app.py

"""
🚀 ARF ULTIMATE INVESTOR DEMO v3.5.0 - FULLY WORKING VERSION
All buttons working, all visualizations rendering, no errors
"""

import datetime
import json
import logging
import uuid
import random
from typing import Dict, Any, List
import gradio as gr
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
from plotly.subplots import make_subplots

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# ===========================================
# INCIDENT DATA STORAGE
# ===========================================

INCIDENT_SCENARIOS = {
    "Cache Miss Storm": {
        "metrics": {
            "Cache Hit Rate": "18.5% (Critical)",
            "Database Load": "92% (Overloaded)",
            "Response Time": "1850ms (Slow)",
            "Affected Users": "45,000"
        },
        "impact": {
            "Revenue Loss": "$8,500/hour",
            "Page Load Time": "+300%",
            "Users Impacted": "45,000"
        },
        "oss_analysis": {
            "status": "✅ Analysis Complete",
            "recommendations": [
                "Increase Redis cache memory allocation",
                "Implement cache warming strategy",
                "Optimize key patterns (TTL adjustments)",
                "Add circuit breaker for database fallback"
            ],
            "estimated_time": "60+ minutes",
            "engineers_needed": "2-3 SREs",
            "manual_effort": "High"
        },
        "enterprise_results": {
            "actions_completed": [
                "✅ Auto-scaled Redis: 4GB → 8GB",
                "✅ Deployed cache warming service",
                "✅ Optimized 12 key patterns",
                "✅ Implemented circuit breaker"
            ],
            "metrics_improvement": {
                "Cache Hit Rate": "18.5% → 72%",
                "Response Time": "1850ms → 450ms",
                "Database Load": "92% → 45%"
            },
            "business_impact": {
                "Recovery Time": "60 min → 12 min",
                "Cost Saved": "$7,200",
                "Users Impacted": "45,000 → 0"
            }
        }
    },
    "Database Connection Pool Exhaustion": {
        "metrics": {
            "Active Connections": "98/100 (Critical)",
            "API Latency": "2450ms",
            "Error Rate": "15.2%",
            "Queue Depth": "1250"
        },
        "impact": {
            "Revenue Loss": "$4,200/hour",
            "Affected Services": "API Gateway, User Service",
            "SLA Violation": "Yes"
        },
        "oss_analysis": {
            "status": "✅ Analysis Complete",
            "recommendations": [
                "Increase connection pool from 100 to 200",
                "Add connection timeout (30s)",
                "Implement leak detection",
                "Add connection health checks"
            ],
            "estimated_time": "45+ minutes",
            "engineers_needed": "1-2 DBAs",
            "manual_effort": "Medium-High"
        }
    },
    "Memory Leak in Production": {
        "metrics": {
            "Memory Usage": "96% (Critical)",
            "GC Pause Time": "4500ms",
            "Error Rate": "28.5%",
            "Restart Frequency": "12/hour"
        },
        "impact": {
            "Revenue Loss": "$5,500/hour",
            "Session Loss": "8,500 users",
            "Customer Impact": "High"
        }
    }
}

# ===========================================
# VISUALIZATION ENGINE
# ===========================================

class VisualizationEngine:
    """Working visualization engine with no errors"""
    
    @staticmethod
    def create_timeline_visualization():
        """Create interactive incident timeline"""
        try:
            # Create sample timeline data
            now = datetime.datetime.now()
            events = [
                {"time": now - datetime.timedelta(minutes=25), "event": "📉 Cache Hit Rate drops to 18.5%", "type": "problem"},
                {"time": now - datetime.timedelta(minutes=22), "event": "⚠️ Alert: Database load hits 92%", "type": "alert"},
                {"time": now - datetime.timedelta(minutes=20), "event": "🤖 ARF detects pattern", "type": "detection"},
                {"time": now - datetime.timedelta(minutes=18), "event": "🧠 Analysis: Cache Miss Storm identified", "type": "analysis"},
                {"time": now - datetime.timedelta(minutes=15), "event": "⚡ Enterprise healing executed", "type": "action"},
                {"time": now - datetime.timedelta(minutes=12), "event": "✅ Cache Hit Rate recovers to 72%", "type": "recovery"},
                {"time": now - datetime.timedelta(minutes=10), "event": "📊 System stabilized", "type": "stable"}
            ]
            
            df = pd.DataFrame(events)
            df['time_str'] = df['time'].dt.strftime('%H:%M:%S')
            
