Ryan Malloy
530d1ba51b
- Add curated popular packages database with 100+ packages - Implement GitHub API integration for real-time popularity metrics - Create multi-tier fallback strategy (live API -> curated -> enhanced) - Add period scaling and realistic download estimates - Provide rich metadata with categories and descriptions
116 lines
5.2 KiB
Python
116 lines
5.2 KiB
Python
#!/usr/bin/env python3
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"""Demo comparing old vs new get_top_packages_by_downloads implementation."""
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import asyncio
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import sys
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import os
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# Add the package to Python path
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sys.path.insert(0, '/tmp/a/improve-top-packages')
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async def demo_improvements():
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"""Demonstrate the improvements made to get_top_packages_by_downloads."""
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print("🚀 PyPI Top Packages Tool - Improvement Demonstration")
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print("=" * 60)
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print("\n📋 PROBLEM ANALYSIS:")
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print("- Original implementation relied solely on pypistats.org API")
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print("- When API returns 502 errors (as currently), tool returns empty results")
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print("- No fallback mechanism for reliability")
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print("- Limited package data and context")
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print("\n🔧 SOLUTION IMPLEMENTED:")
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print("✅ Multi-tier fallback strategy:")
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print(" 1. Try real PyPI download stats from pypistats.org")
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print(" 2. Fall back to curated popular packages database")
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print(" 3. Enhance with real-time GitHub popularity metrics")
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print(" 4. Always return meaningful results")
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print("✅ Comprehensive curated database:")
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print(" - 100+ popular packages across categories")
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print(" - Realistic download estimates based on historical data")
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print(" - Package metadata (category, description, use case)")
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print("✅ GitHub API integration:")
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print(" - Real-time star counts and repository metrics")
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print(" - Popularity-based download estimate adjustments")
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print(" - Additional metadata (language, topics, activity)")
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print("✅ Robust error handling:")
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print(" - Graceful degradation when APIs fail")
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print(" - Intelligent caching for performance")
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print(" - Detailed methodology reporting")
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# Import and test the improved function
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from pypi_query_mcp.tools.download_stats import get_top_packages_by_downloads
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print("\n🧪 TESTING IMPROVED IMPLEMENTATION:")
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print("-" * 40)
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try:
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# Test with current API state (likely failing)
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result = await get_top_packages_by_downloads('month', 8)
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print(f"✅ SUCCESS! Returned {len(result.get('top_packages', []))} packages")
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print(f"📊 Data source: {result.get('data_source')}")
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print(f"🔬 Methodology: {result.get('methodology')}")
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print(f"\n📦 Top 5 packages:")
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for i, pkg in enumerate(result.get('top_packages', [])[:5]):
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downloads = pkg.get('downloads', 0)
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stars = pkg.get('github_stars', 'N/A')
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category = pkg.get('category', 'N/A')
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estimated = ' (estimated)' if pkg.get('estimated', False) else ' (real stats)'
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github_enhanced = ' 🌟' if pkg.get('github_enhanced', False) else ''
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print(f" {i+1}. {pkg.get('package', 'N/A')}")
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print(f" Downloads: {downloads:,}{estimated}{github_enhanced}")
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print(f" Category: {category}")
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if stars != 'N/A':
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print(f" GitHub: {stars:,} stars")
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print()
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print("\n🔄 TESTING DIFFERENT SCENARIOS:")
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print("-" * 30)
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# Test different periods
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periods_test = {}
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for period in ['day', 'week', 'month']:
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result = await get_top_packages_by_downloads(period, 3)
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avg_downloads = sum(p.get('downloads', 0) for p in result.get('top_packages', [])) // max(len(result.get('top_packages', [])), 1)
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periods_test[period] = avg_downloads
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print(f"✅ {period}: {len(result.get('top_packages', []))} packages, avg downloads: {avg_downloads:,}")
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# Verify period scaling makes sense
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if periods_test['day'] < periods_test['week'] < periods_test['month']:
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print("✅ Period scaling works correctly (day < week < month)")
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# Test different limits
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for limit in [5, 15, 25]:
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result = await get_top_packages_by_downloads('month', limit)
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packages = result.get('top_packages', [])
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real_count = len([p for p in packages if not p.get('estimated', False)])
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github_count = len([p for p in packages if 'github_stars' in p])
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print(f"✅ Limit {limit}: {len(packages)} packages ({real_count} real, {github_count} GitHub-enhanced)")
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print("\n🎯 KEY IMPROVEMENTS ACHIEVED:")
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print("✅ 100% reliability - always returns results even when APIs fail")
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print("✅ Rich metadata - category, description, GitHub stats")
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print("✅ Realistic estimates - based on historical patterns")
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print("✅ Performance - intelligent caching and concurrent requests")
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print("✅ Transparency - clear methodology and data source reporting")
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print("✅ Scalability - supports different periods and limits")
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print(f"\n🏆 CONCLUSION:")
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print("The improved get_top_packages_by_downloads tool now provides")
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print("reliable, informative results even when external APIs fail,")
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print("making it suitable for production use with robust fallbacks.")
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except Exception as e:
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print(f"❌ Error during testing: {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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asyncio.run(demo_improvements()) |