Update app.py

app.py

@@ -1,755 +1,755 @@
-"""
-GreenPath - AI & Data Analytics Platform for Reducing Shipment CO₂ Emissions
-Professional Streamlit Frontend with Eco-Friendly Design
-"""
-
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import plotly.graph_objects as go
-from plotly.subplots import make_subplots
-import requests
-import sys
-import os
-from datetime import datetime, timedelta
-import numpy as np
-
-# Add src directory to path
-sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
-
-from emissions.emission_calculator import EmissionCalculator, TransportMode
-from route_optimizer.green_route_optimizer import GreenRouteOptimizer
-
-# Page configuration
-st.set_page_config(
-    page_title="GreenPath - CO₂ Emission Tracker",
-    page_icon="🌱",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Custom CSS for eco-friendly theme
-st.markdown("""
-<style>
-    
-    .main-header {
-        background: linear-gradient(90deg, #2ECC71 0%, #27AE60 100%);
-        padding: 2rem;
-        border-radius: 10px;
-        color: white;
-        text-align: center;
-        margin-bottom: 2rem;
-        position: relative;
-        z-index: 1;
-    }
-    
-    .metric-card {
-        background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%);
-        padding: 1.5rem;
-        border-radius: 10px;
-        box-shadow: 0 2px 10px rgba(0,0,0,0.1);
-        border-left: 4px solid #2ECC71;
-        margin-bottom: 1rem;
-    }
-    
-    .green-button {
-        background-color: #2ECC71;
-        color: white;
-        border: none;
-        padding: 0.5rem 1rem;
-        border-radius: 5px;
-        cursor: pointer;
-    }
-    
-    .sidebar .sidebar-content {
-        background-color: #F8F9FA;
-    }
-    
-    .stSelectbox > div > div {
-        background-color: white !important;
-        border: 1px solid #E0E0E0 !important;
-        border-radius: 8px !important;
-        color: #34495E !important;
-    }
-    
-    .stSelectbox > div > div > div {
-        color: #34495E !important;
-        font-weight: 500 !important;
-    }
-    
-    .eco-badge {
-        background-color: #2ECC71;
-        color: white;
-        padding: 0.2rem 0.5rem;
-        border-radius: 15px;
-        font-size: 0.8rem;
-        font-weight: bold;
-    }
-    
-    
-    /* Navigation styling */
-    .stRadio > div {
-        background-color: transparent;
-        padding: 0.5rem;
-        border-radius: 8px;
-    }
-    
-    .stRadio > div > label {
-        background-color: #FFFFFF;
-        border: 1px solid #E0E0E0;
-        border-radius: 8px;
-        margin: 4px 0;
-        padding: 8px 12px;
-        cursor: pointer;
-        transition: all 0.3s ease;
-        display: block;
-    }
-    
-    .stRadio > div > label:hover {
-        background-color: #E8F5E8;
-        border-color: #2ECC71;
-        color: #2ECC71;
-    }
-    
-    .stRadio > div > label > div {
-        color: #34495E !important;
-        font-size: 14px !important;
-        font-weight: 500;
-        margin: 0;
-    }
-    
-    .stRadio > div > label[data-checked="true"] {
-        background-color: #2ECC71;
-        border-color: #2ECC71;
-        color: white;
-    }
-    
-    .stRadio > div > label[data-checked="true"] > div {
-        color: white !important;
-    }
-    
-    /* Emoji fix */
-    .stRadio label {
-        font-family: "Apple Color Emoji", "Segoe UI Emoji", "Noto Color Emoji", sans-serif;
-    }
-    
-    /* Sidebar styling */
-    .css-1d391kg {
-        background-color: #F8F9FA;
-    }
-    
-    /* Metric styling */
-    [data-testid="metric-container"] {
-        background-color: white;
-        border: 1px solid #E0E0E0;
-        padding: 1rem;
-        border-radius: 8px;
-        border-left: 4px solid #2ECC71;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Initialize components
-@st.cache_resource
-def init_components():
-    calculator = EmissionCalculator()
-    optimizer = GreenRouteOptimizer()
-    return calculator, optimizer
-
-calculator, optimizer = init_components()
-
-# Header
-st.markdown("""
-<div class="main-header">
-    <h1>🌱 GreenPath</h1>
-    <h3>AI-Powered Platform for Reducing Shipment CO₂ Emissions</h3>
-    <p>Designed by Sayed Mohd Zayeem Khateeb</p>
-</div>
-""", unsafe_allow_html=True)
-
-# Sidebar
-with st.sidebar:
-    # Custom GreenPath logo
-    st.markdown("""
-    <div style="text-align: center; padding: 1rem; background: linear-gradient(135deg, #2ECC71, #27AE60); border-radius: 10px; margin-bottom: 1rem;">
-        <h2 style="color: white; margin: 0; font-size: 24px;">🌱 GreenPath</h2>
-        <p style="color: #E8F8F5; margin: 0; font-size: 12px;">AI Emission Tracker</p>
-    </div>
-    """, unsafe_allow_html=True)
-    
-    st.markdown("### 📍 Navigate")
-    
-    # Navigation with better visibility
-    nav_options = {
-        "🏠 Dashboard": "Dashboard",
-        "🧮 Emission Calculator": "Emission Calculator", 
-        "🗺️ Route Optimizer": "Route Optimizer",
-        "📊 Scenario Analysis": "Scenario Analysis",
-        "📈 Analytics": "Analytics"
-    }
-    
-    page = st.selectbox(
-        "Choose a page:",
-        options=list(nav_options.keys()),
-        label_visibility="collapsed",
-        key="navigation"
-    )
-    
-    st.markdown("---")
-    st.markdown("### 🌍 Quick Stats")
-    
-    # Sample KPIs
-    col1, col2 = st.columns(2)
-    with col1:
-        st.metric("CO₂ Saved", "2.4t", "↓ 22%")
-    with col2:
-        st.metric("Routes Optimized", "156", "↑ 15%")
-
-# Main content based on selected page
-if page == "🏠 Dashboard":
-    st.markdown("## 📊 Emission Overview Dashboard")
-    
-    # Top KPIs
-    col1, col2, col3, col4 = st.columns(4)
-    
-    with col1:
-        st.markdown("""
-        <div class="metric-card">
-            <h3 style="color: #2ECC71; margin: 0;">12.5t</h3>
-            <p style="margin: 0; color: #7F8C8D;">Total CO₂ Emissions</p>
-            <small style="color: #E74C3C;">↓ 18% vs last month</small>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown("""
-        <div class="metric-card">
-            <h3 style="color: #2ECC71; margin: 0;">22%</h3>
-            <p style="margin: 0; color: #7F8C8D;">Emission Reduction</p>
-            <small style="color: #27AE60;">Green routes adopted</small>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown("""
-        <div class="metric-card">
-            <h3 style="color: #2ECC71; margin: 0;">0.08</h3>
-            <p style="margin: 0; color: #7F8C8D;">Avg. Emission/Shipment (kg)</p>
-            <small style="color: #27AE60;">Industry best practice</small>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col4:
-        st.markdown("""
-        <div class="metric-card">
-            <h3 style="color: #2ECC71; margin: 0;">$1,250</h3>
-            <p style="margin: 0; color: #7F8C8D;">Carbon Tax Savings</p>
-            <small style="color: #27AE60;">Monthly estimate</small>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # Charts
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.markdown("### 🚛 Emissions by Transport Mode")
-        
-        # Sample data for transport mode comparison
-        modes_data = pd.DataFrame({
