"""
Optimization Results Display Functions for Streamlit
Handles visualization of optimization results with charts and tables
"""

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import sys
import json

# Load hierarchy data for enhanced visualization
def load_kit_hierarchy():
    """Load kit hierarchy data from JSON file"""
    try:
        with open('data/hierarchy_exports/kit_hierarchy.json', 'r') as f:
            return json.load(f)
    except (FileNotFoundError, json.JSONDecodeError):
        return {}

def get_kit_hierarchy_info(product):
    """Get hierarchy level and dependencies for a product using main optimization system"""
    try:
        # Import from the main optimization system
        from src.config.optimization_config import KIT_LEVELS, KIT_DEPENDENCIES
        from src.config.constants import KitLevel
        
        # Use the same hierarchy system as the optimizer
        if product in KIT_LEVELS:
            level = KIT_LEVELS[product]
            level_name = KitLevel.get_name(level)
            dependencies = KIT_DEPENDENCIES.get(product, [])
            return level_name, dependencies
        else:
            return 'unknown', []
            
    except Exception as e:
        print(f"Error getting hierarchy info for {product}: {e}")
        return 'unknown', []


def display_optimization_results(results):
    """Display comprehensive optimization results with visualizations"""
    st.header("📊 Optimization Results")
    
    # Create tabs for different views
    tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs([
        "📈 Weekly Summary", 
        "📅 Daily Deep Dive", 
        "🏭 Line Schedules", 
        "📦 Kit Production", 
        "💰 Cost Analysis",
        "🔍 Input Data",
        "📋 Demand Validation"
    ])
    
    with tab1:
        display_weekly_summary(results)
    
    with tab2:
        display_daily_deep_dive(results)
    
    with tab3:
        display_line_schedules(results)
    
    with tab4:
        display_kit_production(results)
    
    with tab5:
        display_cost_analysis(results)
    
    with tab6:
        display_input_data_inspection()
    
    with tab7:
        display_demand_validation_tab()

def display_weekly_summary(results):
    """Display weekly summary with key metrics and charts"""
    st.subheader("📈 Weekly Performance Summary")
    
    # Key metrics
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        total_cost = results['objective']
        st.metric("Total Cost", f"€{total_cost:,.2f}")
    
    with col2:
        total_production = sum(results['weekly_production'].values())
        st.metric("Total Production", f"{total_production:,.0f} units")
    
    with col3:
        # Calculate fulfillment rate
        from src.config.optimization_config import get_demand_dictionary
        DEMAND_DICTIONARY = get_demand_dictionary()
        total_demand = sum(DEMAND_DICTIONARY.values())
        fulfillment_rate = (total_production / total_demand * 100) if total_demand > 0 else 0
        st.metric("Fulfillment Rate", f"{fulfillment_rate:.1f}%")
    
    with col4:
        # Calculate cost per unit
        cost_per_unit = total_cost / total_production if total_production > 0 else 0
        st.metric("Cost per Unit", f"€{cost_per_unit:.2f}")
    
    # Production vs Demand Chart
    st.subheader("🎯 Production vs Demand")
    
    from src.config.optimization_config import get_demand_dictionary
    DEMAND_DICTIONARY = get_demand_dictionary()
    prod_demand_data = []
    for product, production in results['weekly_production'].items():
        demand = DEMAND_DICTIONARY.get(product, 0)
        prod_demand_data.append({
            'Product': product,
            'Production': production,
            'Demand': demand,
            'Gap': production - demand
        })
    
    df_prod = pd.DataFrame(prod_demand_data)
    
    if not df_prod.empty:
        # Bar chart comparing production vs demand
        fig = go.Figure()
        fig.add_trace(go.Bar(name='Production', x=df_prod['Product'], y=df_prod['Production'], 
                            marker_color='lightblue'))
        fig.add_trace(go.Bar(name='Demand', x=df_prod['Product'], y=df_prod['Demand'],
                            marker_color='orange'))
        
        fig.update_layout(
            title='Weekly Production vs Demand by Product',
            xaxis_title='Product',
            yaxis_title='Units',
            barmode='group',
            height=400
        )
        st.plotly_chart(fig, use_container_width=True)

def display_daily_deep_dive(results):
    """Display daily breakdown with employee counts by type and shift"""
    st.subheader("📅 Daily Employee Count by Type and Shift")
    
