app.py

import gradio as gr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
import json
import io
from PIL import Image

# ============================================================================
# PAK System Configuration and Constants
# ============================================================================

PAK_EFFICACY = 0.92  # 92% infestation reduction
CURRENT_IPM_EFFICACY = 0.70  # 70% average for Triple IPM
PAK_COST_PER_HA = 95  # S$ per hectare
CURRENT_IPM_COST_PER_HA = 150  # S$ per hectare
PAK_LABOR_HOURS_PER_HA = 8  # hours per hectare
CURRENT_IPM_LABOR_HOURS_PER_HA = 16  # hours per hectare
LABOR_COST_PER_HOUR = 5  # S$ per hour

BASELINE_INFESTATION_RATE = 0.41  # 41% baseline infestation
YIELD_PER_HA_BASELINE = 1200  # kg per hectare
COFFEE_PRICE_PER_KG = 2.50  # S$ per kg

# ============================================================================
# Core Calculation Functions
# ============================================================================

def calculate_infestation_rate(baseline_rate, efficacy):
    """Calculate resulting infestation rate after treatment."""
    return baseline_rate * (1 - efficacy)

def calculate_yield_impact(baseline_yield, infestation_reduction):
    """
    Calculate yield improvement based on infestation reduction.
    Assumes 2% yield loss per 1% infestation rate.
    """
    yield_loss_per_infestation = 0.02
    yield_improvement = baseline_yield * infestation_reduction * yield_loss_per_infestation
    return baseline_yield + yield_improvement

def calculate_costs(farm_size_ha, use_pak=True):
    """Calculate total costs for pest management."""
    if use_pak:
        material_cost = farm_size_ha * PAK_COST_PER_HA
        labor_cost = farm_size_ha * PAK_LABOR_HOURS_PER_HA * LABOR_COST_PER_HOUR
    else:
        material_cost = farm_size_ha * CURRENT_IPM_COST_PER_HA
        labor_cost = farm_size_ha * CURRENT_IPM_LABOR_HOURS_PER_HA * LABOR_COST_PER_HOUR
    
    total_cost = material_cost + labor_cost
    return {
        'material_cost': material_cost,
        'labor_cost': labor_cost,
        'total_cost': total_cost
    }

def calculate_roi(farm_size_ha, years=1):
    """Calculate Return on Investment for PAK System vs Current IPM."""
    
    # Current IPM scenario
    current_infestation = calculate_infestation_rate(BASELINE_INFESTATION_RATE, CURRENT_IPM_EFFICACY)
    current_yield = calculate_yield_impact(YIELD_PER_HA_BASELINE, 
                                          CURRENT_IPM_EFFICACY * BASELINE_INFESTATION_RATE)
    current_revenue = farm_size_ha * current_yield * COFFEE_PRICE_PER_KG
    current_costs = calculate_costs(farm_size_ha, use_pak=False)['total_cost']
    current_profit = current_revenue - current_costs
    
    # PAK System scenario
    pak_infestation = calculate_infestation_rate(BASELINE_INFESTATION_RATE, PAK_EFFICACY)
    pak_yield = calculate_yield_impact(YIELD_PER_HA_BASELINE, 
                                       PAK_EFFICACY * BASELINE_INFESTATION_RATE)
    pak_revenue = farm_size_ha * pak_yield * COFFEE_PRICE_PER_KG
    pak_costs = calculate_costs(farm_size_ha, use_pak=True)['total_cost']
    pak_profit = pak_revenue - pak_costs
    
    # ROI Calculation
    profit_improvement = pak_profit - current_profit
    cost_savings = current_costs - pak_costs
    roi_percentage = (profit_improvement / current_costs) * 100 if current_costs > 0 else 0
    
    return {
        'current_infestation': current_infestation,
        'pak_infestation': pak_infestation,
        'current_yield': current_yield,
        'pak_yield': pak_yield,
        'current_revenue': current_revenue,
        'pak_revenue': pak_revenue,
        'current_costs': current_costs,
        'pak_costs': pak_costs,
        'current_profit': current_profit,
        'pak_profit': pak_profit,
        'profit_improvement': profit_improvement,
        'cost_savings': cost_savings,
        'roi_percentage': roi_percentage,
        'payback_period_months': (pak_costs / (profit_improvement / 12)) if profit_improvement > 0 else float('inf')
    }

def generate_comparison_chart(farm_size_ha):
    """Generate comparison charts for PAK vs Current IPM."""
    roi_data = calculate_roi(farm_size_ha)
    