            # Color mapping
            color_map = {
                "problem": "red",
                "alert": "orange", 
                "detection": "blue",
                "analysis": "purple",
                "action": "green",
                "recovery": "lightgreen",
                "stable": "darkgreen"
            }
            
            fig = go.Figure()
            
            for event_type in df['type'].unique():
                type_df = df[df['type'] == event_type]
                fig.add_trace(go.Scatter(
                    x=type_df['time'],
                    y=[event_type] * len(type_df),
                    mode='markers+text',
                    name=event_type.capitalize(),
                    marker=dict(
                        size=15,
                        color=color_map.get(event_type, 'gray'),
                        symbol='circle' if event_type in ['problem', 'alert'] else 'diamond',
                        line=dict(width=2, color='white')
                    ),
                    text=type_df['event'],
                    textposition="top center",
                    hoverinfo='text'
                ))
            
            fig.update_layout(
                title="<b>Incident Timeline - Cache Miss Storm Resolution</b>",
                xaxis_title="Time →",
                yaxis_title="Event Type",
                height=500,
                showlegend=True,
                paper_bgcolor='rgba(0,0,0,0)',
                plot_bgcolor='rgba(0,0,0,0)',
                hovermode='closest',
                xaxis=dict(
                    tickformat='%H:%M',
                    gridcolor='rgba(200,200,200,0.2)'
                ),
                yaxis=dict(
                    gridcolor='rgba(200,200,200,0.1)'
                )
            )
            
            return fig
        except Exception as e:
            logger.error(f"Error creating timeline: {e}")
            return VisualizationEngine._create_error_figure("Timeline")
    
    @staticmethod
    def create_business_dashboard():
        """Create business health dashboard"""
        try:
            fig = make_subplots(
                rows=2, cols=2,
                subplot_titles=('Annual Cost Impact', 'Team Time Reclaimed',
                              'MTTR Comparison', 'ROI Analysis'),
                vertical_spacing=0.15,
                horizontal_spacing=0.15
            )
            
            # 1. Cost Impact
            categories = ['Without ARF', 'With ARF Enterprise', 'Net Savings']
            values = [2960000, 1000000, 1960000]
            
            fig.add_trace(
                go.Bar(
                    x=categories,
                    y=values,
                    marker_color=['#FF6B6B', '#4ECDC4', '#45B7D1'],
                    text=[f'${v/1000000:.1f}M' for v in values],
                    textposition='auto',
                    name='Cost Impact'
                ),
                row=1, col=1
            )
            
            # 2. Time Allocation
            labels = ['Firefighting', 'Innovation', 'Maintenance']
            before = [60, 20, 20]
            after = [10, 60, 30]
            
            fig.add_trace(
                go.Bar(
                    x=labels,
                    y=before,
                    name='Before ARF',
                    marker_color='#FF6B6B'
                ),
                row=1, col=2
            )
            
            fig.add_trace(
                go.Bar(
                    x=labels,
                    y=after,
                    name='After ARF Enterprise',
                    marker_color='#4ECDC4'
                ),
                row=1, col=2
            )
            
            # 3. MTTR Comparison
            mttr_categories = ['Traditional', 'ARF OSS', 'ARF Enterprise']
            mttr_values = [45, 25, 8]
            
            fig.add_trace(
                go.Bar(
                    x=mttr_categories,
                    y=mttr_values,
                    marker_color=['#FF6B6B', '#FFE66D', '#4ECDC4'],
                    text=[f'{v} min' for v in mttr_values],
                    textposition='auto',
                    name='MTTR'
                ),
                row=2, col=1
            )
            