-            'Transport Mode': ['Road Truck', 'Rail', 'Ship Container', 'Air Cargo'],
-            'CO₂ Emissions (kg)': [62, 22, 11, 602],
-            'Usage %': [45, 30, 20, 5]
-        })
-        
-        fig = px.bar(
-            modes_data, 
-            x='Transport Mode', 
-            y='CO₂ Emissions (kg)',
-            color='CO₂ Emissions (kg)',
-            color_continuous_scale=['#2ECC71', '#E74C3C'],
-            title="Emission Factors by Transport Mode"
-        )
-        fig.update_layout(showlegend=False, height=400)
-        st.plotly_chart(fig, use_container_width=True)
-    
-    with col2:
-        st.markdown("### 📈 Emission Trends")
-        
-        # Sample trend data
-        emissions_data = [15.2, 14.8, 13.9, 13.1, 12.8, 12.3, 11.9, 11.5, 12.5]
-        dates = pd.date_range(start='2024-01-01', periods=len(emissions_data), freq='M')
-        trend_data = pd.DataFrame({
-            'Date': dates,
-            'Emissions (tonnes)': emissions_data,
-            'Target': [14.0] * len(emissions_data)
-        })
-        
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(
-            x=trend_data['Date'], 
-            y=trend_data['Emissions (tonnes)'],
-            mode='lines+markers',
-            name='Actual Emissions',
-            line=dict(color='#2ECC71', width=3)
-        ))
-        fig.add_trace(go.Scatter(
-            x=trend_data['Date'], 
-            y=trend_data['Target'],
-            mode='lines',
-            name='Target',
-            line=dict(color='#E74C3C', dash='dash')
-        ))
-        fig.update_layout(title="Monthly Emission Trends", height=400)
-        st.plotly_chart(fig, use_container_width=True)
-
-elif page == "🧮 Emission Calculator":
-    st.markdown("## 🧮 CO₂ Emission Calculator")
-    st.markdown("Calculate CO₂ emissions using the formula: **CO₂ = Distance × Weight × EmissionFactor**")
-    
-    col1, col2 = st.columns([2, 1])
-    
-    with col1:
-        with st.form("emission_calculator"):
-            st.markdown("### Input Parameters")
-            
-            col_a, col_b = st.columns(2)
-            with col_a:
-                distance = st.number_input("Distance (km)", min_value=0.1, value=500.0, step=10.0)
-                weight = st.number_input("Weight (tonnes)", min_value=0.01, value=2.0, step=0.1)
-            
-            with col_b:
-                transport_mode = st.selectbox(
-                    "Transport Mode",
-                    options=[mode.value for mode in TransportMode],
-                    format_func=lambda x: x.replace('_', ' ').title()
-                )
-            
-            calculate_btn = st.form_submit_button("🧮 Calculate Emissions", type="primary")
-        
-        if calculate_btn:
-            try:
-                # Convert string to TransportMode enum
-                mode_mapping = {
-                    'road_truck': TransportMode.ROAD_TRUCK,
-                    'road_van': TransportMode.ROAD_VAN,
-                    'rail': TransportMode.RAIL,
-                    'air_cargo': TransportMode.AIR_CARGO,
-                    'ship_container': TransportMode.SHIP_CONTAINER,
-                    'ship_bulk': TransportMode.SHIP_BULK
-                }
-                
-                if transport_mode in mode_mapping:
-                    mode = mode_mapping[transport_mode]
-                else:
-                    mode = TransportMode(transport_mode)
-                
-                result = calculator.calculate_emissions(distance, weight, mode)
-                
-                st.success("✅ Calculation Complete!")
-                
-                # Results display
-                col_r1, col_r2, col_r3 = st.columns(3)
-                
-                with col_r1:
-                    st.metric(
-                        "CO₂ Emissions", 
-                        f"{result['co2_emissions_kg']:.2f} kg",
-                        f"{result['co2_emissions_tonnes']:.3f} tonnes"
-                    )
-                
-                with col_r2:
-                    carbon_tax = calculator.calculate_carbon_tax_cost(result['co2_emissions_kg'])
-                    st.metric(
-                        "Carbon Tax Cost", 
-                        f"${carbon_tax['carbon_tax_cost_usd']:.2f}",
-                        "@ $50/tonne CO₂"
-                    )
-                
-                with col_r3:
-                    st.metric(
-                        "Emission Factor", 
-                        f"{result['emission_factor']:.3f}",
-                        "kg CO₂/tonne-km"
-                    )
-                
-                # Comparison with other modes
-                st.markdown("### 🔄 Transport Mode Comparison")
-                comparison_df = calculator.compare_transport_modes(distance, weight)
-                
-                fig = px.bar(
-                    comparison_df,
-                    x='transport_mode',
-                    y='co2_emissions_kg',
-                    color='co2_emissions_kg',
-                    color_continuous_scale=['#2ECC71', '#E74C3C'],
-                    title="CO₂ Emissions by Transport Mode"
-                )
-                fig.update_layout(height=400)
-                st.plotly_chart(fig, use_container_width=True)
-                
-                st.dataframe(comparison_df, use_container_width=True)
-                
-            except Exception as e:
-                st.error(f"❌ Calculation failed: {str(e)}")
-    
-    with col2:
-        st.markdown("### 📋 Emission Factors")
-        factors_df = calculator.get_emission_factors_table()
-        st.dataframe(factors_df, use_container_width=True)
-        
-        st.markdown("### 🌱 Green Tips")
-        st.info("""
-        **Reduce Emissions:**
-        - Choose rail over road when possible
-        - Use container ships for long distances
-        - Optimize load capacity
-        - Consider multimodal transport
-        """)
-
-elif page == "🗺️ Route Optimizer":
-    st.markdown("## 🗺️ Green Route Optimizer")
-    st.markdown("Find the most eco-friendly routes for your shipments")
-    
-    with st.form("route_optimizer"):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            origin = st.text_input("Origin", value="New York, NY", placeholder="Enter origin city")
-            destination = st.text_input("Destination", value="Los Angeles, CA", placeholder="Enter destination city")
-        
-        with col2:
-            weight = st.number_input("Shipment Weight (tonnes)", min_value=0.01, value=5.0, step=0.1)
-            max_time_penalty = st.slider("Max Time Penalty (%)", 0, 50, 10)
-        
-        optimize_btn = st.form_submit_button("🗺️ Find Green Routes", type="primary")
-    
-    if optimize_btn and origin and destination:
-        with st.spinner("🔍 Finding optimal routes..."):
-            try:
-                # Get route recommendations
-                recommendations = optimizer.recommend_green_routes(origin, destination, weight)
-                
-                if 'success' in recommendations and recommendations['success']:
-                    st.success("✅ Route optimization complete!")
