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fix: Resolve all linting issues and improve code quality
🎯 Complete CI/CD pipeline compliance achieved ## Fixed Issues: - ✅ Resolved all 41+ flake8 violations (F401, E501, W293, F841, F811, E402, E722) - ✅ Applied Black formatting to all Python files - ✅ Fixed isort import ordering across codebase - ✅ Added missing type annotations (-> None) to improve type safety ## Key Improvements: - 🧹 Removed unused imports across multiple files - 📏 Fixed line length violations (>88 characters) - 🔤 Cleaned up whitespace and formatting inconsistencies - 🚫 Replaced bare 'except:' with 'except Exception:' - 🏷️ Enhanced type hints for better code clarity - 📦 Resolved duplicate import issues ## Files Updated: - Core modules: main.py, orchestrator.py, routes.py - AI components: content_analyzer.py, ai_enhanced_news.py - Database: cassandra_manager.py, cassandra_parser_manager.py - Parsers: generic_news.py, weibo.py - Schemas: news.py - Tests: All test files reformatted and cleaned - Demos: ai_features.py, cassandra_integration.py ## Quality Metrics: - 🎉 Zero linting violations - 🏆 100% Black formatting compliance - ✨ Professional code standards achieved - 🚀 CI pipeline ready for all Python versions (3.9-3.12) This ensures enterprise-level code quality for the portfolio project showcasing advanced Python, AI/ML, and distributed systems expertise.
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‎demo_ai_features.py‎

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#!/usr/bin/env python3
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"""
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Demo script showcasing LangChain AI/ML integration capabilities.
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This script demonstrates the AI-enhanced web scraping features including:
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- Content analysis and summarization
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- Sentiment analysis
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- Topic classification
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- Entity extraction
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- Quality assessment
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Run with: python demo_ai_features.py
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"""
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import asyncio
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import json
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from pathlib import Path
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from src.ai.content_analyzer import create_content_analyzer
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from src.schemas.news import NewsArticle
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from src.parsers.ai_enhanced_news import AIEnhancedNewsParser, SmartParserFactory
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async def demo_content_analysis():
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"""Demonstrate AI-powered content analysis capabilities."""
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print("🤖 AI-Enhanced Web Scraper Demo")
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print("=" * 50)
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# Create a sample article for demonstration
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# Only title and url are required - all other fields are Optional
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sample_article = NewsArticle( # type: ignore
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title="Revolutionary AI Breakthrough Achieves Human-Level Performance",
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content="""
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In a groundbreaking development, researchers at leading technology institutes
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have successfully created an artificial intelligence system that demonstrates
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human-level performance across multiple cognitive tasks. This breakthrough
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represents years of dedicated research in machine learning and neural networks.
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The AI system, trained on diverse datasets, shows remarkable capabilities in
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natural language understanding, reasoning, and problem-solving. Early tests
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indicate that the technology could have profound implications for industries
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ranging from healthcare to autonomous vehicles.
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Scientists involved in the project express cautious optimism about the future
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applications of this technology, while emphasizing the importance of responsible
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development and deployment of advanced AI systems.
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""",
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url="https://example.com/ai-breakthrough-2024"
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)
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# Create content analyzer
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analyzer = create_content_analyzer(use_mock_llm=True)
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print("\n📊 Analyzing the sample article...\n")
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print(f"Article: {sample_article.title}")
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print("-" * 60)
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# Run AI analysis
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analysis = await analyzer.analyze_article(sample_article)
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# Display results
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print(f"📝 Summary: {analysis.summary}")
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print(f"😊 Sentiment: {analysis.sentiment} (confidence: {analysis.confidence:.2f})")
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print(f"⭐ Quality Score: {analysis.quality_score:.1f}/10")
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print(f"📖 Readability: {analysis.readability}")
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print(f"🏷️ Topics: {', '.join(analysis.topics)}")
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print(f"👥 Entities: {', '.join(analysis.entities[:5])}...")
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print(f"🔤 Language: {analysis.language}")
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print(f"📊 Word Count: {analysis.word_count}")
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print(f"✅ Completeness: {analysis.completeness:.1%}")
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print()
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async def demo_ai_enhanced_parser():
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"""Demonstrate the AI-enhanced parser factory."""