            # 4. ROI Gauge
            fig.add_trace(
                go.Indicator(
                    mode="gauge+number+delta",
                    value=5.2,
                    title={'text': "ROI Multiplier"},
                    delta={'reference': 1.0, 'increasing': {'color': "green"}},
                    gauge={
                        'axis': {'range': [0, 10], 'tickwidth': 1},
                        'bar': {'color': "#4ECDC4"},
                        'steps': [
                            {'range': [0, 2], 'color': "lightgray"},
                            {'range': [2, 4], 'color': "gray"},
                            {'range': [4, 6], 'color': "lightgreen"},
                            {'range': [6, 10], 'color': "green"}
                        ],
                        'threshold': {
                            'line': {'color': "red", 'width': 4},
                            'thickness': 0.75,
                            'value': 5.2
                        }
                    }
                ),
                row=2, col=2
            )
            
            fig.update_layout(
                height=700,
                showlegend=True,
                paper_bgcolor='rgba(0,0,0,0)',
                plot_bgcolor='rgba(0,0,0,0)',
                title_text="<b>Executive Business Health Dashboard</b>",
                barmode='group'
            )
            
            # Update axes
            fig.update_xaxes(title_text="Cost Categories", row=1, col=1)
            fig.update_yaxes(title_text="Annual Cost ($)", row=1, col=1)
            
            fig.update_xaxes(title_text="Activity Type", row=1, col=2)
            fig.update_yaxes(title_text="Percentage (%)", row=1, col=2)
            
            fig.update_xaxes(title_text="Solution Type", row=2, col=1)
            fig.update_yaxes(title_text="Minutes to Resolve", row=2, col=1)
            
            return fig
        except Exception as e:
            logger.error(f"Error creating dashboard: {e}")
            return VisualizationEngine._create_error_figure("Dashboard")
    
    @staticmethod
    def create_metrics_stream():
        """Create metrics stream visualization"""
        try:
            # Generate time series data
            times = pd.date_range(end=datetime.datetime.now(), periods=50, freq='1min')
            
            fig = go.Figure()
            
            # Cache Hit Rate
            fig.add_trace(go.Scatter(
                x=times,
                y=[18.5 + i * 1.2 for i in range(50)],  # Recovery trend
                mode='lines',
                name='Cache Hit Rate',
                line=dict(color='blue', width=2),
                yaxis='y1'
            ))
            
            # Database Load
            fig.add_trace(go.Scatter(
                x=times,
                y=[92 - i * 0.94 for i in range(50)],  # Decreasing trend
                mode='lines',
                name='Database Load',
                line=dict(color='red', width=2),
                yaxis='y2'
            ))
            
            fig.update_layout(
                title="<b>Real-time Metrics Recovery</b>",
                xaxis_title="Time",
                height=500,
                paper_bgcolor='rgba(0,0,0,0)',
                plot_bgcolor='rgba(0,0,0,0)',
                yaxis=dict(
                    title="Cache Hit Rate (%)",
                    side='left',
                    range=[0, 100]
                ),
                yaxis2=dict(
                    title="Database Load (%)",
                    side='right',
                    overlaying='y',
                    range=[0, 100]
                ),
                legend=dict(
                    yanchor="top",
                    y=0.99,
                    xanchor="left",
                    x=0.01
                )
            )
            
            return fig
        except Exception as e:
            logger.error(f"Error creating stream: {e}")
            return VisualizationEngine._create_error_figure("Metrics Stream")
    
    @staticmethod
    def create_performance_radar():
        """Create performance radar chart"""
        try:
            categories = ['Reliability', 'Speed', 'Cost Savings', 'Auto-Heal Rate', 'ROI']
            values = [95, 88, 92, 82, 85]
            
            fig = go.Figure(data=go.Scatterpolar(
                r=values + [values[0]],
                theta=categories + [categories[0]],
                fill='toself',
                fillcolor='rgba(52, 152, 219, 0.3)',
                line=dict(color='rgba(52, 152, 219, 0.8)', width=2),
                name="ARF Enterprise"
            ))
            
            fig.update_layout(
                polar=dict(
                    radialaxis=dict(
                        visible=True,
                        range=[0, 100],
                        gridcolor='rgba(200, 200, 200, 0.3)'
                    )),
                showlegend=True,
                paper_bgcolor='rgba(0,0,0,0)',
                plot_bgcolor='rgba(0,0,0,0)',
                height=500,
                title="<b>Performance Radar - ARF Enterprise</b>"
            )
            
            return fig
        except Exception as e:
            logger.error(f"Error creating radar: {e}")
            return VisualizationEngine._create_error_figure("Radar Chart")
    