    # Transform schedule data to show employee counts by shift
    employee_counts = []
    
    # Process the production schedule to extract employee usage by shift
    # Only count employees when there's ACTUAL production work
    for row in results['run_schedule']:
        # Skip rows with no actual production activity
        if row['run_hours'] <= 0 and row['units'] <= 0:
            continue
            
        day = f"Day {row['day']}"
        shift_name = {1: 'Regular', 2: 'Evening', 3: 'Overtime'}.get(row['shift'], f"Shift {row['shift']}")
        
        # Get team requirements for this production run
        from src.config.optimization_config import get_team_requirements
        TEAM_REQ_PER_PRODUCT = get_team_requirements()
        
        for emp_type in ['UNICEF Fixed term', 'Humanizer']:
            if row['product'] in TEAM_REQ_PER_PRODUCT.get(emp_type, {}):
                employee_count = TEAM_REQ_PER_PRODUCT[emp_type][row['product']]
                
                # Only add if there are employees needed AND actual production occurs
                if employee_count > 0 and (row['run_hours'] > 0 or row['units'] > 0):
                    employee_counts.append({
                        'Day': day,
                        'Employee Type': emp_type,
                        'Shift': shift_name,
                        'Product': row['product'],
                        'Employee Count': employee_count,
                        'Hours Worked': row['run_hours'],
                        'Total Person-Hours': employee_count * row['run_hours']
                    })
    
    if employee_counts:
        df_employees = pd.DataFrame(employee_counts)
        
        # Aggregate by day, employee type, and shift
        df_summary = df_employees.groupby(['Day', 'Employee Type', 'Shift']).agg({
            'Employee Count': 'sum',
            'Total Person-Hours': 'sum'
        }).reset_index()
        
        # Create stacked bar chart showing employee counts by shift
        fig = px.bar(df_summary, 
                    x='Day', 
                    y='Employee Count', 
                    color='Shift',
                    facet_col='Employee Type',
                    title='Daily Employee Count by Type and Shift',
                    color_discrete_map={
                        'Regular': '#32CD32',    # Green
                        'Overtime': '#FF8C00',   # Orange
                        'Evening': '#4169E1'     # Blue
                    },
                    height=500)
        
        fig.update_layout(
            yaxis_title='Number of Employees',
            showlegend=True
        )
        st.plotly_chart(fig, use_container_width=True)
        
        # Detailed breakdown table
        st.subheader("📋 Employee Allocation Details")
        
        # Show summary by day and shift with capacity context
        st.markdown("**Summary by Day and Shift:**")
        summary_pivot = df_summary.pivot_table(
            values='Employee Count', 
            index=['Day', 'Shift'], 
            columns='Employee Type', 
            aggfunc='sum', 
            fill_value=0
        ).reset_index()
        
        # Add capacity information
        try:
            from src.config.optimization_config import get_max_employee_per_type_on_day
            MAX_EMPLOYEE_PER_TYPE_ON_DAY = get_max_employee_per_type_on_day()  # Dynamic call
            
            # Add capacity columns (removed utilization percentage)
            for emp_type in ['UNICEF Fixed term', 'Humanizer']:
                if emp_type in summary_pivot.columns:
                    capacity_col = f'{emp_type} Capacity'
                    
                    # Extract day number from 'Day X' format
                    summary_pivot['Day_Num'] = summary_pivot['Day'].str.extract(r'(\d+)').astype(int)
                    
                    # Get capacity for each day
                    summary_pivot[capacity_col] = summary_pivot['Day_Num'].apply(
                        lambda day: MAX_EMPLOYEE_PER_TYPE_ON_DAY.get(emp_type, {}).get(day, 0)
                    )
            
            # Drop temporary column
            summary_pivot = summary_pivot.drop('Day_Num', axis=1)
            
        except Exception as e:
            print(f"Could not add capacity information: {e}")
        
        st.dataframe(summary_pivot, use_container_width=True)
        
        # Show detailed breakdown
        st.markdown("**Detailed Production Assignments:**")
        df_detailed = df_employees[['Day', 'Employee Type', 'Shift', 'Product', 'Employee Count', 'Hours Worked']].copy()
        df_detailed = df_detailed.sort_values(['Day', 'Shift', 'Employee Type'])
        st.dataframe(df_detailed, use_container_width=True)
        
    else:
        st.info("📭 No employees scheduled - All production runs have zero hours and zero units")
        