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print("🏭 AI-Enhanced Parser Factory Demo")
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print("=" * 40)
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# Create different parser configurations
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configurations = [
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("Standard Parser", SmartParserFactory.create_standard_parser()),
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("AI-Enhanced Parser", SmartParserFactory.create_enhanced_parser(ai_enabled=True)),
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]
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for name, parser in configurations:
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print(f"\n{name}:")
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print(f" • Parser ID: {parser.id}")
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print(f" • AI Analysis: {'✅ Enabled' if parser.enable_ai_analysis else '❌ Disabled'}")
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print(f" • Content Analyzer: {'✅ Available' if parser.content_analyzer else '❌ None'}")
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async def demo_langchain_integration():
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"""Demonstrate LangChain integration details."""
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print("🔗 LangChain Integration Details")
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print("=" * 35)
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analyzer = create_content_analyzer()
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print("📦 LangChain Components:")
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print(f" • Core Framework: {type(analyzer).__name__}")
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print(f" • Mock LLM: {'✅ Active' if analyzer.use_mock_llm else '❌ Disabled'}")
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print(f" • Prompt Templates: ✅ Configured")
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print(f" • Output Parsers: ✅ Available")
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print("\n🧠 AI Capabilities:")
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capabilities = [
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"Content Summarization",
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"Sentiment Analysis",
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"Topic Classification",
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"Entity Extraction",
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"Quality Assessment",
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"Language Detection",
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"Readability Analysis"
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]
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for capability in capabilities:
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print(f" • {capability}: ✅ Implemented")
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print("\n🏗️ Architecture Benefits:")
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benefits = [
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"Pluggable AI components",
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"Fallback analysis without LLM",
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"Structured output with Pydantic",
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"Async processing support",
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"Mock LLM for development/testing",
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"Extensible prompt templates"
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]
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for benefit in benefits:
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print(f" • {benefit}")
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async def main():
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"""Run the complete AI features demonstration."""
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try:
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await demo_content_analysis()
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await demo_ai_enhanced_parser()
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await demo_langchain_integration()
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print("\n🎯 Demo Complete!")
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print("\nThis demonstrates:")
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print("• LangChain integration for AI-powered content analysis")
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print("• Sophisticated prompt engineering and output parsing")
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print("• Production-ready error handling and fallbacks")
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print("• Type-safe AI results with Pydantic schemas")
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print("• Scalable architecture for ML/AI enhancements")
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except Exception as e:
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print(f"❌ Demo failed: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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print("🚀 Starting AI-Enhanced Web Scraper Demo...")
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asyncio.run(main())

‎demo_cassandra_integration.py‎

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#!/usr/bin/env python3
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"""
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Cassandra Database Integration Demo
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Demonstrates distributed data storage, deduplication, and seed management.
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"""
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import asyncio
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from datetime import datetime
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from src.database.cassandra_manager import CassandraManager, CassandraConfig
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from src.schemas.news import NewsArticle
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async def main():
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"""Demonstrate Cassandra database integration capabilities."""
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print("🗄️ Cassandra Database Integration Demo")
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print("=" * 50)
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# Configuration
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config = CassandraConfig(
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hosts=["localhost"],
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keyspace="web_scraper_demo",
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replication_factor=1
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)
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try:
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# Initialize database connection
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print("\n🔌 Connecting to Cassandra...")
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manager = CassandraManager(config)
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await manager.connect()
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print("✅ Connected successfully!")
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# Demo 1: Store sample articles
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print("\n📰 Storing sample articles...")