    @staticmethod
    def create_execution_history():
        """Create execution history chart"""
        try:
            executions = [
                {"time": "22:14", "scenario": "Cache Miss Storm", "savings": 7200},
                {"time": "21:58", "scenario": "Memory Leak", "savings": 5200},
                {"time": "21:45", "scenario": "API Rate Limit", "savings": 2800},
                {"time": "21:30", "scenario": "DB Pool Exhaustion", "savings": 3800},
                {"time": "21:15", "scenario": "Cache Miss Storm", "savings": 7200},
                {"time": "21:00", "scenario": "Cascading Failure", "savings": 12500}
            ]
            
            df = pd.DataFrame(executions)
            
            fig = go.Figure(data=[
                go.Bar(
                    x=df['scenario'],
                    y=df['savings'],
                    marker_color='#4ECDC4',
                    text=[f'${s:,.0f}' for s in df['savings']],
                    textposition='outside',
                    name='Cost Saved'
                )
            ])
            
            fig.update_layout(
                title="<b>Execution History - Cost Savings</b>",
                xaxis_title="Incident Scenario",
                yaxis_title="Cost Saved ($)",
                height=500,
                paper_bgcolor='rgba(0,0,0,0)',
                plot_bgcolor='rgba(0,0,0,0)',
                showlegend=False
            )
            
            return fig
        except Exception as e:
            logger.error(f"Error creating history chart: {e}")
            return VisualizationEngine._create_error_figure("History Chart")
    
    @staticmethod
    def _create_error_figure(chart_type: str):
        """Create error figure with message"""
        fig = go.Figure()
        fig.update_layout(
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            height=400,
            annotations=[dict(
                text=f"{chart_type} visualization<br>will appear here",
                xref="paper", yref="paper",
                x=0.5, y=0.5,
                showarrow=False,
                font=dict(size=16, color="gray")
            )]
        )
        return fig

# ===========================================
# MAIN APPLICATION
# ===========================================

def run_oss_analysis(scenario_name: str):
    """Run OSS analysis - NOW WORKING"""
    try:
        scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
        analysis = scenario.get("oss_analysis", {})
        
        if not analysis:
            analysis = {
                "status": "✅ Analysis Complete",
                "recommendations": [
                    "Increase resource allocation",
                    "Implement monitoring",
                    "Add circuit breakers",
                    "Optimize configuration"
                ],
                "estimated_time": "45-60 minutes",
                "engineers_needed": "2-3",
                "manual_effort": "Required"
            }
        
        return analysis
    except Exception as e:
        logger.error(f"OSS analysis error: {e}")
        return {
            "status": "❌ Analysis Failed",
            "error": "Please try again",
            "recommendations": ["Check system configuration"]
        }

def execute_enterprise_healing(scenario_name: str, approval_required: bool):
    """Execute enterprise healing - NOW WORKING"""
    try:
        scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
        results = scenario.get("enterprise_results", {})
        
        if not results:
            results = {
                "status": "✅ Auto-Executed" if not approval_required else "✅ Approved and Executed",
                "actions_completed": [
                    "✅ Auto-scaled resources",
                    "✅ Implemented optimization",
                    "✅ Deployed monitoring",
                    "✅ Validated recovery"
                ],
                "cost_saved": f"${random.randint(2000, 8000):,}",
                "time_savings": f"{random.randint(30, 60)} min → {random.randint(5, 15)} min"
            }
        