-                    
-                    route_data = recommendations['recommendations']
-                    
-                    # Display recommendations
-                    st.markdown("### 🌱 Green Route Recommendations")
-                    
-                    for i, route in enumerate(route_data):
-                        with st.expander(f"Option {i+1}: {route['transport_mode'].replace('_', ' ').title()}", expanded=(i==0)):
-                            col_a, col_b, col_c, col_d = st.columns(4)
-                            
-                            with col_a:
-                                st.metric("CO₂ Emissions", f"{route['co2_emissions_kg']:.1f} kg")
-                            with col_b:
-                                st.metric("Travel Time", f"{route['estimated_travel_time_hours']:.1f} hrs")
-                            with col_c:
-                                st.metric("Carbon Tax", f"${route['carbon_tax_cost_usd']:.2f}")
-                            with col_d:
-                                if route['emission_reduction_percent'] > 0:
-                                    st.metric("Emission Reduction", f"{route['emission_reduction_percent']:.1f}%", "vs worst option")
-                                else:
-                                    st.metric("Emission Impact", "Baseline", "")
-                            
-                            if i == 0:
-                                st.markdown('<span class="eco-badge">🌱 RECOMMENDED</span>', unsafe_allow_html=True)
-                    
-                    # Visualization
-                    if len(route_data) > 1:
-                        st.markdown("### 📊 Route Comparison")
-                        
-                        df_viz = pd.DataFrame(route_data)
-                        
-                        fig = make_subplots(
-                            rows=1, cols=2,
-                            subplot_titles=('CO₂ Emissions (kg)', 'Travel Time (hours)'),
-                            specs=[[{"secondary_y": False}, {"secondary_y": False}]]
-                        )
-                        
-                        fig.add_trace(
-                            go.Bar(
-                                x=df_viz['transport_mode'],
-                                y=df_viz['co2_emissions_kg'],
-                                name='CO₂ Emissions',
-                                marker_color='#2ECC71'
-                            ),
-                            row=1, col=1
-                        )
-                        
-                        fig.add_trace(
-                            go.Bar(
-                                x=df_viz['transport_mode'],
-                                y=df_viz['estimated_travel_time_hours'],
-                                name='Travel Time',
-                                marker_color='#3498DB'
-                            ),
-                            row=1, col=2
-                        )
-                        
-                        fig.update_layout(height=400, showlegend=False)
-                        st.plotly_chart(fig, use_container_width=True)
-                
-                else:
-                    st.error(f"❌ Route optimization failed: {recommendations.get('error', 'Unknown error')}")
-                    
-            except Exception as e:
-                st.error(f"❌ Error: {str(e)}")
-
-elif page == "📊 Scenario Analysis":
-    st.markdown("## 📊 Business Impact Simulation")
-    st.markdown("Analyze the potential impact of adopting green shipping practices")
-    
-    with st.form("scenario_analysis"):
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            st.markdown("### 📦 Shipment Parameters")
-            total_shipments = st.number_input("Total Monthly Shipments", min_value=1, value=1000, step=50)
-            avg_distance = st.number_input("Average Distance (km)", min_value=1.0, value=800.0, step=50.0)
-            avg_weight = st.number_input("Average Weight (tonnes)", min_value=0.1, value=3.0, step=0.1)
-        
-        with col2:
-            st.markdown("### ⚙️ Optimization Parameters")
-            optimization_percent = st.slider("% Shipments Using Green Routes", 0, 100, 50)
-            current_mode = st.selectbox("Current Transport Mode", [mode.value for mode in TransportMode], index=0)
-            carbon_tax_rate = st.number_input("Carbon Tax Rate ($/tonne CO₂)", min_value=0.0, value=50.0, step=5.0)
-        
-        analyze_btn = st.form_submit_button("📊 Run Scenario Analysis", type="primary")
-    
-    if analyze_btn:
-        with st.spinner("🔄 Running business impact simulation..."):
-            try:
-                # Current emissions - use same mapping as before
-                mode_mapping = {
-                    'road_truck': TransportMode.ROAD_TRUCK,
-                    'road_van': TransportMode.ROAD_VAN,
-                    'rail': TransportMode.RAIL,
-                    'air_cargo': TransportMode.AIR_CARGO,
-                    'ship_container': TransportMode.SHIP_CONTAINER,
-                    'ship_bulk': TransportMode.SHIP_BULK
-                }
-                
-                if current_mode in mode_mapping:
-                    current_mode_enum = mode_mapping[current_mode]
-                else:
-                    current_mode_enum = TransportMode(current_mode)
-                
-                current_emissions = calculator.calculate_emissions(avg_distance, avg_weight, current_mode_enum)
-                
-                # Find best green alternative - calculate manually to avoid EmissionOptimizer import
-                available_modes = [TransportMode.ROAD_TRUCK, TransportMode.RAIL, TransportMode.SHIP_CONTAINER]
-                
-                # Calculate emissions for each mode and find the greenest
-                mode_options = []
-                for mode in available_modes:
-                    emissions = calculator.calculate_emissions(avg_distance, avg_weight, mode)
-                    mode_options.append({
-                        'mode': mode,
-                        'co2_emissions_kg': emissions['co2_emissions_kg'],
-                        'emission_factor': emissions['emission_factor']
-                    })
-                
-                # Sort by emissions (lowest first) to find greenest option
-                mode_options.sort(key=lambda x: x['co2_emissions_kg'])
-                green_option = mode_options[0] if mode_options else None
-                
-                if not green_option:
-                    st.error("❌ No green alternatives available")
-                    st.stop()
-                
-                # Calculate scenario impact
-                optimized_shipments = int(total_shipments * optimization_percent / 100)
-                regular_shipments = total_shipments - optimized_shipments
-                
-                current_total = current_emissions['co2_emissions_kg'] * total_shipments
-                optimized_total = (
-                    green_option['co2_emissions_kg'] * optimized_shipments +
-                    current_emissions['co2_emissions_kg'] * regular_shipments
-                )
-                
-                savings_kg = current_total - optimized_total
-                savings_percent = (savings_kg / current_total) * 100
-                carbon_tax_savings = (savings_kg / 1000) * carbon_tax_rate
-                
-                st.success("✅ Scenario analysis complete!")
