demo.py

#!/usr/bin/env python3
"""
Demo script for the Agricultural Analysis Tool
Showcases the main features and functionality of the MCP server and analysis tools.
"""

import warnings
warnings.filterwarnings('ignore')

from data_loader import AgriculturalDataLoader
from analysis_tools import AgriculturalAnalyzer
import pandas as pd

def main():
    """Run the demo of agricultural analysis features."""
    
    print("🚜" + "="*60)
    print("    AGRICULTURAL ANALYSIS TOOL - DEMO")
    print("    Station Expérimentale de Kerguéhennec")
    print("="*63)
    print()
    
    # Initialize components
    print("🔧 Initializing components...")
    data_loader = AgriculturalDataLoader()
    analyzer = AgriculturalAnalyzer(data_loader)
    print("✅ Components initialized successfully")
    print()
    
    # Load data
    print("📊 Loading agricultural intervention data...")
    df = data_loader.load_all_files()
    print(f"✅ Loaded {len(df):,} intervention records")
    print(f"📅 Data spans {df.year.nunique()} years: {sorted(df.year.unique())}")
    print(f"🌱 Covers {df.crop_type.nunique()} different crop types")
    print(f"📍 Across {df.plot_name.nunique()} different plots")
    print(f"💊 Including {df.is_herbicide.sum():,} herbicide applications")
    print()
    
    # Show top crops and plots
    print("🌾 TOP CROPS ANALYZED:")
    top_crops = df.crop_type.value_counts().head(10)
    for i, (crop, count) in enumerate(top_crops.items(), 1):
        print(f"   {i:2}. {crop:<30} ({count:3} interventions)")
    print()
    
    print("📍 TOP PLOTS ANALYZED:")
    top_plots = df.plot_name.value_counts().head(10)
    for i, (plot, count) in enumerate(top_plots.items(), 1):
        print(f"   {i:2}. {plot:<30} ({count:3} interventions)")
    print()
    
    # Analyze weed pressure
    print("🌿 WEED PRESSURE ANALYSIS (IFT - Treatment Frequency Index)")
    print("-" * 60)
    trends = analyzer.analyze_weed_pressure_trends()
    summary = trends['summary']
    
    print(f"📈 Overall IFT Statistics:")
    print(f"   • Mean IFT:           {summary['mean_ift']:.2f}")
    print(f"   • Standard deviation: {summary['std_ift']:.2f}")
    print(f"   • Minimum IFT:        {summary['min_ift']:.2f}")
    print(f"   • Maximum IFT:        {summary['max_ift']:.2f}")
    print()
    
    # Show IFT trends by year
    if 'yearly_ift' in trends:
        yearly_data = pd.DataFrame(trends['yearly_ift'])
        print("📊 IFT Evolution by Year:")
        for _, row in yearly_data.iterrows():
            year = int(row['year'])
            ift = row['ift_herbicide']
            risk_indicator = "🟢" if ift < 1.0 else "🟡" if ift < 2.0 else "🔴"
            print(f"   {year}: {ift:.2f} {risk_indicator}")
        print()
    
    # Prediction demo
    print("🔮 WEED PRESSURE PREDICTIONS (2025-2027)")
    print("-" * 60)
    try:
        predictions = analyzer.predict_weed_pressure(target_years=[2025, 2026, 2027])
        model_perf = predictions['model_performance']
        print(f"🤖 Model Performance:")
        print(f"   • R² Score: {model_perf['r2']:.3f}")
        print(f"   • Mean Squared Error: {model_perf['mse']:.3f}")
        print()
        
        # Show predictions for each year
        for year in [2025, 2026, 2027]:
            if year in predictions['predictions']:
                year_pred = predictions['predictions'][year]
                print(f"📅 Predictions for {year}:")
                
                # Group by risk level
                risk_counts = year_pred['risk_level'].value_counts()
                for risk_level in ['low', 'medium', 'high']:
                    count = risk_counts.get(risk_level, 0)
                    emoji = {"low": "🟢", "medium": "🟡", "high": "🔴"}[risk_level]
                    print(f"   {emoji} {risk_level.capitalize()} risk: {count} plots")
                
                # Show a few examples
                low_risk = year_pred[year_pred['risk_level'] == 'low']
                if len(low_risk) > 0:
                    print(f"   🌱 Best plots for sensitive crops:")
                    for _, row in low_risk.head(5).iterrows():
                        print(f"      • {row['plot_name']}: IFT {row['predicted_ift']:.2f}")
                print()
        
    except Exception as e:
        print(f"❌ Prediction error: {e}")
        print()
    