        # Add approval info
        if approval_required:
            approval_html = f"""
            <div style='padding: 15px; background: #f8f9fa; border-radius: 8px; border-left: 4px solid #007bff; margin: 10px 0;'>
            <h4 style='margin: 0 0 10px 0;'>🛡️ Approval Required</h4>
            <p><b>Action:</b> Scale cache for {scenario_name}</p>
            <p><b>Risk:</b> Low (auto-rollback available)</p>
            <p><b>Status:</b> ✅ <span style='color: green;'>Approved & Executed</span></p>
            </div>
            """
        else:
            approval_html = f"""
            <div style='padding: 15px; background: #e8f5e8; border-radius: 8px; border-left: 4px solid #28a745; margin: 10px 0;'>
            <h4 style='margin: 0 0 10px 0;'>⚡ Auto-Executed</h4>
            <p><b>Action:</b> Autonomous healing for {scenario_name}</p>
            <p><b>Mode:</b> Fully autonomous (guardrails active)</p>
            <p><b>Status:</b> ✅ <span style='color: green;'>Successfully completed</span></p>
            </div>
            """
        
        return approval_html, {"approval_required": approval_required, "compliance_mode": "strict"}, results
    except Exception as e:
        logger.error(f"Enterprise execution error: {e}")
        error_html = f"<div style='color: red; padding: 10px;'>❌ Execution error: {str(e)}</div>"
        return error_html, {"error": True}, {"status": "Failed"}

def update_visualization(scenario_name: str, viz_type: str):
    """Update visualization based on selection"""
    try:
        viz_engine = VisualizationEngine()
        
        if viz_type == "Interactive Timeline":
            return viz_engine.create_timeline_visualization()
        elif viz_type == "Metrics Stream":
            return viz_engine.create_metrics_stream()
        elif viz_type == "Performance Radar":
            return viz_engine.create_performance_radar()
        else:
            return viz_engine.create_timeline_visualization()
    except Exception as e:
        logger.error(f"Visualization error: {e}")
        return VisualizationEngine._create_error_figure("Visualization")

def calculate_roi(monthly_incidents: int, avg_impact: int, team_size: int):
    """Calculate ROI - NOW WORKING"""
    try:
        annual_impact = monthly_incidents * 12 * avg_impact
        team_cost = team_size * 150000  # $150k per engineer
        savings = annual_impact * 0.82  # 82% savings with ARF
        
        if team_cost > 0:
            roi_multiplier = savings / team_cost
        else:
            roi_multiplier = 0
        
        # Determine recommendation
        if roi_multiplier >= 5.0:
            recommendation = "✅ Excellent fit for ARF Enterprise"
            icon = "🚀"
        elif roi_multiplier >= 2.0:
            recommendation = "✅ Good ROI with ARF Enterprise"
            icon = "✅"
        elif roi_multiplier >= 1.0:
            recommendation = "⚠️ Consider ARF OSS edition first"
            icon = "ℹ️"
        else:
            recommendation = "⚠️ Start with ARF OSS (free)"
            icon = "🆓"
        
        return {
            "analysis": {
                "your_annual_impact": f"${annual_impact:,.0f}",
                "your_team_cost": f"${team_cost:,.0f}",
                "potential_savings": f"${savings:,.0f}",
                "your_roi_multiplier": f"{roi_multiplier:.1f}×",
                "vs_industry_average": "5.2× average ROI",
                "recommendation": f"{icon} {recommendation}",
                "payback_period": f"{(team_cost / (savings / 12)):.1f} months" if savings > 0 else "N/A"
            }
        }
    except Exception as e:
        logger.error(f"ROI calculation error: {e}")
        return {"error": f"Calculation error: {str(e)}"}

# ===========================================
# GRADIO INTERFACE
# ===========================================

def create_interface():
    """Create the Gradio interface"""
    
    with gr.Blocks(
        title="🚀 ARF Investor Demo v3.5.0",
        theme=gr.themes.Soft(),
        css="""
        .gradio-container { max-width: 1200px; margin: auto; }
        h1, h2, h3 { color: #1a365d !important; }
        .primary-button { background: linear-gradient(90deg, #667eea 0%, #764ba2 100%) !important; }
        """
    ) as demo:
        
        # ============ HEADER ============
        gr.Markdown("""
        # 🚀 Agentic Reliability Framework - Investor Demo v3.5.0
        ## From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability
        
        **Experience the transformation:** OSS (Advisory) ↔ Enterprise (Autonomous)
        """)
        
        # ============ MAIN TABS ============
        with gr.Tabs():
            
            # TAB 1: LIVE INCIDENT DEMO
            with gr.TabItem("🔥 Live Incident Demo", id="live-demo"):
                with gr.Row():
                    # Left Panel
                    with gr.Column(scale=1):
                        gr.Markdown("### 🎬 Incident Scenario")
                        scenario_dropdown = gr.Dropdown(
                            choices=list(INCIDENT_SCENARIOS.keys()),
                            value="Cache Miss Storm",
                            label="Select critical incident:"
                        )
                        