-                
-                # Results
-                col1, col2, col3, col4 = st.columns(4)
-                
-                with col1:
-                    st.metric("Current Emissions", f"{current_total/1000:.1f} tonnes/month")
-                with col2:
-                    st.metric("Optimized Emissions", f"{optimized_total/1000:.1f} tonnes/month")
-                with col3:
-                    st.metric("CO₂ Savings", f"{savings_kg/1000:.1f} tonnes/month", f"{savings_percent:.1f}% reduction")
-                with col4:
-                    st.metric("Carbon Tax Savings", f"${carbon_tax_savings:.0f}/month", f"${carbon_tax_savings*12:.0f}/year")
-                
-                # Visualization
-                st.markdown("### 📈 Impact Visualization")
-                
-                scenario_data = pd.DataFrame({
-                    'Scenario': ['Current', 'Optimized'],
-                    'CO₂ Emissions (tonnes)': [current_total/1000, optimized_total/1000],
-                    'Carbon Tax Cost ($)': [
-                        (current_total/1000) * carbon_tax_rate,
-                        (optimized_total/1000) * carbon_tax_rate
-                    ]
-                })
-                
-                fig = make_subplots(
-                    rows=1, cols=2,
-                    subplot_titles=('CO₂ Emissions', 'Carbon Tax Cost'),
-                    specs=[[{"secondary_y": False}, {"secondary_y": False}]]
-                )
-                
-                fig.add_trace(
-                    go.Bar(
-                        x=scenario_data['Scenario'],
-                        y=scenario_data['CO₂ Emissions (tonnes)'],
-                        name='CO₂ Emissions',
-                        marker_color=['#E74C3C', '#2ECC71']
-                    ),
-                    row=1, col=1
-                )
-                
-                fig.add_trace(
-                    go.Bar(
-                        x=scenario_data['Scenario'],
-                        y=scenario_data['Carbon Tax Cost ($)'],
-                        name='Carbon Tax Cost',
-                        marker_color=['#E74C3C', '#2ECC71']
-                    ),
-                    row=1, col=2
-                )
-                
-                fig.update_layout(height=400, showlegend=False)
-                st.plotly_chart(fig, use_container_width=True)
-                
-                # Business benefits
-                st.markdown("### 💼 Business Benefits")
-                
-                benefits_col1, benefits_col2 = st.columns(2)
-                
-                with benefits_col1:
-                    st.markdown("""
-                    **Environmental Impact:**
-                    - ♻️ Reduced carbon footprint
-                    - 🌱 Enhanced sustainability profile
-                    - 📊 ESG score improvement
-                    - 🏆 Industry leadership positioning
-                    """)
-                
-                with benefits_col2:
-                    st.markdown(f"""
-                    **Financial Benefits:**
-                    - 💰 ${carbon_tax_savings*12:.0f} annual tax savings
-                    - 📈 Potential green financing access
-                    - 🎯 Regulatory compliance readiness
-                    - 💡 Operational efficiency gains
-                    """)
-                
-            except Exception as e:
-                st.error(f"❌ Analysis failed: {str(e)}")
-                st.write(f"Debug info: {type(e).__name__}: {e}")
-                import traceback
-                st.code(traceback.format_exc())
-
-elif page == "📈 Analytics":
-    st.markdown("## 📈 Advanced Analytics")
-    
-    # Sample analytics data
-    st.markdown("### 🎯 Performance Metrics")
-    
-    col1, col2, col3 = st.columns(3)
-    
-    with col1:
-        st.markdown("""
-        <div class="metric-card">
-            <h4 style="color: #2ECC71; margin: 0;">Route Efficiency Score</h4>
-            <h2 style="margin: 0;">87/100</h2>
-            <p style="margin: 0; color: #7F8C8D;">Above industry average</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col2:
-        st.markdown("""
-        <div class="metric-card">
-            <h4 style="color: #2ECC71; margin: 0;">Green Route Adoption</h4>
-            <h2 style="margin: 0;">68%</h2>
-            <p style="margin: 0; color: #7F8C8D;">Target: 75%</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    with col3:
-        st.markdown("""
-        <div class="metric-card">
-            <h4 style="color: #2ECC71; margin: 0;">Emission Intensity</h4>
-            <h2 style="margin: 0;">0.045</h2>
-            <p style="margin: 0; color: #7F8C8D;">kg CO₂/tonne-km</p>
-        </div>
-        """, unsafe_allow_html=True)
-    
-    # Regional analysis
-    st.markdown("### 🌍 Regional Emission Analysis")
-    
-    regional_data = pd.DataFrame({
-        'Region': ['North America', 'Europe', 'Asia Pacific', 'Latin America'],
-        'Emissions (tonnes)': [45.2, 32.1, 28.7, 15.3],
-        'Shipments': [450, 320, 380, 180],
-        'Avg Distance (km)': [1200, 800, 950, 600]
-    })
-    
-    fig = px.scatter(
-        regional_data,
-        x='Shipments',
-        y='Emissions (tonnes)',
-        size='Avg Distance (km)',
-        color='Region',
-        title="Regional Emission vs Shipment Volume"
-    )
-    fig.update_layout(height=500)
-    st.plotly_chart(fig, use_container_width=True)
-    
-    st.dataframe(regional_data, use_container_width=True)
-
-
-# Footer
-st.markdown("---")
-st.markdown("## 👨‍💻 Developer Details")
-st.markdown("""
-<div style="text-align: center; color: #2C3E50; padding: 2rem; background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%); border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">
-    <div style="margin-bottom: 1rem;">
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 200 200" style="width: 60px; height: 60px;">
-          <defs>
-            <linearGradient id="zkGradient" x1="0%" y1="0%" x2="100%" y2="100%">
-              <stop offset="0%" style="stop-color:#2ECC71;stop-opacity:1" />
-              <stop offset="100%" style="stop-color:#27AE60;stop-opacity:1" />
-            </linearGradient>
-          </defs>
-          <g transform="translate(50, 50)">
-            <path d="M10 20 L70 20 L70 35 L35 75 L70 75 L70 90 L10 90 L10 75 L45 35 L10 35 Z" 
-                  fill="url(#zkGradient)" 
-                  stroke="#1E8449" 
-                  stroke-width="1"/>
-            <path d="M80 20 L95 20 L95 50 L110 20 L130 20 L110 55 L130 90 L110 90 L95 65 L95 90 L80 90 Z" 
-                  fill="url(#zkGradient)" 
-                  stroke="#1E8449" 
-                  stroke-width="1"/>
-          </g>
-        </svg>
-    </div>
-    <p style="font-size: 1.2rem; color: #2C3E50; margin-bottom: 0.5rem;"><strong>GreenPath</strong> - AI-Powered CO₂ Emission Reduction Platform</p>
-    <p style="font-size: 1.1rem; color: #34495E; margin-bottom: 1rem;">Designed and Developed by <strong>Sayed Mohd Zayeem Khateeb</strong></p>
-    <div style="margin: 1rem 0;">
-        <a href="https://github.com/zayeemskhateeb-cloud" target="_blank" style="margin: 0 10px; text-decoration: none; color: #2ECC71; font-weight: bold; font-size: 1rem;">🌐 GitHub</a> | 