        # Show debug info about filtered rows
        total_schedule_rows = len(results.get('run_schedule', []))
        if total_schedule_rows > 0:
            st.markdown(f"*Note: {total_schedule_rows} schedule entries exist but all have zero production activity*")

def display_line_schedules(results):
    """Display line schedules showing what runs when and with how many workers"""
    st.subheader("🏭 Production Line Schedules")
    
    # Process schedule data
    schedule_data = []
    from src.config.optimization_config import get_team_requirements, get_demand_dictionary, shift_code_to_name, line_code_to_name
    TEAM_REQ_PER_PRODUCT = get_team_requirements()
    DEMAND_DICTIONARY = get_demand_dictionary()
    
    # Get the mapping dictionaries
    shift_names = shift_code_to_name()
    line_names = line_code_to_name()
    
    for row in results['run_schedule']:
        # Get team requirements for this product
        unicef_workers = TEAM_REQ_PER_PRODUCT.get('UNICEF Fixed term', {}).get(row['product'], 0)
        humanizer_workers = TEAM_REQ_PER_PRODUCT.get('Humanizer', {}).get(row['product'], 0)
        total_workers = unicef_workers + humanizer_workers
        
        # Get demand for this product
        kit_total_demand = DEMAND_DICTIONARY.get(row['product'], 0)
        
        # Convert codes to readable names
        line_name = line_names.get(row['line_type_id'], f"Line {row['line_type_id']}")
        shift_name = shift_names.get(row['shift'], f"Shift {row['shift']}")
        
        schedule_data.append({
            'Day': f"Day {row['day']}",
            'Line': f"{line_name} {row['line_idx']}",
            'Shift': shift_name,
            'Product': row['product'],
            'Kit Total Demand': kit_total_demand,
            'Hours': round(row['run_hours'], 2),
            'Units': round(row['units'], 0),
            'UNICEF Workers': unicef_workers,
            'Humanizer Workers': humanizer_workers,
            'Total Workers': total_workers
        })
    
    df_schedule = pd.DataFrame(schedule_data)
    
    if not df_schedule.empty:
        # Timeline view with hierarchy levels
        st.subheader("⏰ Production Line by Line and Day")
        
        # Add hierarchy information to the schedule data
        for row in schedule_data:
            hierarchy_level, dependencies = get_kit_hierarchy_info(row['Product'])
            row['Hierarchy_Level'] = hierarchy_level
            row['Dependencies'] = dependencies
        
        # Recreate dataframe with hierarchy info
        df_schedule = pd.DataFrame(schedule_data)
        
        # Create enhanced timeline chart with hierarchy colors
        fig = create_enhanced_timeline_with_relationships(df_schedule)
        
        if fig:
            st.plotly_chart(fig, use_container_width=True)
        else:
            st.warning("Could not create enhanced timeline chart")
            
        # Add hierarchy legend (updated to match fixed system)
        st.markdown("""
        **🎨 Hierarchy Level Colors:**
        - 🟢 **Prepack**: Level 0 - Dependencies produced first (Lime Green)
        - 🔵 **Subkit**: Level 1 - Intermediate assemblies (Royal Blue)
        - 🟠 **Master**: Level 2 - Final products (Dark Orange)
        """)
        
        # Detailed schedule table (filtered to show only meaningful rows)
        st.subheader("📋 Detailed Production Schedule")
        
        # Filter out rows with zero hours AND zero units (not useful)
        df_schedule_filtered = df_schedule[
            (df_schedule['Hours'] > 0) | (df_schedule['Units'] > 0)
        ].copy()
        
        if df_schedule_filtered.empty:
            st.warning("No production activity scheduled (all hours and units are zero)")
        else:
            # Show count of filtered vs total rows
            filtered_count = len(df_schedule_filtered)
            total_count = len(df_schedule)
            if filtered_count < total_count:
                st.info(f"Showing {filtered_count} active production entries (filtered out {total_count - filtered_count} zero-activity rows)")
            
            st.dataframe(df_schedule_filtered, use_container_width=True)

def create_enhanced_timeline_with_relationships(df_schedule):
    """Create enhanced timeline chart with hierarchy colors and relationship lines"""
    if df_schedule.empty:
        return None
    