                        gr.Markdown("### 📊 Current Crisis Metrics")
                        metrics_display = gr.JSON(
                            value=INCIDENT_SCENARIOS["Cache Miss Storm"]["metrics"]
                        )
                        
                        gr.Markdown("### 💰 Business Impact")
                        impact_display = gr.JSON(
                            value=INCIDENT_SCENARIOS["Cache Miss Storm"]["impact"]
                        )
                    
                    # Right Panel
                    with gr.Column(scale=2):
                        # Visualization
                        gr.Markdown("### 📈 Incident Timeline Visualization")
                        viz_radio = gr.Radio(
                            choices=["Interactive Timeline", "Metrics Stream", "Performance Radar"],
                            value="Interactive Timeline",
                            label="Choose visualization:"
                        )
                        
                        timeline_output = gr.Plot()
                        
                        # Action Buttons
                        with gr.Row():
                            oss_btn = gr.Button("🆓 Run OSS Analysis", variant="secondary")
                            enterprise_btn = gr.Button("🚀 Execute Enterprise Healing", variant="primary")
                        
                        # Approval Toggle
                        approval_toggle = gr.Checkbox(
                            label="🔐 Require Manual Approval",
                            value=True,
                            info="Toggle to show approval workflow vs auto-execution"
                        )
                        
                        # Approval Display
                        approval_display = gr.HTML(
                            value="<div style='padding: 10px; background: #f8f9fa; border-radius: 5px;'>Approval status will appear here</div>"
                        )
                        
                        # Configuration
                        config_display = gr.JSON(
                            label="⚙️ Enterprise Configuration",
                            value={"approval_required": True, "compliance_mode": "strict"}
                        )
                        
                        # Results
                        results_display = gr.JSON(
                            label="🎯 Execution Results",
                            value={"status": "Ready for execution..."}
                        )
            
            # TAB 2: BUSINESS IMPACT & ROI
            with gr.TabItem("💰 Business Impact & ROI", id="business-roi"):
                with gr.Column():
                    # Business Dashboard
                    gr.Markdown("### 📊 Business Health Dashboard")
                    dashboard_output = gr.Plot()
                    
                    # ROI Calculator
                    gr.Markdown("### 🧮 Interactive ROI Calculator")
                    with gr.Row():
                        with gr.Column(scale=1):
                            monthly_slider = gr.Slider(
                                1, 100, value=15, step=1,
                                label="Monthly incidents"
                            )
                            impact_slider = gr.Slider(
                                1000, 50000, value=8500, step=500,
                                label="Avg incident impact ($)"
                            )
                            team_slider = gr.Slider(
                                1, 20, value=5, step=1,
                                label="Reliability team size"
                            )
                            calculate_btn = gr.Button("Calculate My ROI", variant="primary")
                        
                        with gr.Column(scale=2):
                            roi_output = gr.JSON(
                                label="Your ROI Analysis",
                                value={"analysis": "Adjust sliders and click 'Calculate My ROI'"}
                            )
                    
                    # Capability Comparison
                    gr.Markdown("### 📋 Capability Comparison")
                    with gr.Row():
                        with gr.Column():
                            gr.Markdown("""
                            **OSS Edition (Free)**
                            - Advisory recommendations only
                            - Manual implementation required
                            - No auto-healing
                            - Community support
                            - No ROI measurement
                            """)
                        with gr.Column():
                            gr.Markdown("""
                            **Enterprise Edition**
                            - Autonomous execution
                            - 81.7% auto-heal rate
                            - Full audit trails & compliance
                            - 24/7 enterprise support
                            - 5.2× average ROI
                            - 2-3 month payback
                            """)
            
            # TAB 3: AUDIT TRAIL
            with gr.TabItem("📜 Audit Trail", id="audit-trail"):
                with gr.Row():
                    with gr.Column(scale=1):
                        gr.Markdown("### 📋 Recent Executions")
                        with gr.Row():
                            refresh_btn = gr.Button("🔄 Refresh", size="sm")
                            clear_btn = gr.Button("🗑️ Clear All", variant="stop", size="sm")
                        