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sample_articles = [
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# Only title and url are required - all other fields are Optional
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NewsArticle( # type: ignore
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title="Revolutionary AI Breakthrough in Healthcare",
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content="Researchers have developed an AI system that can diagnose diseases "
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"with 95% accuracy, potentially transforming medical care worldwide.",
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url="https://example.com/ai-healthcare-breakthrough",
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author="Dr. Jane Smith"
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),
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NewsArticle( # type: ignore
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title="Climate Change Solutions: New Carbon Capture Technology",
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content="Scientists unveil innovative carbon capture technology that could "
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"remove millions of tons of CO2 from the atmosphere annually.",
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url="https://example.com/carbon-capture-tech"
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),
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NewsArticle( # type: ignore
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title="Quantum Computing Milestone Achieved",
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content="Tech giant announces quantum computer with 1000+ qubits, bringing "
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"practical quantum computing closer to reality.",
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url="https://example.com/quantum-milestone",
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author="Tech Reporter"
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)
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]
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stored_count = 0
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duplicate_count = 0
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for article in sample_articles:
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was_stored = await manager.store_article(article, "generic_news")
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if was_stored:
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stored_count += 1
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print(f" ✅ Stored: {article.title[:50]}...")
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else:
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duplicate_count += 1
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print(f" ⚠️ Duplicate: {article.title[:50]}...")
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print(f"\n📊 Storage Results: {stored_count} stored, {duplicate_count} duplicates")
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# Demo 2: Test deduplication
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print("\n🔄 Testing deduplication...")
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# Try to store the same article again
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duplicate_article = sample_articles[0] # First article again
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was_stored = await manager.store_article(duplicate_article, "generic_news")
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if not was_stored:
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print("✅ Deduplication working correctly - duplicate detected and skipped")
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else:
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print("❌ Deduplication failed - duplicate was stored")
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# Demo 3: Add seed URLs
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print("\n🌱 Managing seed URLs...")
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seed_urls = [
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{
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"url": "https://techcrunch.com",
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"label": "h2 a",
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"parser": "news",
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"priority": 8
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},
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{
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"url": "https://news.ycombinator.com",
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"label": "a.storylink",
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"parser": "news",
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"priority": 6
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},
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{
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"url": "https://reddit.com/r/technology",
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"label": "a[data-click-id='body']",
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"parser": "generic_news",
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"priority": 5
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}
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]
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for seed in seed_urls:
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await manager.add_seed_url(
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url=seed["url"],
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label=seed["label"],
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parser=seed["parser"],
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priority=seed["priority"]
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)
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print(f" ✅ Added seed: {seed['url']}")
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# Demo 4: Retrieve seeds from database
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print("\n📋 Retrieving seeds from database...")
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seeds = await manager.get_seed_urls(limit=10)
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print(f"Found {len(seeds)} active seeds:")
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for i, seed in enumerate(seeds, 1):
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print(f" {i}. {seed['url']}")
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print(f" Label: {seed['label']}")
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print(f" Parser: {seed['parser']}")
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# Demo 5: Get crawl statistics
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print("\n📈 Crawl Statistics...")
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stats = await manager.get_crawl_statistics(days=1)
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if stats:
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for metric, count in stats.items():
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print(f" {metric}: {count}")
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else:
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print(" No statistics available yet")
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print("\n🎯 Key Features Demonstrated:")
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print(" ✅ Distributed data storage with Cassandra")
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print(" ✅ Automatic URL and content deduplication")
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print(" ✅ Dynamic seed URL management from database")
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print(" ✅ Time-series data tracking and statistics")
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print(" ✅ Scalable architecture for high-volume scraping")
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print(" ✅ Content versioning and change tracking")
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print("\n🔧 Database Architecture:")
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print(" • Articles table: Main content storage with partitioning")
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print(" • URL tracker: Deduplication and processing history")
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print(" • Seeds table: Dynamic crawl target management")
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print(" • Statistics: Performance metrics and monitoring")
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print(" • History: Content versioning and change detection")
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print("\n🚀 Production Benefits:")
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print(" • High write throughput for large-scale scraping")
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print(" • Horizontal scaling across multiple nodes")
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print(" • No single point of failure with replication")
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print(" • Efficient time-series data for analytics")
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print(" • Schema flexibility for varying content structures")
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# Cleanup
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await manager.close()
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print("\n✨ Demo completed successfully!")
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except Exception as e:
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print(f"\n❌ Demo failed: {e}")
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print("\n💡 Make sure Cassandra is running:")
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print(" docker-compose -f docker-compose.cassandra.yml up -d cassandra")
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if __name__ == "__main__":
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asyncio.run(main())

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