    # Define hierarchy colors (using proper hierarchy levels with visible colors)
    hierarchy_colors = {
        'prepack': '#32CD32',         # Lime Green - Level 0 (dependencies)
        'subkit': '#4169E1',          # Royal Blue - Level 1 (intermediate)
        'master': '#FF8C00',          # Dark Orange - Level 2 (final products)
        'unknown': '#8B0000'          # Dark Red - fallback (should not appear now)
    }
    
    # Create the base chart using hierarchy levels for colors
    fig = px.bar(df_schedule, 
                x='Hours', 
                y='Line', 
                color='Hierarchy_Level',
                facet_col='Day', 
                orientation='h',
                title='Production Schedule by Line and Day (Colored by Hierarchy Level)',
                height=500,
                color_discrete_map=hierarchy_colors,
                hover_data=['Product', 'Units', 'Total Workers'])
    
    # Improve visibility with stronger borders and opacity
    fig.update_traces(
        marker_line_color='black',  # Add black borders
        marker_line_width=1.5,      # Make borders visible
        opacity=0.8                 # Slightly transparent but not too much
    )
    
    # Improve layout with better text visibility
    fig.update_layout(
        showlegend=True,
        plot_bgcolor='white',       # White background
        paper_bgcolor='white',
        font=dict(size=12, color='#000000', family='Arial, sans-serif'),  # Black text, clear font
        title_font=dict(color='#000000', size=14, family='Arial Bold'),      # Bold black title
        legend_title_text='Hierarchy Level',
        legend=dict(
            font=dict(size=11, color='#000000'),  # Black legend text
            bgcolor='rgba(255,255,255,0.8)',      # Semi-transparent white background
            bordercolor='#000000',                # Black border around legend
            borderwidth=1
        )
    )
    
    # Improve axes with dark, bold text
    fig.update_xaxes(
        showgrid=True, 
        gridwidth=0.5, 
        gridcolor='lightgray',
        title_font=dict(size=12, color='#000000'),
        tickfont=dict(color='#000000', size=10)
    )
    fig.update_yaxes(
        showgrid=True, 
        gridwidth=0.5, 
        gridcolor='lightgray',
        title_font=dict(size=12, color='#000000'),
        tickfont=dict(color='#000000', size=10)
    )
    
    # Add dependency arrows/lines between related kits
    try:
        fig = add_dependency_connections(fig, df_schedule)
    except Exception as e:
        print(f"Could not add dependency connections: {e}")
    
    return fig

def add_dependency_connections(fig, df_schedule):
    """Add arrows or lines showing dependencies between kits"""
    # Create a mapping of product to its position in the chart
    product_positions = {}
    
    for _, row in df_schedule.iterrows():
        product = row['Product']
        day = row['Day']
        line = row['Line']
        
        # Store position info
        product_positions[product] = {
            'day': day,
            'line': line,
            'dependencies': row.get('Dependencies', [])
        }
    
    # Count relationships for display
    relationship_count = 0
    dependency_details = []
    
    for product, pos_info in product_positions.items():
        dependencies = pos_info['dependencies']
        
        for dep in dependencies:
            if dep in product_positions:
                # Both product and dependency are in production
                dep_pos = product_positions[dep]
                relationship_count += 1
                
                dependency_details.append({
                    'product': product,
                    'dependency': dep,
                    'product_day': pos_info['day'],
                    'dependency_day': dep_pos['day'],
                    'timing': 'correct' if dep_pos['day'] <= pos_info['day'] else 'violation'
                })
    
    # Add annotations about relationships
    if relationship_count > 0:
        violations = len([d for d in dependency_details if d['timing'] == 'violation'])
        
        fig.add_annotation(
            text=f"🔗 {relationship_count} dependencies | {'⚠️ ' + str(violations) + ' violations' if violations > 0 else '✅ All correct'}",
            xref="paper", yref="paper",
            x=0.02, y=0.98,
            showarrow=False,
            font=dict(size=10, color="purple"),
            bgcolor="rgba(255,255,255,0.8)",
            bordercolor="purple",
            borderwidth=1
        )
        
        # Add dependency info box
        if dependency_details:
            dependency_text = "\\n".join([
                f"• {d['dependency']} → {d['product']} ({'✅' if d['timing'] == 'correct' else '⚠️'})"
                for d in dependency_details[:5]  # Show first 5
            ])
            
            if len(dependency_details) > 5:
                dependency_text += f"\\n... and {len(dependency_details) - 5} more"
                
            fig.add_annotation(
                text=dependency_text,
                xref="paper", yref="paper",
                x=0.02, y=0.02,
                showarrow=False,
                font=dict(size=8, color="navy"),
                bgcolor="rgba(240,248,255,0.9)",
                bordercolor="navy",
                borderwidth=1,
                align="left"
            )
    
    return fig

def display_kit_production(results):
    """Display kit production details"""
    st.subheader("📦 Kit Production Analysis")
    
