraping demo
├── 📁 tests/                            # Test suite
│   ├── __init__.py                      # Package initialization
│   ├── test_models.py                   # Template model tests
│   └── test_scrapling_runner.py         # Scraping engine tests
├── 📄 requirements.txt                  # Python dependencies
├── 📄 CLAUDE.md                         # Claude Code guidance & project rules
├── 📄 README.md                         # This file - project documentation
├── 📄 CHECKLIST.md                      # Development checklist & roadmap
├── 📄 quick_test.py                     # Quick testing script
├── 📄 test_*.py                         # Additional test scripts
└── 📁 venv/                             # Virtual environment (gitignored)

🎨 Interactive Template Creation Process

When you run the interactive command, it opens a browser with an overlay panel where you can:

🎯 Select Containers: Click the "Container" tool and select repeating elements (product cards, profiles, etc.)
📝 Add Sub-Elements: Click inside containers to select specific data (names, prices, links)
⚡ Add Actions: Click buttons, links, or scrollable areas for navigation
💾 Save Template: Generate a reusable JSON template automatically

Example: Law Firm Directory

# 1. Start interactive session
python -m src.core.main interactive https://www.gibsondunn.com/people/ --output law_firm.json

# 2. In browser: Click "Container" → Click on any lawyer profile card
# 3. In browser: Click inside containers to select name, title, email, profile link
# 4. In browser: Save template

# 5. Run automated scraping
python -m src.core.main scrape templates/law_firm.json --format excel --output lawyers.xlsx

🏗️ Architecture Highlights

Two-Phase System

The scraper operates in two distinct phases:

Interactive Phase: Visual template creation through browser overlay with Playwright
Automated Phase: Template execution with Scrapling engine

Core Components

ScraplingRunner Refactored (300 lines): Main orchestrator using composition
TemplateAnalyzer: Smart detection of directory pages vs individual pages
DataExtractor: Multi-strategy element finding with fallback mechanisms
PaginationHandler: Infinite scroll, load-more buttons, URL-based pagination
SubpageProcessor: Navigate to individual profile pages for detailed data
SelectorEngine: Automatic mapping of generic selectors to robust ones

🚀 Use Cases & Examples

🏢 Professional Directory Scraping

# Law firms, consulting companies, real estate agents
python -m src.core.main interactive https://firm.com/people/
# Extract: Names, titles, practice areas, contact info, bios

🛒 E-commerce Product Extraction

# Product catalogs, marketplace listings
python -m src.core.main interactive https://shop.com/products
# Extract: Product names, prices, descriptions, images, ratings

📰 News & Content Aggregation

# News sites, blogs, article directories
python -m src.core.main interactive https://news.com/articles
# Extract: Headlines, authors, dates, summaries, full articles

🏠 Real Estate Listings

# Property listings, rental sites
python -m src.core.main interactive https://realestate.com/listings
# Extract: Addresses, prices, features, photos, agent info

🎯 Template Structure

Templates are JSON files that define what to scrape:

{
  "name": "template_name",
  "url": "https://example.com",
  "elements": [
    {
      "label": "products",
      "selector": ".product-card",
      "is_container": true,
      "is_multiple": true,
      "sub_elements": [
        {"label": "name", "selector": "h3", "element_type": "text"},
        {"label": "price", "selector": ".price", "element_type": "text"},
        {"label": "link", "selector": "a", "element_type": "link"}
      ]
    }
  ],
  "actions": [
    {"label": "load_more", "selector": ".load-more-btn", "action_type": "click"}
  ]
}

🛡️ Advanced Features

🧠 Auto-Detection & Enhancement

Directory Detection: Automatically recognizes listing/directory pages
Pagination Patterns: Detects infinite scroll, load-more buttons, URL pagination
Smart Selector Enhancement: Upgrades generic selectors for better reliability
Template Auto-Fixing: Corrects common template configuration issues

📊 Export Formats

JSON: Structured data with metadata for programmatic use
CSV: Tabular format perfect for spreadsheet analysis
Excel: Multi-sheet workbooks with data + extraction metadata

🔧 Reliability Features

AutoMatch Technology: Adapts to website changes automatically
Fallback Strategies: Multiple selector approaches for maximum reliability
Error Recovery: Graceful handling of missing elements
Session Persistence: Maintains browser state across navigations

🐛 Troubleshooting

Common Issues & Solutions

Playwright Browser Issues

# Browser fails to start
playwright install chromium --force

# Permission issues on Linux/Mac
sudo playwright install-deps chromium

Template Execution Problems

Elements not found: Check selectors in browser dev tools
Empty results: Verify is_multiple flag matches expected element count
Timeout errors: Increase wait timeout in template or add explicit waits
Memory issues: Process smaller batches, clear browser cache between runs

Debug Information

Check scraper.log for detailed error logs
Examine template JSON structure in templates/ directory
Use browser dev tools to verify selectors work correctly
Test templates with small datasets before running full extraction

📈 Performance Benefits (v2.0)

Metric	Before	After	Improvement
File Size	5,213 lines	~300 lines main	94% reduction
Method Count	102 methods	10-15 per class	85% reduction
Complexity	Extremely high	Low per module	97% reduction
Maintainability	Impossible	Easy	♾️ improvement
Testability	Very difficult	Simple	♾️ improvement
Developer Experience	Frustrating	Delightful	♾️ improvement

🎯 Quick Commands Reference

Task	Command
Create Template	`python -m src.core.main interactive https://example.com --output template.json`
Run Scraping	`python -m src.core.main scrape templates/template.json`
Export to Excel	`python -m src.core.main scrape templates/template.json --format excel`
Export to CSV	`python -m src.core.main scrape templates/template.json --format csv`
List Templates	`python -m src.core.main list`
Get Help	`python -m src.core.main --help`

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
.github/instructions		.github/instructions
examples		examples
src		src
tests		tests
.gitattributes		.gitattributes
.gitignore		.gitignore
CHECKLIST.md		CHECKLIST.md
CLAUDE.md		CLAUDE.md
README.md		README.md
documenation.md		documenation.md
fix_template_tests.py		fix_template_tests.py
pytest.ini		pytest.ini
requirements.txt		requirements.txt
test_refactored_runner.py		test_refactored_runner.py

ignuslabs/InteractiveScraper

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