    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
    fig.suptitle(f'PAK System vs Current IPM - {farm_size_ha} hectares', fontsize=16, fontweight='bold')
    
    # Infestation Rate Comparison
    ax1 = axes[0, 0]
    methods = ['Current IPM', 'PAK System']
    infestation_rates = [roi_data['current_infestation'] * 100, roi_data['pak_infestation'] * 100]
    colors = ['#C85A3C', '#7A9E3F']
    bars1 = ax1.bar(methods, infestation_rates, color=colors, alpha=0.8, edgecolor='black', linewidth=1.5)
    ax1.set_ylabel('Infestation Rate (%)', fontweight='bold')
    ax1.set_title('Infestation Rate Comparison', fontweight='bold')
    ax1.set_ylim(0, 50)
    for bar, rate in zip(bars1, infestation_rates):
        height = bar.get_height()
        ax1.text(bar.get_x() + bar.get_width()/2., height,
                f'{rate:.1f}%', ha='center', va='bottom', fontweight='bold')
    
    # Yield Comparison
    ax2 = axes[0, 1]
    yields = [roi_data['current_yield'], roi_data['pak_yield']]
    bars2 = ax2.bar(methods, yields, color=colors, alpha=0.8, edgecolor='black', linewidth=1.5)
    ax2.set_ylabel('Yield (kg/ha)', fontweight='bold')
    ax2.set_title('Yield Comparison', fontweight='bold')
    ax2.set_ylim(0, max(yields) * 1.2)
    for bar, yield_val in zip(bars2, yields):
        height = bar.get_height()
        ax2.text(bar.get_x() + bar.get_width()/2., height,
                f'{yield_val:.0f}', ha='center', va='bottom', fontweight='bold')
    
    # Cost Comparison
    ax3 = axes[1, 0]
    costs = [roi_data['current_costs'], roi_data['pak_costs']]
    bars3 = ax3.bar(methods, costs, color=colors, alpha=0.8, edgecolor='black', linewidth=1.5)
    ax3.set_ylabel('Total Cost (S$)', fontweight='bold')
    ax3.set_title('Annual Cost Comparison', fontweight='bold')
    ax3.set_ylim(0, max(costs) * 1.2)
    for bar, cost in zip(bars3, costs):
        height = bar.get_height()
        ax3.text(bar.get_x() + bar.get_width()/2., height,
                f'S${cost:.0f}', ha='center', va='bottom', fontweight='bold')
    
    # Profit Comparison
    ax4 = axes[1, 1]
    profits = [roi_data['current_profit'], roi_data['pak_profit']]
    bars4 = ax4.bar(methods, profits, color=colors, alpha=0.8, edgecolor='black', linewidth=1.5)
    ax4.set_ylabel('Annual Profit (S$)', fontweight='bold')
    ax4.set_title('Annual Profit Comparison', fontweight='bold')
    ax4.set_ylim(0, max(profits) * 1.2)
    for bar, profit in zip(bars4, profits):
        height = bar.get_height()
        ax4.text(bar.get_x() + bar.get_width()/2., height,
                f'S${profit:.0f}', ha='center', va='bottom', fontweight='bold')
    
    plt.tight_layout()
    return fig

def generate_timeline_projection(farm_size_ha, years=5):
    """Generate 5-year financial projection."""
    years_range = np.arange(0, years + 1)
    current_profits = []
    pak_profits = []
    
    for year in years_range:
        roi = calculate_roi(farm_size_ha)
        current_profits.append(roi['current_profit'] * year)
        pak_profits.append(roi['pak_profit'] * year)
    
    fig, ax = plt.subplots(figsize=(12, 6))
    ax.plot(years_range, current_profits, marker='o', linewidth=2.5, markersize=8, 
            label='Current IPM', color='#C85A3C')
    ax.plot(years_range, pak_profits, marker='s', linewidth=2.5, markersize=8, 
            label='PAK System', color='#7A9E3F')
    
    ax.set_xlabel('Years', fontweight='bold', fontsize=12)
    ax.set_ylabel('Cumulative Profit (S$)', fontweight='bold', fontsize=12)
    ax.set_title(f'5-Year Financial Projection - {farm_size_ha} hectares', fontweight='bold', fontsize=14)
    ax.legend(fontsize=11, loc='upper left')
    ax.grid(True, alpha=0.3)
    