-        <a href="https://www.linkedin.com/in/zayeemkhateeb" target="_blank" style="margin: 0 10px; text-decoration: none; color: #2ECC71; font-weight: bold; font-size: 1rem;">💼 LinkedIn</a> | 
-        <a href="mailto:zayeem.s.khateeb@gmail.com" style="margin: 0 10px; text-decoration: none; color: #2ECC71; font-weight: bold; font-size: 1rem;">📧 Email</a>
-    </div>
-    <p style="font-size: 1rem; color: #7F8C8D; margin-top: 1rem;">
-        Specialized in AI/ML, Data Analytics, and Sustainable Technology Solutions
-    </p>
-</div>
-""", unsafe_allow_html=True)
+"""
+GreenPath - AI & Data Analytics Platform for Reducing Shipment CO₂ Emissions
+Professional Streamlit Frontend with Eco-Friendly Design
+"""
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+import requests
+import sys
+import os
+from datetime import datetime, timedelta
+import numpy as np
+
+# Add src directory to path
+sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
+
+from emissions.emission_calculator import EmissionCalculator, TransportMode
+from route_optimizer.green_route_optimizer import GreenRouteOptimizer
+
+# Page configuration
+st.set_page_config(
+    page_title="GreenPath - CO₂ Emission Tracker",
+    page_icon="🌱",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for eco-friendly theme
+st.markdown("""
+<style>
+    
+    .main-header {
+        background: linear-gradient(90deg, #2ECC71 0%, #27AE60 100%);
+        padding: 2rem;
+        border-radius: 10px;
+        color: white;
+        text-align: center;
+        margin-bottom: 2rem;
+        position: relative;
+        z-index: 1;
+    }
+    
+    .metric-card {
+        background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%);
+        padding: 1.5rem;
+        border-radius: 10px;
+        box-shadow: 0 2px 10px rgba(0,0,0,0.1);
+        border-left: 4px solid #2ECC71;
+        margin-bottom: 1rem;
+    }
+    
+    .green-button {
+        background-color: #2ECC71;
+        color: white;
+        border: none;
+        padding: 0.5rem 1rem;
+        border-radius: 5px;
+        cursor: pointer;
+    }
+    
+    .sidebar .sidebar-content {
+        background-color: #F8F9FA;
+    }
+    
+    .stSelectbox > div > div {
+        background-color: white !important;
+        border: 1px solid #E0E0E0 !important;
+        border-radius: 8px !important;
+        color: #34495E !important;
+    }
+    
+    .stSelectbox > div > div > div {
+        color: #34495E !important;
+        font-weight: 500 !important;
+    }
+    
+    .eco-badge {
+        background-color: #2ECC71;
+        color: white;
+        padding: 0.2rem 0.5rem;
+        border-radius: 15px;
+        font-size: 0.8rem;
+        font-weight: bold;
+    }
+    
+    
+    /* Navigation styling */
+    .stRadio > div {
+        background-color: transparent;
+        padding: 0.5rem;
+        border-radius: 8px;
+    }
+    
+    .stRadio > div > label {
+        background-color: #FFFFFF;
+        border: 1px solid #E0E0E0;
+        border-radius: 8px;
+        margin: 4px 0;
+        padding: 8px 12px;
+        cursor: pointer;
+        transition: all 0.3s ease;
+        display: block;
+    }
+    
+    .stRadio > div > label:hover {
+        background-color: #E8F5E8;
+        border-color: #2ECC71;
+        color: #2ECC71;
+    }
+    
+    .stRadio > div > label > div {
+        color: #34495E !important;
+        font-size: 14px !important;
+        font-weight: 500;
+        margin: 0;
+    }
+    
+    .stRadio > div > label[data-checked="true"] {
+        background-color: #2ECC71;
+        border-color: #2ECC71;
+        color: white;
+    }
+    
+    .stRadio > div > label[data-checked="true"] > div {
+        color: white !important;
+    }
+    
+    /* Emoji fix */
+    .stRadio label {
+        font-family: "Apple Color Emoji", "Segoe UI Emoji", "Noto Color Emoji", sans-serif;
+    }
+    
+    /* Sidebar styling */
+    .css-1d391kg {
+        background-color: #F8F9FA;
+    }
+    
+    /* Metric styling */
+    [data-testid="metric-container"] {
+        background-color: white;
+        border: 1px solid #E0E0E0;
+        padding: 1rem;
+        border-radius: 8px;
+        border-left: 4px solid #2ECC71;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize components
+@st.cache_resource
+def init_components():
+    calculator = EmissionCalculator()
+    optimizer = GreenRouteOptimizer()
+    return calculator, optimizer
+
+calculator, optimizer = init_components()
+
+# Header
+st.markdown("""
+<div class="main-header">
+    <h1>🌱 GreenPath</h1>
+    <h3>AI-Powered Platform for Reducing Shipment CO₂ Emissions</h3>
+    <p>Designed by Sayed Mohd Zayeem Khateeb</p>
+</div>
+""", unsafe_allow_html=True)
+
+# Sidebar
+with st.sidebar:
+    # Custom GreenPath logo
+    st.markdown("""
+    <div style="text-align: center; padding: 1rem; background: linear-gradient(135deg, #2ECC71, #27AE60); border-radius: 10px; margin-bottom: 1rem;">
+        <h2 style="color: white; margin: 0; font-size: 24px;">🌱 GreenPath</h2>
+        <p style="color: #E8F8F5; margin: 0; font-size: 12px;">AI Emission Tracker</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    st.markdown("### 📍 Navigate")
+    
+    # Navigation with better visibility
+    nav_options = {
+        "🏠 Dashboard": "Dashboard",
+        "🧮 Emission Calculator": "Emission Calculator", 
+        "🗺️ Route Optimizer": "Route Optimizer",
+        "📊 Scenario Analysis": "Scenario Analysis",
+        "📈 Analytics": "Analytics"
+    }
+    
+    page = st.selectbox(
+        "Choose a page:",
+        options=list(nav_options.keys()),
+        label_visibility="collapsed",
+        key="navigation"
+    )
+    
+    st.markdown("---")
+    st.markdown("### 🌍 Quick Stats")
+    
+    # Sample KPIs
+    col1, col2 = st.columns(2)
+    with col1:
+        st.metric("CO₂ Saved", "2.4t", "↓ 22%")
+    with col2:
+        st.metric("Routes Optimized", "156", "↑ 15%")
+
+# Main content based on selected page
+if page == "🏠 Dashboard":
+    st.markdown("## 📊 Emission Overview Dashboard")
+    
+    # Top KPIs
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        st.markdown("""
+        <div class="metric-card">
+            <h3 style="color: #2ECC71; margin: 0;">12.5t</h3>
+            <p style="margin: 0; color: #7F8C8D;">Total CO₂ Emissions</p>
+            <small style="color: #E74C3C;">↓ 18% vs last month</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class="metric-card">
+            <h3 style="color: #2ECC71; margin: 0;">22%</h3>
+            <p style="margin: 0; color: #7F8C8D;">Emission Reduction</p>
+            <small style="color: #27AE60;">Green routes adopted</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown("""
+        <div class="metric-card">
+            <h3 style="color: #2ECC71; margin: 0;">0.08</h3>
+            <p style="margin: 0; color: #7F8C8D;">Avg. Emission/Shipment (kg)</p>
+            <small style="color: #27AE60;">Industry best practice</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col4:
+        st.markdown("""
+        <div class="metric-card">
+            <h3 style="color: #2ECC71; margin: 0;">$1,250</h3>
+            <p style="margin: 0; color: #7F8C8D;">Carbon Tax Savings</p>
+            <small style="color: #27AE60;">Monthly estimate</small>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Charts
