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| 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 | |
| ) | |