    # Format y-axis as currency
    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'S${x/1000:.0f}K'))
    
    plt.tight_layout()
    return fig

# ============================================================================
# Gradio Interface Functions
# ============================================================================

def analyze_farm(farm_size_ha, baseline_infestation=41):
    """Main analysis function for farm-level ROI calculation."""
    
    if farm_size_ha <= 0:
        return "Error: Farm size must be greater than 0", None, None
    
    roi_data = calculate_roi(farm_size_ha)
    
    # Generate summary report
    summary = f"""
╔════════════════════════════════════════════════════════════════╗
║         PAK SYSTEM ROI ANALYSIS - {farm_size_ha} HECTARES
╚════════════════════════════════════════════════════════════════╝

INFESTATION CONTROL
──────────────────────────────────────────────────────────────────
Current IPM Infestation Rate:        {roi_data['current_infestation']*100:.1f}%
PAK System Infestation Rate:         {roi_data['pak_infestation']*100:.1f}%
Improvement:                         {(roi_data['current_infestation'] - roi_data['pak_infestation'])*100:.1f}%

YIELD IMPACT
──────────────────────────────────────────────────────────────────
Current IPM Yield:                   {roi_data['current_yield']:.0f} kg/ha
PAK System Yield:                    {roi_data['pak_yield']:.0f} kg/ha
Additional Yield:                    {roi_data['pak_yield'] - roi_data['current_yield']:.0f} kg/ha

FINANCIAL ANALYSIS (Annual)
──────────────────────────────────────────────────────────────────
Current IPM:
  • Total Cost:                      S${roi_data['current_costs']:,.0f}
  • Revenue:                         S${roi_data['current_revenue']:,.0f}
  • Profit:                          S${roi_data['current_profit']:,.0f}

PAK System:
  • Total Cost:                      S${roi_data['pak_costs']:,.0f}
  • Revenue:                         S${roi_data['pak_revenue']:,.0f}
  • Profit:                          S${roi_data['pak_profit']:,.0f}

RETURN ON INVESTMENT
──────────────────────────────────────────────────────────────────
Annual Profit Improvement:           S${roi_data['profit_improvement']:,.0f}
Annual Cost Savings:                 S${roi_data['cost_savings']:,.0f}
ROI Percentage:                      {roi_data['roi_percentage']:.1f}%
Payback Period:                      {roi_data['payback_period_months']:.1f} months

5-YEAR PROJECTION
──────────────────────────────────────────────────────────────────
Current IPM Total Profit (5 years):  S${roi_data['current_profit']*5:,.0f}
PAK System Total Profit (5 years):   S${roi_data['pak_profit']*5:,.0f}
Additional Profit (5 years):         S${(roi_data['pak_profit'] - roi_data['current_profit'])*5:,.0f}
"""
    
    comparison_chart = generate_comparison_chart(farm_size_ha)
    timeline_chart = generate_timeline_projection(farm_size_ha, years=5)
    
    return summary, comparison_chart, timeline_chart

def network_analysis(total_farmers, avg_farm_size_ha):
    """Analyze PAK System impact across farmer network."""
    
    if total_farmers <= 0 or avg_farm_size_ha <= 0:
        return "Error: Please enter valid values", None
    
    total_hectares = total_farmers * avg_farm_size_ha
    roi_data = calculate_roi(total_hectares)
    
    summary = f"""
╔════════════════════════════════════════════════════════════════╗
║         NETWORK-LEVEL PAK SYSTEM IMPACT ANALYSIS
╚════════════════════════════════════════════════════════════════╝

NETWORK SCALE
──────────────────────────────────────────────────────────────────
Total Farmers:                       {total_farmers:,}
Average Farm Size:                   {avg_farm_size_ha} hectares
Total Network Area:                  {total_hectares:,} hectares

AGGREGATE ANNUAL IMPACT
──────────────────────────────────────────────────────────────────
Total Cost Savings:                  S${roi_data['cost_savings']:,.0f}
Total Profit Improvement:            S${roi_data['profit_improvement']:,.0f}
Total Additional Yield:              {(roi_data['pak_yield'] - roi_data['current_yield']) * total_hectares:,.0f} kg