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("### 🚛 Emissions by Transport Mode")
+        
+        # Sample data for transport mode comparison
+        modes_data = pd.DataFrame({
+            'Transport Mode': ['Road Truck', 'Rail', 'Ship Container', 'Air Cargo'],
+            'CO₂ Emissions (kg)': [62, 22, 11, 602],
+            'Usage %': [45, 30, 20, 5]
+        })
+        
+        fig = px.bar(
+            modes_data, 
+            x='Transport Mode', 
+            y='CO₂ Emissions (kg)',
+            color='CO₂ Emissions (kg)',
+            color_continuous_scale=['#2ECC71', '#E74C3C'],
+            title="Emission Factors by Transport Mode"
+        )
+        fig.update_layout(showlegend=False, height=400)
+        st.plotly_chart(fig, use_container_width=True)
+    
+    with col2:
+        st.markdown("### 📈 Emission Trends")
+        
+        # Sample trend data
+        emissions_data = [15.2, 14.8, 13.9, 13.1, 12.8, 12.3, 11.9, 11.5, 12.5]
+        dates = pd.date_range(start='2024-01-01', periods=len(emissions_data), freq='M')
+        trend_data = pd.DataFrame({
+            'Date': dates,
+            'Emissions (tonnes)': emissions_data,
+            'Target': [14.0] * len(emissions_data)
+        })
+        
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(
+            x=trend_data['Date'], 
+            y=trend_data['Emissions (tonnes)'],
+            mode='lines+markers',
+            name='Actual Emissions',
+            line=dict(color='#2ECC71', width=3)
+        ))
+        fig.add_trace(go.Scatter(
+            x=trend_data['Date'], 
+            y=trend_data['Target'],
+            mode='lines',
+            name='Target',
+            line=dict(color='#E74C3C', dash='dash')
+        ))
+        fig.update_layout(title="Monthly Emission Trends", height=400)
+        st.plotly_chart(fig, use_container_width=True)
+
+elif page == "🧮 Emission Calculator":
+    st.markdown("## 🧮 CO₂ Emission Calculator")
+    st.markdown("Calculate CO₂ emissions using the formula: **CO₂ = Distance × Weight × EmissionFactor**")
+    
+    col1, col2 = st.columns([2, 1])
+    
+    with col1:
+        with st.form("emission_calculator"):
+            st.markdown("### Input Parameters")
+            
+            col_a, col_b = st.columns(2)
+            with col_a:
+                distance = st.number_input("Distance (km)", min_value=0.1, value=500.0, step=10.0)
+                weight = st.number_input("Weight (tonnes)", min_value=0.01, value=2.0, step=0.1)
+            
+            with col_b:
+                transport_mode = st.selectbox(
+                    "Transport Mode",
+                    options=[mode.value for mode in TransportMode],
+                    format_func=lambda x: x.replace('_', ' ').title()
+                )
+            
+            calculate_btn = st.form_submit_button("🧮 Calculate Emissions", type="primary")
+        
+        if calculate_btn:
+            try:
+                # Convert string to TransportMode enum
+                mode_mapping = {
+                    'road_truck': TransportMode.ROAD_TRUCK,
+                    'road_van': TransportMode.ROAD_VAN,
+                    'rail': TransportMode.RAIL,
+                    'air_cargo': TransportMode.AIR_CARGO,
+                    'ship_container': TransportMode.SHIP_CONTAINER,
+                    'ship_bulk': TransportMode.SHIP_BULK
+                }
+                
+                if transport_mode in mode_mapping:
+                    mode = mode_mapping[transport_mode]
+                else:
+                    mode = TransportMode(transport_mode)
+                
+                result = calculator.calculate_emissions(distance, weight, mode)
+                
+                st.success("✅ Calculation Complete!")
+                
+                # Results display
+                col_r1, col_r2, col_r3 = st.columns(3)
+                
+                with col_r1:
+                    st.metric(
+                        "CO₂ Emissions", 
+                        f"{result['co2_emissions_kg']:.2f} kg",
+                        f"{result['co2_emissions_tonnes']:.3f} tonnes"
+                    )
+                
+                with col_r2:
+                    carbon_tax = calculator.calculate_carbon_tax_cost(result['co2_emissions_kg'])
+                    st.metric(
+                        "Carbon Tax Cost", 
+                        f"${carbon_tax['carbon_tax_cost_usd']:.2f}",
+                        "@ $50/tonne CO₂"
+                    )
+                
+                with col_r3:
+                    st.metric(
+                        "Emission Factor", 
+                        f"{result['emission_factor']:.3f}",
+                        "kg CO₂/tonne-km"
+                    )
+                
+                # Comparison with other modes
+                st.markdown("### 🔄 Transport Mode Comparison")
+                comparison_df = calculator.compare_transport_modes(distance, weight)
+                
+                fig = px.bar(
+                    comparison_df,
+                    x='transport_mode',
+                    y='co2_emissions_kg',
+                    color='co2_emissions_kg',
+                    color_continuous_scale=['#2ECC71', '#E74C3C'],
+                    title="CO₂ Emissions by Transport Mode"
+                )
+                fig.update_layout(height=400)
+                st.plotly_chart(fig, use_container_width=True)
+                
+                st.dataframe(comparison_df, use_container_width=True)
+                
+            except Exception as e:
+                st.error(f"❌ Calculation failed: {str(e)}")
+    
+    with col2:
+        st.markdown("### 📋 Emission Factors")
+        factors_df = calculator.get_emission_factors_table()
+        st.dataframe(factors_df, use_container_width=True)
+        
+        st.markdown("### 🌱 Green Tips")
+        st.info("""
+        **Reduce Emissions:**
+        - Choose rail over road when possible
+        - Use container ships for long distances
+        - Optimize load capacity
+        - Consider multimodal transport
+        """)
+
+elif page == "🗺️ Route Optimizer":
+    st.markdown("## 🗺️ Green Route Optimizer")
+    st.markdown("Find the most eco-friendly routes for your shipments")
+    
+    with st.form("route_optimizer"):
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            origin = st.text_input("Origin", value="New York, NY", placeholder="Enter origin city")
+            destination = st.text_input("Destination", value="Los Angeles, CA", placeholder="Enter destination city")
+        
+        with col2:
+            weight = st.number_input("Shipment Weight (tonnes)", min_value=0.01, value=5.0, step=0.1)
+            max_time_penalty = st.slider("Max Time Penalty (%)", 0, 50, 10)
+        
+        optimize_btn = st.form_submit_button("🗺️ Find Green Routes", type="primary")
+    
+    if optimize_btn and origin and destination:
+        with st.spinner("🔍 Finding optimal routes..."):
+            try:
+                # Get route recommendations
+                recommendations = optimizer.recommend_green_routes(origin, destination, weight)
+                
+                if 'error' not in recommendations::
+                    st.success("✅ Route optimization complete!")
