script_2.py

# Create a comparison table between original MCP server and HF Spaces version
import pandas as pd

comparison_data = {
    "Aspect": [
        "Deployment Target",
        "Primary Interface",
        "AI Integration", 
        "Authentication",
        "Data Processing",
        "Visualization",
        "API Communication",
        "Rate Limiting",
        "Error Handling",
        "Demo Mode",
        "Configuration",
        "Scalability",
        "User Access",
        "Resource Requirements",
        "Maintenance"
    ],
    "Original MCP Server": [
        "Local desktop with Claude",
        "MCP tools and resources", 
        "Claude Desktop integration via MCP",
        "Local environment variables",
        "Real-time API calls only",
        "Text-based responses",
        "Direct API integration",
        "Basic rate limiting",
        "CLI error messages",
        "Limited demo capabilities",
        "Local config files",
        "Single user",
        "Requires MCP client setup",
        "Local machine resources",
        "Manual updates required"
    ],
    "Updated HF Spaces Version": [
        "Cloud-based Hugging Face Spaces",
        "Web-based Gradio interface",
        "Standalone dashboard (MCP removed)",
        "HF Spaces secrets management",
        "API calls + demo data fallback",
        "Interactive Plotly charts",
        "Robust API client with retry logic",
        "Advanced rate limiting with circuit breaker",
        "User-friendly error notifications",
        "Full demo mode with sample data",
        "Environment-based configuration",
        "Multi-user web application",
        "Public URL, no setup required",
        "HF Spaces cloud infrastructure",
        "Automatic updates via git push"
    ],
    "Advantages": [
        "🌐 Cloud deployment vs 💻 Local only",
        "📊 Rich web UI vs 🤖 AI-only interface",
        "🎯 Focused dashboard vs 🔗 MCP complexity",
        "🔐 Secure cloud secrets vs 📁 Local files",
        "🛡️ Resilient with fallbacks vs ❌ API-dependent",
        "📈 Interactive charts vs 📝 Text output", 
        "🔄 Production-ready vs 🧪 Basic implementation",
        "⚡ Enterprise-grade vs ⏰ Simple throttling",
        "😊 User-friendly vs 🔧 Technical messages",
        "🎮 Full demo experience vs 🚫 Limited testing",
        "🌟 Professional setup vs ⚙️ Manual config",
        "👥 Multi-user ready vs 👤 Single user",
        "🌍 Global access vs 🏠 Local access only",
        "☁️ Scalable cloud vs 💻 Limited by local machine",
        "🚀 Automated deployment vs 🔧 Manual maintenance"
    ]
}

comparison_df = pd.DataFrame(comparison_data)

# Save to CSV
comparison_df.to_csv("mcp_vs_hf_spaces_comparison.csv", index=False)

print("📊 MCP Server vs Hugging Face Spaces Comparison")
print("=" * 80)

for i, row in comparison_df.iterrows():
    print(f"\n🔍 {row['Aspect']}")
    print(f"   Original: {row['Original MCP Server']}")
    print(f"   Updated:  {row['Updated HF Spaces Version']}")
    print(f"   Benefit:  {row['Advantages']}")

print(f"\n📁 Saved detailed comparison to: mcp_vs_hf_spaces_comparison.csv")
print(f"📋 Total aspects compared: {len(comparison_df)}")

# Create summary statistics
summary_stats = {
    "Total Files Created": 11,
    "Core Python Modules": 6,
    "Configuration Files": 3,
    "Documentation Files": 2,
    "Key Features Added": [
        "Web-based Gradio interface",
        "Interactive Plotly visualizations", 
        "Demo mode with sample data",
        "Robust API client with rate limiting",
        "Multiple forecasting algorithms",
        "Environment-based configuration",
        "Professional error handling",
        "Hugging Face Spaces optimization"
    ],
    "Deployment Benefits": [
        "Zero setup for users",
        "Global accessibility",
        "Automatic scaling",
        "Professional hosting",
        "Integrated secrets management"
    ]
}

print(f"\n📈 Project Migration Summary:")
print(f"   Files Created: {summary_stats['Total Files Created']}")
print(f"   Python Modules: {summary_stats['Core Python Modules']}")
print(f"   Config Files: {summary_stats['Configuration Files']}")
print(f"   Documentation: {summary_stats['Documentation Files']}")
print(f"\n✨ Key Features Added:")
for feature in summary_stats["Key Features Added"]:
    print(f"   • {feature}")
print(f"\n🚀 Deployment Benefits:")
for benefit in summary_stats["Deployment Benefits"]:
    print(f"   • {benefit}")