PER-FARMER ANNUAL IMPACT
──────────────────────────────────────────────────────────────────
Cost Savings per Farmer:             S${roi_data['cost_savings']/total_farmers:,.0f}
Profit Improvement per Farmer:       S${roi_data['profit_improvement']/total_farmers:,.0f}
Additional Income per Farmer:        S${(roi_data['profit_improvement']/total_farmers):,.0f}

SUSTAINABILITY METRICS
──────────────────────────────────────────────────────────────────
Synthetic Pesticide Elimination:     100%
Labor Reduction:                     50%+
Organic Certification Compliance:    100%
Biodiversity Impact:                 Positive (no harm to beneficial insects)

5-YEAR NETWORK PROJECTION
──────────────────────────────────────────────────────────────────
Total Additional Income (5 years):   S${(roi_data['profit_improvement'] - roi_data['cost_savings'])*5:,.0f}
Cumulative Farmers Benefited:        {total_farmers:,}
Estimated Yield Increase (5 years):  {(roi_data['pak_yield'] - roi_data['current_yield']) * total_hectares * 5:,.0f} kg
"""
    
    # Create network visualization
    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
    fig.suptitle(f'Network Impact: {total_farmers:,} Farmers, {total_hectares:,} hectares', 
                 fontsize=14, fontweight='bold')
    
    # Cost savings breakdown
    ax1 = axes[0]
    categories = ['Material\nCosts', 'Labor\nCosts']
    current_material = (total_hectares * CURRENT_IPM_COST_PER_HA)
    current_labor = (total_hectares * CURRENT_IPM_LABOR_HOURS_PER_HA * LABOR_COST_PER_HOUR)
    pak_material = (total_hectares * PAK_COST_PER_HA)
    pak_labor = (total_hectares * PAK_LABOR_HOURS_PER_HA * LABOR_COST_PER_HOUR)
    
    x = np.arange(len(categories))
    width = 0.35
    
    bars1 = ax1.bar(x - width/2, [current_material, current_labor], width, 
                    label='Current IPM', color='#C85A3C', alpha=0.8, edgecolor='black')
    bars2 = ax1.bar(x + width/2, [pak_material, pak_labor], width, 
                    label='PAK System', color='#7A9E3F', alpha=0.8, edgecolor='black')
    
    ax1.set_ylabel('Annual Cost (S$)', fontweight='bold')
    ax1.set_title('Cost Breakdown Comparison', fontweight='bold')
    ax1.set_xticks(x)
    ax1.set_xticklabels(categories)
    ax1.legend()
    ax1.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'S${x/1000:.0f}K'))
    
    # Profit distribution
    ax2 = axes[1]
    profit_data = [roi_data['current_profit'], roi_data['pak_profit']]
    colors = ['#C85A3C', '#7A9E3F']
    bars = ax2.bar(['Current IPM', 'PAK System'], profit_data, color=colors, alpha=0.8, edgecolor='black')
    ax2.set_ylabel('Annual Profit (S$)', fontweight='bold')
    ax2.set_title('Network Annual Profit', fontweight='bold')
    ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'S${x/1000:.0f}K'))
    
    for bar, profit in zip(bars, profit_data):
        height = bar.get_height()
        ax2.text(bar.get_x() + bar.get_width()/2., height,
                f'S${profit/1000:.0f}K', ha='center', va='bottom', fontweight='bold')
    
    plt.tight_layout()
    
    return summary, fig

def generate_detailed_report(farm_size_ha, years=5):
    """Generate comprehensive PDF-ready report."""
    
    roi_data = calculate_roi(farm_size_ha)
    
    report = f"""
════════════════════════════════════════════════════════════════════════════════
                    PRECISION ATTRACT-AND-KILL (PAK) SYSTEM
                         DETAILED FEASIBILITY REPORT
════════════════════════════════════════════════════════════════════════════════

EXECUTIVE SUMMARY
────────────────────────────────────────────────────────────────────────────────

The Precision Attract-and-Kill System represents a transformative approach to 
Coffee Berry Borer management. This report analyzes the technical, financial, 
and operational feasibility of implementing the PAK System on a {farm_size_ha}-hectare 
farm or network.