    # Weekly production summary
    production_data = []
    from src.config.optimization_config import get_demand_dictionary
    DEMAND_DICTIONARY = get_demand_dictionary()
    
    for product, production in results['weekly_production'].items():
        demand = DEMAND_DICTIONARY.get(product, 0)
        production_data.append({
            'Product': product,
            'Production': production,
            'Demand': demand,
            'Fulfillment %': (production / demand * 100) if demand > 0 else 0,
            'Over/Under': production - demand
        })
    
    df_production = pd.DataFrame(production_data)
    
    if not df_production.empty:
        # Fulfillment rate chart
        fig = px.bar(df_production, x='Product', y='Fulfillment %',
                    title='Kit Fulfillment Rate by Product',
                    color='Fulfillment %',
                    color_continuous_scale=['red', 'yellow', 'green'],
                    height=400)
        fig.add_hline(y=100, line_dash="dash", line_color="black", 
                     annotation_text="100% Target")
        st.plotly_chart(fig, use_container_width=True)
        
        # Production summary table
        st.subheader("📋 Kit Production Summary")
        st.dataframe(df_production, use_container_width=True)

def display_cost_analysis(results):
    """Display cost breakdown and analysis"""
    st.subheader("💰 Cost Breakdown Analysis")
    
    # Calculate cost breakdown
    from src.config.optimization_config import get_cost_list_per_emp_shift, get_team_requirements, shift_code_to_name, line_code_to_name
    COST_LIST_PER_EMP_SHIFT = get_cost_list_per_emp_shift()  # Dynamic call
    TEAM_REQ_PER_PRODUCT = get_team_requirements()
    
    # Get the mapping dictionaries
    shift_names = shift_code_to_name()
    line_names = line_code_to_name()
    
    cost_data = []
    total_cost_by_type = {}
    
    for row in results['run_schedule']:
        product = row['product']
        hours = row['run_hours']
        shift = row['shift']
        shift_name = shift_names.get(shift, f"Shift {shift}")
        line_name = line_names.get(row['line_type_id'], f"Line {row['line_type_id']}")
        
        # Calculate costs for this production run (accounting for payment mode)
        from src.config.optimization_config import get_payment_mode_config, get_max_hour_per_shift_per_person
        PAYMENT_MODE_CONFIG = get_payment_mode_config()  # Dynamic call
        MAX_HOUR_PER_SHIFT_PER_PERSON = get_max_hour_per_shift_per_person()  # Dynamic call
        
        for emp_type in ['UNICEF Fixed term', 'Humanizer']:
            workers_needed = TEAM_REQ_PER_PRODUCT.get(emp_type, {}).get(product, 0)
            hourly_rate = COST_LIST_PER_EMP_SHIFT.get(emp_type, {}).get(shift, 0)
            
            # Check payment mode for this shift
            payment_mode = PAYMENT_MODE_CONFIG.get(shift, "partial")
            
            if payment_mode == "bulk" and hours > 0:
                # Bulk payment: pay for full shift hours if workers are active
                shift_hours = MAX_HOUR_PER_SHIFT_PER_PERSON.get(shift, hours)
                cost = workers_needed * shift_hours * hourly_rate
                display_hours = shift_hours  # Show full shift hours in display
            else:
                # Partial payment: pay for actual hours worked
                cost = workers_needed * hours * hourly_rate
                display_hours = hours  # Show actual hours in display
            
            if emp_type not in total_cost_by_type:
                total_cost_by_type[emp_type] = 0
            total_cost_by_type[emp_type] += cost
            
            if cost > 0:
                # Add payment mode indicator to shift name for clarity
                payment_indicator = f" ({payment_mode})" if payment_mode == "bulk" else ""
                cost_data.append({
                    'Employee Type': emp_type,
                    'Day': f"Day {row['day']}",
                    'Shift': f"{shift_name}{payment_indicator}",
                    'Line': f"{line_name} {row['line_idx']}",
                    'Product': product,
                    'Actual Hours': round(hours, 2),
                    'Paid Hours': round(display_hours, 2),
                    'Workers': workers_needed,
                    'Hourly Rate': f"€{hourly_rate:.2f}",
                    'Cost': round(cost, 2)
                })
    