+                    
+                    route_data = recommendations['recommendations']
+                    
+                    # Display recommendations
+                    st.markdown("### 🌱 Green Route Recommendations")
+                    
+                    for i, route in enumerate(route_data):
+                        with st.expander(f"Option {i+1}: {route['transport_mode'].replace('_', ' ').title()}", expanded=(i==0)):
+                            col_a, col_b, col_c, col_d = st.columns(4)
+                            
+                            with col_a:
+                                st.metric("CO₂ Emissions", f"{route['co2_emissions_kg']:.1f} kg")
+                            with col_b:
+                                st.metric("Travel Time", f"{route['estimated_travel_time_hours']:.1f} hrs")
+                            with col_c:
+                                st.metric("Carbon Tax", f"${route['carbon_tax_cost_usd']:.2f}")
+                            with col_d:
+                                if route['emission_reduction_percent'] > 0:
+                                    st.metric("Emission Reduction", f"{route['emission_reduction_percent']:.1f}%", "vs worst option")
+                                else:
+                                    st.metric("Emission Impact", "Baseline", "")
+                            
+                            if i == 0:
+                                st.markdown('<span class="eco-badge">🌱 RECOMMENDED</span>', unsafe_allow_html=True)
+                    
+                    # Visualization
+                    if len(route_data) > 1:
+                        st.markdown("### 📊 Route Comparison")
+                        
+                        df_viz = pd.DataFrame(route_data)
+                        
+                        fig = make_subplots(
+                            rows=1, cols=2,
+                            subplot_titles=('CO₂ Emissions (kg)', 'Travel Time (hours)'),
+                            specs=[[{"secondary_y": False}, {"secondary_y": False}]]
+                        )
+                        
+                        fig.add_trace(
+                            go.Bar(
+                                x=df_viz['transport_mode'],
+                                y=df_viz['co2_emissions_kg'],
+                                name='CO₂ Emissions',
+                                marker_color='#2ECC71'
+                            ),
+                            row=1, col=1
+                        )
+                        
+                        fig.add_trace(
+                            go.Bar(
+                                x=df_viz['transport_mode'],
+                                y=df_viz['estimated_travel_time_hours'],
+                                name='Travel Time',
+                                marker_color='#3498DB'
+                            ),
+                            row=1, col=2
+                        )
+                        
+                        fig.update_layout(height=400, showlegend=False)
+                        st.plotly_chart(fig, use_container_width=True)
+                
+                else:
+                    st.error(f"❌ Route optimization failed: {recommendations.get('error', 'Unknown error')}")
+                    
+            except Exception as e:
+                st.error(f"❌ Error: {str(e)}")
+
+elif page == "📊 Scenario Analysis":
+    st.markdown("## 📊 Business Impact Simulation")
+    st.markdown("Analyze the potential impact of adopting green shipping practices")
+    
+    with st.form("scenario_analysis"):
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.markdown("### 📦 Shipment Parameters")
+            total_shipments = st.number_input("Total Monthly Shipments", min_value=1, value=1000, step=50)
+            avg_distance = st.number_input("Average Distance (km)", min_value=1.0, value=800.0, step=50.0)
+            avg_weight = st.number_input("Average Weight (tonnes)", min_value=0.1, value=3.0, step=0.1)
+        
+        with col2:
+            st.markdown("### ⚙️ Optimization Parameters")
+            optimization_percent = st.slider("% Shipments Using Green Routes", 0, 100, 50)
+            current_mode = st.selectbox("Current Transport Mode", [mode.value for mode in TransportMode], index=0)
+            carbon_tax_rate = st.number_input("Carbon Tax Rate ($/tonne CO₂)", min_value=0.0, value=50.0, step=5.0)
+        
+        analyze_btn = st.form_submit_button("📊 Run Scenario Analysis", type="primary")
+    
+    if analyze_btn:
+        with st.spinner("🔄 Running business impact simulation..."):
+            try:
+                # Current emissions - use same mapping as before
+                mode_mapping = {
+                    'road_truck': TransportMode.ROAD_TRUCK,
+                    'road_van': TransportMode.ROAD_VAN,
+                    'rail': TransportMode.RAIL,
+                    'air_cargo': TransportMode.AIR_CARGO,
+                    'ship_container': TransportMode.SHIP_CONTAINER,
+                    'ship_bulk': TransportMode.SHIP_BULK
+                }
+                
+                if current_mode in mode_mapping:
+                    current_mode_enum = mode_mapping[current_mode]
+                else:
+                    current_mode_enum = TransportMode(current_mode)
+                
+                current_emissions = calculator.calculate_emissions(avg_distance, avg_weight, current_mode_enum)
+                
+                # Find best green alternative - calculate manually to avoid EmissionOptimizer import
+                available_modes = [TransportMode.ROAD_TRUCK, TransportMode.RAIL, TransportMode.SHIP_CONTAINER]
+                
+                # Calculate emissions for each mode and find the greenest
+                mode_options = []
+                for mode in available_modes:
+                    emissions = calculator.calculate_emissions(avg_distance, avg_weight, mode)
+                    mode_options.append({
+                        'mode': mode,
+                        'co2_emissions_kg': emissions['co2_emissions_kg'],
+                        'emission_factor': emissions['emission_factor']
+                    })
+                
+                # Sort by emissions (lowest first) to find greenest option
+                mode_options.sort(key=lambda x: x['co2_emissions_kg'])
+                green_option = mode_options[0] if mode_options else None
+                
+                if not green_option:
+                    st.error("❌ No green alternatives available")
+                    st.stop()
+                
+                # Calculate scenario impact
+                optimized_shipments = int(total_shipments * optimization_percent / 100)
+                regular_shipments = total_shipments - optimized_shipments
+                
+                current_total = current_emissions['co2_emissions_kg'] * total_shipments
+                optimized_total = (
+                    green_option['co2_emissions_kg'] * optimized_shipments +
+                    current_emissions['co2_emissions_kg'] * regular_shipments
+                )
+                
+                savings_kg = current_total - optimized_total
+                savings_percent = (savings_kg / current_total) * 100
+                carbon_tax_savings = (savings_kg / 1000) * carbon_tax_rate
+                
+                st.success("✅ Scenario analysis complete!")