KEY FINDINGS:
• Infestation Reduction: {roi_data['current_infestation']*100:.1f}% → {roi_data['pak_infestation']*100:.1f}% ({(roi_data['current_infestation'] - roi_data['pak_infestation'])*100:.1f}% improvement)
• Annual Cost Savings: S${roi_data['cost_savings']:,.0f}
• Annual Profit Improvement: S${roi_data['profit_improvement']:,.0f}
• ROI: {roi_data['roi_percentage']:.1f}%
• Payback Period: {roi_data['payback_period_months']:.1f} months

════════════════════════════════════════════════════════════════════════════════

TECHNICAL SPECIFICATIONS
────────────────────────────────────────────────────────────────────────────────

Active Ingredients:
• Semiochemical Lure: Optimized blend of CBB aggregation pheromone and host volatiles
• Biological Agent: Beauveria bassiana (entomopathogenic fungus)
• Formulation: Micro-encapsulated paste/gel for weather resistance

Application Methods:
• Drone/UAV: Automated spot-spraying for estates and farmer clusters
• Backpack Sprayer: Semi-automated application for individual smallholders
• Application Frequency: 2-3 times per season during peak CBB activity

Safety & Compliance:
• Organic Certification: Fully compliant (Rainforest Alliance, 4C, etc.)
• Environmental Impact: No harm to pollinators or beneficial insects
• Biodiversity: Supports regenerative agriculture practices

════════════════════════════════════════════════════════════════════════════════

FINANCIAL ANALYSIS
────────────────────────────────────────────────────────────────────────────────

YEAR 1 ANALYSIS ({farm_size_ha} hectares):

Current IPM Approach:
  Material Costs:                    S${farm_size_ha * CURRENT_IPM_COST_PER_HA:,.0f}
  Labor Costs:                       S${farm_size_ha * CURRENT_IPM_LABOR_HOURS_PER_HA * LABOR_COST_PER_HOUR:,.0f}
  Total Costs:                       S${roi_data['current_costs']:,.0f}
  Gross Revenue:                     S${roi_data['current_revenue']:,.0f}
  Net Profit:                        S${roi_data['current_profit']:,.0f}

PAK System Approach:
  Material Costs:                    S${farm_size_ha * PAK_COST_PER_HA:,.0f}
  Labor Costs:                       S${farm_size_ha * PAK_LABOR_HOURS_PER_HA * LABOR_COST_PER_HOUR:,.0f}
  Total Costs:                       S${roi_data['pak_costs']:,.0f}
  Gross Revenue:                     S${roi_data['pak_revenue']:,.0f}
  Net Profit:                        S${roi_data['pak_profit']:,.0f}

COMPARATIVE ADVANTAGE:
  Cost Savings:                      S${roi_data['cost_savings']:,.0f} ({(roi_data['cost_savings']/roi_data['current_costs'])*100:.1f}%)
  Revenue Increase:                  S${roi_data['pak_revenue'] - roi_data['current_revenue']:,.0f} ({((roi_data['pak_revenue'] - roi_data['current_revenue'])/roi_data['current_revenue'])*100:.1f}%)
  Profit Improvement:                S${roi_data['profit_improvement']:,.0f} ({(roi_data['profit_improvement']/roi_data['current_profit'])*100:.1f}%)

{years}-YEAR PROJECTION:
  Current IPM Total Profit:          S${roi_data['current_profit']*years:,.0f}
  PAK System Total Profit:           S${roi_data['pak_profit']*years:,.0f}
  Additional Profit ({years} years):       S${(roi_data['pak_profit'] - roi_data['current_profit'])*years:,.0f}

════════════════════════════════════════════════════════════════════════════════

IMPLEMENTATION ROADMAP
────────────────────────────────────────────────────────────────────────────────

PHASE 1: PROOF OF CONCEPT (2026)
Timeline: 12 months
Investment: S$6,500 (for 5-10 hectares)
Deliverables:
  • Field trial validation in Vietnam
  • Efficacy data (target: 90%+ infestation reduction)
  • Cost-benefit analysis
  • Farmer feedback and adoption metrics

PHASE 2: EXPANSION & STANDARDIZATION (2027)
Timeline: 12 months
Investment: S$12,500-15,000 (for Indonesia & PNG)
Deliverables:
  • Formulation adaptation for regional conditions
  • Expanded field trials (50+ farmers)
  • Standardized protocols and training materials
  • Regional scale-up plan

PHASE 3: COMMERCIALIZATION (2028+)
Timeline: Ongoing
Model: IPM-as-a-Service
  • ECOM or partner provides formulation and application
  • Fixed post-harvest fee (S$5-10/bag)
  • Rapid adoption across farmer network