    # Note: Idle employee tracking removed - we only track employees actually working on production
    
    # Total cost metrics
    total_cost = results['objective']
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        st.metric("Total Cost", f"€{total_cost:,.2f}")
    
    with col2:
        unicef_cost = total_cost_by_type.get('UNICEF Fixed term', 0)
        st.metric("UNICEF Cost", f"€{unicef_cost:,.2f}")
    
    with col3:
        humanizer_cost = total_cost_by_type.get('Humanizer', 0)
        st.metric("Humanizer Cost", f"€{humanizer_cost:,.2f}")
    
    with col4:
        avg_daily_cost = total_cost / len(set(row['day'] for row in results['run_schedule'])) if results['run_schedule'] else 0
        st.metric("Avg Daily Cost", f"€{avg_daily_cost:,.2f}")
    
    # Cost breakdown pie chart
    if total_cost_by_type:
        fig = px.pie(values=list(total_cost_by_type.values()), 
                    names=list(total_cost_by_type.keys()),
                    title='Cost Distribution by Employee Type')
        st.plotly_chart(fig, use_container_width=True)
    
    # Detailed cost table
    if cost_data:
        df_costs = pd.DataFrame(cost_data)
        
        # Add total row
        total_cost = df_costs['Cost'].sum()
        total_paid_hours = df_costs['Paid Hours'].sum() if 'Paid Hours' in df_costs.columns else df_costs['Actual Hours'].sum()
        total_row = pd.DataFrame([{
            'Employee Type': '**TOTAL**',
            'Day': '-',
            'Shift': '-',
            'Line': '-',
            'Product': '-', 
            'Actual Hours': df_costs['Actual Hours'].sum(),
            'Paid Hours': total_paid_hours,
            'Workers': df_costs['Workers'].sum(),
            'Hourly Rate': '-',
            'Cost': total_cost
        }])
        
        # Combine original data with total row
        df_costs_with_total = pd.concat([df_costs, total_row], ignore_index=True)
        
        st.subheader("📋 Detailed Cost Breakdown")
        st.dataframe(df_costs_with_total, use_container_width=True)


def display_input_data_inspection():
    """
    Display comprehensive input data inspection showing what was fed into the optimizer
    """
    st.subheader("🔍 Input Data Inspection")
    st.markdown("This section shows all the input data and parameters that were fed into the optimization model.")
    
    # Import the optimization config to get current values
    try:
        from src.config import optimization_config
        from src.config.constants import ShiftType, LineType, KitLevel
        
        # Create expandable sections for different data categories
        with st.expander("📅 **Schedule & Time Parameters**", expanded=True):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Date Range:**")
                date_span = optimization_config.get_date_span()
                st.write(f"• Planning Period: {len(date_span)} days")
                st.write(f"• Date Span: {list(date_span)}")
                
                st.write("**Shift Configuration:**")
                shift_list = optimization_config.get_shift_list()
                for shift in shift_list:
                    shift_name = ShiftType.get_name(shift)
                    st.write(f"• {shift_name} (ID: {shift})")
            
            with col2:
                st.write("**Work Hours Configuration:**")
                max_hours_shift = optimization_config.get_max_hour_per_shift_per_person()
                for shift_id, hours in max_hours_shift.items():
                    shift_name = ShiftType.get_name(shift_id)
                    st.write(f"• {shift_name}: {hours} hours/shift")
                
                from src.config.optimization_config import MAX_HOUR_PER_PERSON_PER_DAY
                max_daily_hours = MAX_HOUR_PER_PERSON_PER_DAY
                st.write(f"• Maximum daily hours per person: {max_daily_hours}")
        
        with st.expander("👥 **Workforce Parameters**", expanded=False):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Employee Types:**")
                emp_types = optimization_config.get_employee_type_list()
                for emp_type in emp_types:
                    st.write(f"• {emp_type}")
                
                st.write("**Daily Workforce Capacity:**")
                max_emp_per_day = optimization_config.get_max_employee_per_type_on_day()
                for emp_type, daily_caps in max_emp_per_day.items():
                    st.write(f"**{emp_type}:**")
                    for day, count in daily_caps.items():
                        st.write(f"  - Day {day}: {count} employees")
            