    # Suitable plots for sensitive crops
    print("🎯 PLOTS SUITABLE FOR SENSITIVE CROPS (peas, beans)")
    print("-" * 60)
    try:
        suitable_plots = analyzer.identify_suitable_plots_for_sensitive_crops(
            target_years=[2025, 2026, 2027],
            max_ift_threshold=1.0
        )
        
        for year, plots in suitable_plots.items():
            print(f"📅 {year}: {len(plots)} suitable plots")
            if plots:
                for plot in plots[:5]:  # Show first 5
                    print(f"   ✅ {plot}")
                if len(plots) > 5:
                    print(f"   ... and {len(plots) - 5} more")
            else:
                print("   ❌ No plots meet the criteria")
            print()
    except Exception as e:
        print(f"❌ Analysis error: {e}")
        print()
    
    # Crop rotation analysis
    print("🔄 CROP ROTATION IMPACT ANALYSIS")
    print("-" * 60)
    try:
        rotation_impact = analyzer.analyze_crop_rotation_impact()
        if not rotation_impact.empty:
            print("🏆 Best rotations (lowest average IFT):")
            best_rotations = rotation_impact.head(10)
            for i, (_, row) in enumerate(best_rotations.iterrows(), 1):
                print(f"   {i:2}. {row['rotation_type']:<40} IFT: {row['mean_ift']:.2f}")
            print()
            
            print("⚠️  Worst rotations (highest average IFT):")
            worst_rotations = rotation_impact.tail(5)
            for i, (_, row) in enumerate(worst_rotations.iterrows(), 1):
                print(f"   {i:2}. {row['rotation_type']:<40} IFT: {row['mean_ift']:.2f}")
        else:
            print("❌ Insufficient data for rotation analysis")
        print()
    except Exception as e:
        print(f"❌ Rotation analysis error: {e}")
        print()
    
    # Herbicide usage analysis
    print("💊 HERBICIDE USAGE ANALYSIS")
    print("-" * 60)
    try:
        herbicide_analysis = analyzer.analyze_herbicide_alternatives()
        print("📈 Most frequently used herbicides:")
        top_herbicides = herbicide_analysis.head(10)
        for i, (_, row) in enumerate(top_herbicides.iterrows(), 1):
            crop_info = f" ({row['crop_type']})" if pd.notna(row['crop_type']) else ""
            print(f"   {i:2}. {row['produit']:<30}{crop_info}")
            print(f"       Applications: {row['applications']:<3} | Total qty: {row['total_quantity']:.1f}")
        print()
    except Exception as e:
        print(f"❌ Herbicide analysis error: {e}")
        print()
    
    # Summary and recommendations
    print("📋 SUMMARY AND RECOMMENDATIONS")
    print("="*60)
    print("✅ ACHIEVEMENTS:")
    print("   • Successfully loaded and analyzed 10 years of intervention data")
    print("   • Calculated weed pressure trends using IFT methodology")
    print("   • Developed predictive model for future weed pressure")
    print("   • Identified suitable plots for sensitive crops")
    print("   • Analyzed impact of crop rotations")
    print()
    
    print("🎯 KEY INSIGHTS:")
    avg_ift = summary['mean_ift']
    if avg_ift < 1.0:
        print("   • Overall weed pressure is LOW - good for sensitive crops")
    elif avg_ift < 2.0:
        print("   • Overall weed pressure is MODERATE - requires monitoring")
    else:
        print("   • Overall weed pressure is HIGH - needs intervention")
    
    print(f"   • Current average IFT: {avg_ift:.2f}")
    print(f"   • {df.plot_name.nunique()} plots available for analysis")
    print(f"   • {df.crop_type.nunique()} different crop types in rotation")
    print()
    
    print("🚀 NEXT STEPS:")
    print("   • Use the Gradio interface for interactive analysis")
    print("   • Deploy on Hugging Face Spaces for broader access")
    print("   • Configure MCP server for LLM integration")
    print("   • Upload dataset to Hugging Face Hub")
    print()
    
    print("🌐 ACCESS THE TOOL:")
    print("   • Gradio Interface: python gradio_app.py")
    print("   • MCP Server: python mcp_server.py")
    print("   • HF Deployment: python app.py")
    print()
    
    print("🚜" + "="*60)
    print("    DEMO COMPLETED SUCCESSFULLY!")
    print("="*63)

if __name__ == "__main__":
    main()