                        audit_table = gr.Dataframe(
                            headers=["Time", "Scenario", "Actions", "Status", "Savings"],
                            value=[
                                ["22:14", "Cache Miss Storm", "4", "✅ Executed", "$7,200"],
                                ["21:58", "Memory Leak", "3", "✅ Executed", "$5,200"],
                                ["21:45", "API Rate Limit", "4", "✅ Executed", "$2,800"],
                                ["21:30", "DB Connection Pool", "4", "✅ Executed", "$3,800"]
                            ],
                            interactive=False,
                            wrap=True
                        )
                    
                    with gr.Column(scale=2):
                        gr.Markdown("### 📈 Execution History")
                        history_output = gr.Plot()
        
        # ============ FOOTER ============
        gr.Markdown("---")
        with gr.Row():
            with gr.Column(scale=2):
                gr.Markdown("""
                **📞 Contact & Demo**  
                📧 enterprise@arf.dev  
                🌐 [https://arf.dev](https://arf.dev)  
                📚 [Documentation](https://docs.arf.dev)  
                💻 [GitHub](https://github.com/petterjuan/agentic-reliability-framework)
                """)
            with gr.Column(scale=1):
                gr.Markdown("""
                **🎯 Schedule a Demo**  
                [https://arf.dev/demo](https://arf.dev/demo)
                """)
        
        # ============ EVENT HANDLERS ============
        
        # Scenario change updates metrics and visualization
        scenario_dropdown.change(
            lambda name: (
                INCIDENT_SCENARIOS.get(name, {}).get("metrics", {}),
                INCIDENT_SCENARIOS.get(name, {}).get("impact", {}),
                update_visualization(name, "Interactive Timeline")
            ),
            inputs=[scenario_dropdown],
            outputs=[metrics_display, impact_display, timeline_output]
        )
        
        # Visualization type change
        viz_radio.change(
            lambda scenario, viz: update_visualization(scenario, viz),
            inputs=[scenario_dropdown, viz_radio],
            outputs=[timeline_output]
        )
        
        # OSS Analysis button - NOW WORKING
        oss_btn.click(
            lambda scenario: run_oss_analysis(scenario),
            inputs=[scenario_dropdown],
            outputs=[results_display]
        )
        
        # Enterprise Execution button - NOW WORKING
        enterprise_btn.click(
            lambda scenario, approval: execute_enterprise_healing(scenario, approval),
            inputs=[scenario_dropdown, approval_toggle],
            outputs=[approval_display, config_display, results_display]
        )
        
        # Approval toggle updates config
        approval_toggle.change(
            lambda approval: {"approval_required": approval, "compliance_mode": "strict"},
            inputs=[approval_toggle],
            outputs=[config_display]
        )
        
        # ROI Calculation - NOW WORKING
        calculate_btn.click(
            calculate_roi,
            inputs=[monthly_slider, impact_slider, team_slider],
            outputs=[roi_output]
        )
        
        # Load initial visualizations
        demo.load(
            lambda: (
                VisualizationEngine.create_business_dashboard(),
                VisualizationEngine.create_execution_history()
            ),
            outputs=[dashboard_output, history_output]
        )
        
        # Refresh audit trail
        refresh_btn.click(
            lambda: VisualizationEngine.create_execution_history(),
            outputs=[history_output]
        )
        
        # Clear audit trail
        clear_btn.click(
            lambda: (
                [],
                VisualizationEngine._create_error_figure("History cleared")
            ),
            outputs=[audit_table, history_output]
        )
    
    return demo

# ===========================================
# LAUNCH APPLICATION
# ===========================================

if __name__ == "__main__":
    logger.info("🚀 Launching ARF Investor Demo v3.5.0 - ALL FIXES APPLIED")
    logger.info("✅ OSS Analysis button: FIXED")
    logger.info("✅ Enterprise Execution: FIXED")
    logger.info("✅ ROI Calculator: FIXED")
    logger.info("✅ All visualizations: WORKING")
    
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=False,
        show_error=True
    )