            with col2:
                st.write("**Team Requirements per Product:**")
                team_req = optimization_config.get_team_requirements()
                st.write("*Sample products:*")
                # Show first few products as examples
                sample_products = list(team_req.get('UNICEF Fixed term', {}).keys())[:5]
                for product in sample_products:
                    st.write(f"**{product}:**")
                    for emp_type in emp_types:
                        req = team_req.get(emp_type, {}).get(product, 0)
                        if req > 0:
                            st.write(f"  - {emp_type}: {req}")
                
                if len(team_req.get('UNICEF Fixed term', {})) > 5:
                    remaining = len(team_req.get('UNICEF Fixed term', {})) - 5
                    st.write(f"... and {remaining} more products")
        
        with st.expander("🏭 **Production & Line Parameters**", expanded=False):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Line Configuration:**")
                line_list = optimization_config.get_line_list()
                line_cnt = optimization_config.get_line_cnt_per_type()
                
                for line_type in line_list:
                    line_name = LineType.get_name(line_type)
                    count = line_cnt.get(line_type, 0)
                    st.write(f"• {line_name} (ID: {line_type}): {count} lines")
                
                st.write("**Maximum Workers per Line:**")
                max_workers = optimization_config.get_max_parallel_workers()
                for line_type, max_count in max_workers.items():
                    line_name = LineType.get_name(line_type)
                    st.write(f"• {line_name}: {max_count} workers max")
            
            with col2:
                st.write("**Product-Line Matching:**")
                from src.config.optimization_config import KIT_LINE_MATCH_DICT
                kit_line_match = KIT_LINE_MATCH_DICT
                st.write("*Sample mappings:*")
                sample_items = list(kit_line_match.items())[:10]
                for product, line_type in sample_items:
                    line_name = LineType.get_name(line_type)
                    st.write(f"• {product}: {line_name}")
                
                if len(kit_line_match) > 10:
                    remaining = len(kit_line_match) - 10
                    st.write(f"... and {remaining} more product mappings")
        
        with st.expander("📦 **Product & Demand Data**", expanded=False):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Product List:**")
                product_list = optimization_config.get_product_list()
                st.write(f"• Total products: {len(product_list)}")
                st.write("*Sample products:*")
                for product in product_list[:10]:
                    st.write(f"  - {product}")
                if len(product_list) > 10:
                    st.write(f"  ... and {len(product_list) - 10} more")
                
                st.write("**Production Speed (units/hour):**")
                from src.preprocess import extract
                speed_data = extract.read_package_speed_data()
                st.write("*Sample speeds:*")
                sample_speeds = list(speed_data.items())[:5]
                for product, speed in sample_speeds:
                    st.write(f"• {product}: {speed:.1f} units/hour")
                if len(speed_data) > 5:
                    remaining = len(speed_data) - 5
                    st.write(f"... and {remaining} more products")
            
            with col2:
                st.write("**Weekly Demand:**")
                demand_dict = optimization_config.get_demand_dictionary()
                st.write(f"• Total products with demand: {len(demand_dict)}")
                
                # Calculate total demand
                total_demand = sum(demand_dict.values())
                st.write(f"• Total weekly demand: {total_demand:,.0f} units")
                
                st.write("*Sample demands:*")
                # Sort by demand to show highest first
                sorted_demands = sorted(demand_dict.items(), key=lambda x: x[1], reverse=True)[:10]
                for product, demand in sorted_demands:
                    st.write(f"• {product}: {demand:,.0f} units")
                
                if len(demand_dict) > 10:
                    remaining = len(demand_dict) - 10
                    st.write(f"... and {remaining} more products")
        
        with st.expander("🏗️ **Kit Hierarchy & Dependencies**", expanded=False):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Kit Levels:**")
                kit_levels = optimization_config.get_kit_levels()
                
                # Count by level
                level_counts = {}
                for kit, level in kit_levels.items():
                    level_name = KitLevel.get_name(level)
                    if level_name not in level_counts:
                        level_counts[level_name] = 0
                    level_counts[level_name] += 1
                
                for level_name, count in level_counts.items():
                    st.write(f"• {level_name}: {count} kits")
                
                st.write("*Sample kit levels:*")
                sample_levels = list(kit_levels.items())[:10]
                for kit, level in sample_levels:
                    level_name = KitLevel.get_name(level)
                    st.write(f"  - {kit}: {level_name}")
                
                if len(kit_levels) > 10:
                    remaining = len(kit_levels) - 10
                    st.write(f"  ... and {remaining} more kits")
            
            with col2:
                st.write("**Dependencies:**")
                kit_deps = optimization_config.get_kit_dependencies()
                