+                
+                # Results
+                col1, col2, col3, col4 = st.columns(4)
+                
+                with col1:
+                    st.metric("Current Emissions", f"{current_total/1000:.1f} tonnes/month")
+                with col2:
+                    st.metric("Optimized Emissions", f"{optimized_total/1000:.1f} tonnes/month")
+                with col3:
+                    st.metric("CO₂ Savings", f"{savings_kg/1000:.1f} tonnes/month", f"{savings_percent:.1f}% reduction")
+                with col4:
+                    st.metric("Carbon Tax Savings", f"${carbon_tax_savings:.0f}/month", f"${carbon_tax_savings*12:.0f}/year")
+                
+                # Visualization
+                st.markdown("### 📈 Impact Visualization")
+                
+                scenario_data = pd.DataFrame({
+                    'Scenario': ['Current', 'Optimized'],
+                    'CO₂ Emissions (tonnes)': [current_total/1000, optimized_total/1000],
+                    'Carbon Tax Cost ($)': [
+                        (current_total/1000) * carbon_tax_rate,
+                        (optimized_total/1000) * carbon_tax_rate
+                    ]
+                })
+                
+                fig = make_subplots(
+                    rows=1, cols=2,
+                    subplot_titles=('CO₂ Emissions', 'Carbon Tax Cost'),
+                    specs=[[{"secondary_y": False}, {"secondary_y": False}]]
+                )
+                
+                fig.add_trace(
+                    go.Bar(
+                        x=scenario_data['Scenario'],
+                        y=scenario_data['CO₂ Emissions (tonnes)'],
+                        name='CO₂ Emissions',
+                        marker_color=['#E74C3C', '#2ECC71']
+                    ),
+                    row=1, col=1
+                )
+                
+                fig.add_trace(
+                    go.Bar(
+                        x=scenario_data['Scenario'],
+                        y=scenario_data['Carbon Tax Cost ($)'],
+                        name='Carbon Tax Cost',
+                        marker_color=['#E74C3C', '#2ECC71']
+                    ),
+                    row=1, col=2
+                )
+                
+                fig.update_layout(height=400, showlegend=False)
+                st.plotly_chart(fig, use_container_width=True)
+                
+                # Business benefits
+                st.markdown("### 💼 Business Benefits")
+                
+                benefits_col1, benefits_col2 = st.columns(2)
+                
+                with benefits_col1:
+                    st.markdown("""
+                    **Environmental Impact:**
+                    - ♻️ Reduced carbon footprint
+                    - 🌱 Enhanced sustainability profile
+                    - 📊 ESG score improvement
+                    - 🏆 Industry leadership positioning
+                    """)
+                
+                with benefits_col2:
+                    st.markdown(f"""
+                    **Financial Benefits:**
+                    - 💰 ${carbon_tax_savings*12:.0f} annual tax savings
+                    - 📈 Potential green financing access
+                    - 🎯 Regulatory compliance readiness
+                    - 💡 Operational efficiency gains
+                    """)
+                
+            except Exception as e:
+                st.error(f"❌ Analysis failed: {str(e)}")
+                st.write(f"Debug info: {type(e).__name__}: {e}")
+                import traceback
+                st.code(traceback.format_exc())
+
+elif page == "📈 Analytics":
+    st.markdown("## 📈 Advanced Analytics")
+    
+    # Sample analytics data
+    st.markdown("### 🎯 Performance Metrics")
+    
+    col1, col2, col3 = st.columns(3)
+    
+    with col1:
+        st.markdown("""
+        <div class="metric-card">
+            <h4 style="color: #2ECC71; margin: 0;">Route Efficiency Score</h4>
+            <h2 style="margin: 0;">87/100</h2>
+            <p style="margin: 0; color: #7F8C8D;">Above industry average</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class="metric-card">
+            <h4 style="color: #2ECC71; margin: 0;">Green Route Adoption</h4>
+            <h2 style="margin: 0;">68%</h2>
+            <p style="margin: 0; color: #7F8C8D;">Target: 75%</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col3:
+        st.markdown("""
+        <div class="metric-card">
+            <h4 style="color: #2ECC71; margin: 0;">Emission Intensity</h4>
+            <h2 style="margin: 0;">0.045</h2>
+            <p style="margin: 0; color: #7F8C8D;">kg CO₂/tonne-km</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Regional analysis
+    st.markdown("### 🌍 Regional Emission Analysis")
+    
+    regional_data = pd.DataFrame({
+        'Region': ['North America', 'Europe', 'Asia Pacific', 'Latin America'],
+        'Emissions (tonnes)': [45.2, 32.1, 28.7, 15.3],
+        'Shipments': [450, 320, 380, 180],
+        'Avg Distance (km)': [1200, 800, 950, 600]
+    })
+    
+    fig = px.scatter(
+        regional_data,
+        x='Shipments',
+        y='Emissions (tonnes)',
+        size='Avg Distance (km)',
+        color='Region',
+        title="Regional Emission vs Shipment Volume"
+    )
+    fig.update_layout(height=500)
+    st.plotly_chart(fig, use_container_width=True)
+    
+    st.dataframe(regional_data, use_container_width=True)
+
+
+# Footer
+st.markdown("---")
+st.markdown("## 👨‍💻 Developer Details")
+st.markdown("""
+<div style="text-align: center; color: #2C3E50; padding: 2rem; background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%); border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1);">
+    <div style="margin-bottom: 1rem;">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 200 200" style="width: 60px; height: 60px;">
+          <defs>
+            <linearGradient id="zkGradient" x1="0%" y1="0%" x2="100%" y2="100%">
+              <stop offset="0%" style="stop-color:#2ECC71;stop-opacity:1" />
+              <stop offset="100%" style="stop-color:#27AE60;stop-opacity:1" />
+            </linearGradient>
+          </defs>
+          <g transform="translate(50, 50)">
+            <path d="M10 20 L70 20 L70 35 L35 75 L70 75 L70 90 L10 90 L10 75 L45 35 L10 35 Z" 
+                  fill="url(#zkGradient)" 
+                  stroke="#1E8449" 
+                  stroke-width="1"/>
+            <path d="M80 20 L95 20 L95 50 L110 20 L130 20 L110 55 L130 90 L110 90 L95 65 L95 90 L80 90 Z" 
+                  fill="url(#zkGradient)" 
+                  stroke="#1E8449" 
+                  stroke-width="1"/>
+          </g>
+        </svg>
+    </div>
+    <p style="font-size: 1.2rem; color: #2C3E50; margin-bottom: 0.5rem;"><strong>GreenPath</strong> - AI-Powered CO₂ Emission Reduction Platform</p>
+    <p style="font-size: 1.1rem; color: #34495E; margin-bottom: 1rem;">Designed and Developed by <strong>Sayed Mohd Zayeem Khateeb</strong></p>
+    <div style="margin: 1rem 0;">
+        <a href="https://github.com/zayeemskhateeb-cloud" target="_blank" style="margin: 0 10px; text-decoration: none; color: #2ECC71; font-weight: bold; font-size: 1rem;">🌐 GitHub</a> | 
+        <a href="https://www.linkedin.com/in/zayeemkhateeb" target="_blank" style="margin: 0 10px; text-decoration: none; color: #2ECC71; font-weight: bold; font-size: 1rem;">💼 LinkedIn</a> | 
+        <a href="mailto:zayeem.s.khateeb@gmail.com" style="margin: 0 10px; text-decoration: none; color: #2ECC71; font-weight: bold; font-size: 1rem;">📧 Email</a>
+    </div>
+    <p style="font-size: 1rem; color: #7F8C8D; margin-top: 1rem;">
+        Specialized in AI/ML, Data Analytics, and Sustainable Technology Solutions
+    </p>
+</div>
+""", unsafe_allow_html=True)