════════════════════════════════════════════════════════════════════════════════

RISK ASSESSMENT & MITIGATION
────────────────────────────────────────────────────────────────────────────────

Risk: Formulation efficacy below target
Probability: Medium | Impact: High
Mitigation: Preliminary lab testing; contingency budget for adjustments

Risk: Weather impact on field trials
Probability: Medium | Impact: Medium
Mitigation: Multiple demo plot locations; extended trial period if needed

Risk: Farmer adoption challenges
Probability: Medium | Impact: Medium
Mitigation: Early engagement; clear training; peer learning networks

Risk: Regulatory or certification issues
Probability: Low | Impact: High
Mitigation: Early engagement with certification bodies; full documentation

════════════════════════════════════════════════════════════════════════════════

SUSTAINABILITY & LIVELIHOOD IMPACT
────────────────────────────────────────────────────────────────────────────────

Environmental Benefits:
• Elimination of synthetic pesticides
• Support for soil health and biodiversity
• Reduced carbon intensity of coffee production
• Climate-smart agriculture alignment

Livelihood Benefits:
• Reduced labor burden (50% reduction)
• Improved income stability
• Enhanced quality of life for farming communities
• Scalable across ECOM's global network

════════════════════════════════════════════════════════════════════════════════

CONCLUSION
────────────────────────────────────────────────────────────────────────────────

The Precision Attract-and-Kill System represents a transformative opportunity 
for ECOM to address the Coffee Berry Borer challenge with a single, nature-based, 
and highly scalable solution. The financial analysis demonstrates clear ROI, 
with payback periods under 12 months and substantial long-term profit improvements.

The modest Phase 1 investment of S$6,500 is justified by the potential impact 
across ECOM's network of 6,000+ smallholder farmers, with estimated total 
additional income exceeding S$300,000 annually once fully scaled.

We recommend proceeding with Phase 1 validation in Vietnam in 2026, with clear 
success metrics and a defined pathway to Phase 2 expansion and commercialization.

════════════════════════════════════════════════════════════════════════════════
Report Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
════════════════════════════════════════════════════════════════════════════════
"""
    
    return report

# ============================================================================
# Gradio Interface Setup
# ============================================================================

def create_interface():
    """Create the Gradio interface for the PAK System POC platform."""
    
    with gr.Blocks(title="PAK System POC Platform", theme=gr.themes.Soft()) as demo:
        
        gr.Markdown("""
        # 🌱 Precision Attract-and-Kill (PAK) System
        ## Interactive POC Platform for Coffee Berry Borer Management
        
        Welcome to the PAK System Proof of Concept platform. Use the tools below to analyze 
        the financial and operational feasibility of implementing the PAK System on your farm 
        or across your farmer network.
        """)
        
        with gr.Tabs():
            
            # ================================================================
            # TAB 1: FARM-LEVEL ANALYSIS
            # ================================================================
            with gr.TabItem("🚜 Farm Analysis"):
                gr.Markdown("""
                ### Individual Farm ROI Calculator
                
                Enter your farm size to see detailed financial projections comparing 
                the PAK System with current IPM approaches.
                """)
                
                with gr.Row():
                    with gr.Column():
                        farm_size = gr.Slider(
                            label="Farm Size (hectares)",
                            minimum=0.5,
                            maximum=50,
                            value=2,
                            step=0.5
                        )
                        analyze_btn = gr.Button("Analyze Farm", variant="primary", size="lg")
                    
                    with gr.Column():
                        gr.Markdown("**Quick Reference**\n\n"
                                   "• Smallholder: 0.5-2 ha\n"
                                   "• Medium Farm: 2-10 ha\n"
                                   "• Estate: 10-50+ ha")
                
                with gr.Row():
                    summary_output = gr.Textbox(
                        label="Analysis Summary",
                        lines=25,
                        interactive=False,
                        max_lines=50
                    )
                
                with gr.Row():
                    with gr.Column():
                        chart1 = gr.Plot(label="Comparison Charts")
                    with gr.Column():
                        chart2 = gr.Plot(label="5-Year Projection")
                
                analyze_btn.click(
                    fn=analyze_farm,
                    inputs=[farm_size],
                    outputs=[summary_output, chart1, chart2]
                )
            
            # ================================================================
            # TAB 2: NETWORK ANALYSIS
            # ================================================================
            with gr.TabItem("🌍 Network Analysis"):
                gr.Markdown("""
                ### Farmer Network Impact Calculator
                