                # Count dependencies
                total_deps = sum(len(deps) for deps in kit_deps.values())
                kits_with_deps = len([k for k, deps in kit_deps.items() if deps])
                
                st.write(f"• Total dependency relationships: {total_deps}")
                st.write(f"• Kits with dependencies: {kits_with_deps}")
                
                st.write("*Sample dependencies:*")
                sample_deps = [(k, deps) for k, deps in kit_deps.items() if deps][:5]
                for kit, deps in sample_deps:
                    st.write(f"• {kit}:")
                    for dep in deps[:3]:  # Show max 3 deps per kit
                        st.write(f"  - depends on: {dep}")
                    if len(deps) > 3:
                        st.write(f"  - ... and {len(deps) - 3} more")
                
                if len(sample_deps) > 5:
                    remaining = len([k for k, deps in kit_deps.items() if deps]) - 5
                    st.write(f"... and {remaining} more kits with dependencies")
        
        with st.expander("💰 **Cost & Payment Configuration**", expanded=False):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Hourly Cost Rates:**")
                cost_rates = optimization_config.get_cost_list_per_emp_shift()
                
                for emp_type, shift_costs in cost_rates.items():
                    st.write(f"**{emp_type}:**")
                    for shift_id, cost in shift_costs.items():
                        shift_name = ShiftType.get_name(shift_id)
                        st.write(f"  - {shift_name}: €{cost:.2f}/hour")
            
            with col2:
                st.write("**Payment Mode Configuration:**")
                payment_config = optimization_config.get_payment_mode_config()
                
                payment_descriptions = {
                    'bulk': 'Full shift payment (even for partial hours)',
                    'partial': 'Pay only for actual hours worked'
                }
                
                for shift_id, mode in payment_config.items():
                    shift_name = ShiftType.get_name(shift_id)
                    description = payment_descriptions.get(mode, mode)
                    st.write(f"• **{shift_name}:** {mode.title()}")
                    st.caption(f"  {description}")
        
        with st.expander("⚙️ **Additional Configuration**", expanded=False):
            col1, col2 = st.columns(2)
            
            with col1:
                st.write("**Schedule Mode:**")
                schedule_mode = "weekly"  # Fixed to weekly only
                st.write(f"• Planning mode: {schedule_mode}")
                
                st.write("**Evening Shift Mode:**")
                from src.config.optimization_config import EVENING_SHIFT_MODE
                evening_mode = EVENING_SHIFT_MODE
                evening_threshold = optimization_config.get_evening_shift_demand_threshold()
                st.write(f"• Mode: {evening_mode}")
                st.write(f"• Activation threshold: {evening_threshold:.1%}")
            
            with col2:
                st.write("**Fixed Staffing:**")
                fixed_min_unicef = optimization_config.get_fixed_min_unicef_per_day()
                st.write(f"• Minimum UNICEF staff per day: {fixed_min_unicef}")
                
                st.write("**Data Sources:**")
                st.write("• Kit hierarchy: kit_hierarchy.json")
                st.write("• Production orders: CSV files")
                st.write("• Personnel data: WH_Workforce CSV")
                st.write("• Speed data: Kits_Calculation CSV")
        
    except Exception as e:
        st.error(f"❌ Error loading input data inspection: {str(e)}")
        st.info("💡 This may happen if the optimization configuration is not properly loaded. Please check the Settings page first.")
    
    # Add refresh button
    st.markdown("---")
    if st.button("🔄 Refresh Input Data", help="Reload the current configuration data"):
        st.rerun()


def display_demand_validation_tab():
    """
    Display demand validation in the optimization results tab
    """
    try:
        from src.demand_validation_viz import display_demand_validation
        display_demand_validation()
    except ImportError as e:
        st.error(f"❌ Error loading demand validation module: {str(e)}")
        st.info("💡 Please ensure the demand validation module is properly installed.")
    except Exception as e:
        st.error(f"❌ Error in demand validation: {str(e)}")
        st.info("💡 Please check the data files and configuration.")