Update app.py

app.py

@@ -1,7 +1,723 @@
 import gradio as gr
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime, timedelta
+import json
 
-def greet(name):
-    return "Hello " + name + "!!"
+# ============================================================================
+# PAK System Configuration and Constants
+# ============================================================================
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+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['total_cost'] - pak_costs['total_cost']
+    roi_percentage = (profit_improvement / current_costs['total_cost']) * 100
+    
+    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['total_cost'],
+        'pak_costs': pak_costs['total_cost'],
+        '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['total_cost'] / (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${roi_data['pak_profit']*years:,.0f}
+      Additional Profit (5 years):       S${(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
+    )