                Analyze the aggregate impact of implementing the PAK System 
                across a farmer network or sourcing region.
                """)
                
                with gr.Row():
                    with gr.Column():
                        total_farmers = gr.Slider(
                            label="Total Farmers",
                            minimum=10,
                            maximum=10000,
                            value=100,
                            step=10
                        )
                    with gr.Column():
                        avg_farm_size = gr.Slider(
                            label="Average Farm Size (hectares)",
                            minimum=0.5,
                            maximum=10,
                            value=1.5,
                            step=0.5
                        )
                
                analyze_network_btn = gr.Button("Analyze Network", variant="primary", size="lg")
                
                with gr.Row():
                    network_summary = gr.Textbox(
                        label="Network Analysis",
                        lines=25,
                        interactive=False,
                        max_lines=50
                    )
                
                with gr.Row():
                    network_chart = gr.Plot(label="Network Impact Visualization")
                
                analyze_network_btn.click(
                    fn=network_analysis,
                    inputs=[total_farmers, avg_farm_size],
                    outputs=[network_summary, network_chart]
                )
            
            # ================================================================
            # TAB 3: DETAILED REPORT
            # ================================================================
            with gr.TabItem("📊 Detailed Report"):
                gr.Markdown("""
                ### Comprehensive Feasibility Report
                
                Generate a detailed, publication-ready report for stakeholder 
                presentations and decision-making.
                """)
                
                with gr.Row():
                    with gr.Column():
                        report_farm_size = gr.Slider(
                            label="Farm Size (hectares)",
                            minimum=0.5,
                            maximum=100,
                            value=5,
                            step=0.5
                        )
                    with gr.Column():
                        report_years = gr.Slider(
                            label="Projection Period (years)",
                            minimum=1,
                            maximum=10,
                            value=5,
                            step=1
                        )
                
                generate_report_btn = gr.Button("Generate Report", variant="primary", size="lg")
                
                report_output = gr.Textbox(
                    label="Detailed Report",
                    lines=40,
                    interactive=False,
                    max_lines=100
                )
                
                generate_report_btn.click(
                    fn=generate_detailed_report,
                    inputs=[report_farm_size, report_years],
                    outputs=report_output
                )
            
            # ================================================================
            # TAB 4: SYSTEM INFORMATION
            # ================================================================
            with gr.TabItem("ℹ️ System Information"):
                gr.Markdown("""
                ### PAK System Technical Specifications
                
                #### Active Ingredients
                - **Semiochemical Lure**: Optimized blend of CBB aggregation pheromone and host volatiles
                - **Biological Agent**: Beauveria bassiana (entomopathogenic fungus)
                - **Formulation**: Micro-encapsulated paste/gel for weather resistance
                
                #### Application Methods
                - **Drone/UAV**: Automated spot-spraying for estates and farmer clusters
                - **Backpack Sprayer**: Semi-automated application for individual smallholders
                - **Frequency**: 2-3 applications per season during peak CBB activity
                
                #### Performance Metrics
                - **Infestation Reduction**: 90%+ (vs. 50-90% for current IPM)
                - **Cost Reduction**: 33% (from S$150/ha to <S$100/ha)
                - **Labor Reduction**: 50%+ (from 16 to 8 hours/ha)
                - **Standardization**: High consistency across farm types
                
                #### Safety & Compliance
                - ✅ Organic Certification: Fully compliant
                - ✅ Environmental Impact: No harm to beneficial insects
                - ✅ Biodiversity: Supports regenerative agriculture
                - ✅ Regulatory: Aligns with international standards
                
                #### Implementation Timeline
                - **Phase 1 (2026)**: Proof of Concept in Vietnam (S$6,500)
                - **Phase 2 (2027)**: Expansion to Indonesia & PNG (S$12,500-15,000)
                - **Phase 3 (2028+)**: Commercial rollout via IPM-as-a-Service model
                
                #### Business Model
                - **Service Delivery**: ECOM or partner provides formulation and application
                - **Pricing**: Fixed post-harvest fee (S$5-10/bag)
                - **Farmer Benefit**: Clear ROI with payback period <12 months
                - **Scalability**: Applicable across ECOM's global network
                """)
    
    return demo

# ============================================================================
# Main Execution
# ============================================================================

if __name__ == "__main__":
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True
    )