app.py

"""
🚀 ARF ULTIMATE INVESTOR DEMO v3.3.7
Enhanced with professional visualizations, export features, and data persistence
"""

import asyncio
import datetime
import json
import logging
import time
import uuid
import random
import base64
import io
from typing import Dict, Any, List, Optional, Tuple
from collections import defaultdict, deque
import hashlib

import gradio as gr
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
from plotly.subplots import make_subplots
import matplotlib.pyplot as plt
from matplotlib import font_manager
import seaborn as sns

# Import OSS components
try:
    from agentic_reliability_framework.arf_core.models.healing_intent import (
        HealingIntent,
        create_rollback_intent,
        create_restart_intent,
        create_scale_out_intent,
    )
    from agentic_reliability_framework.arf_core.engine.simple_mcp_client import OSSMCPClient
    OSS_AVAILABLE = True
except ImportError:
    OSS_AVAILABLE = False
    logger = logging.getLogger(__name__)
    logger.warning("OSS package not available")

# ============================================================================
# DATA PERSISTENCE & SESSION MANAGEMENT
# ============================================================================

class DemoSessionManager:
    """Manage session data persistence and historical trends"""
    
    def __init__(self):
        self.sessions = {}
        self.global_stats = {
            "total_sessions": 0,
            "total_revenue_protected": 0.0,
            "total_executions": 0,
            "historical_trends": deque(maxlen=100),  # Last 100 data points
            "peak_performance": {
                "highest_roi": 0.0,
                "fastest_mttr": float('inf'),
                "largest_incident_resolved": 0.0,
            }
        }
    
    def start_session(self, session_id: str):
        """Start a new user session"""
        if session_id not in self.sessions:
            self.sessions[session_id] = {
                "start_time": time.time(),
                "actions": [],
                "metrics": {},
                "scenarios_tried": set(),
                "roi_calculations": [],
                "exported_reports": [],
            }
            self.global_stats["total_sessions"] += 1
        return self.sessions[session_id]
    
    def record_action(self, session_id: str, action: str, details: Dict[str, Any]):
        """Record user action with details"""
        if session_id in self.sessions:
            self.sessions[session_id]["actions"].append({
                "timestamp": time.time(),
                "action": action,
                "details": details,
            })
            
            # Update global historical trends
            if "revenue_protected" in details:
                self.global_stats["historical_trends"].append({
                    "timestamp": time.time(),
                    "revenue": details["revenue_protected"],
                    "session": session_id[-6:],  # Last 6 chars for anonymity
                })
                self.global_stats["total_revenue_protected"] += details["revenue_protected"]
            
            self.global_stats["total_executions"] += 1
            
            # Update peak performance
            if details.get("revenue_protected", 0) > self.global_stats["peak_performance"]["largest_incident_resolved"]:
                self.global_stats["peak_performance"]["largest_incident_resolved"] = details["revenue_protected"]
    
    def get_session_summary(self, session_id: str) -> Dict[str, Any]:
        """Get summary of current session"""
        if session_id in self.sessions:
            session = self.sessions[session_id]
            duration = time.time() - session["start_time"]
            
            return {
                "session_duration": f"{duration/60:.1f} minutes",
                "total_actions": len(session["actions"]),
                "scenarios_tried": len(session["scenarios_tried"]),
                "roi_calculations": len(session["roi_calculations"]),
                "last_action": session["actions"][-1]["action"] if session["actions"] else "None",
                "session_id_short": session_id[-8:],
            }
        return {}
    
    def get_historical_trends_chart(self):
        """Create historical trends visualization"""
        if not self.global_stats["historical_trends"]:
            return go.Figure()
        
        # Prepare data
        data = list(self.global_stats["historical_trends"])
        df = pd.DataFrame(data)
        
        # Create figure with subplots
        fig = make_subplots(
            rows=2, cols=2,
            subplot_titles=('Revenue Protection Over Time', 'Cumulative Revenue',
                          'Session Activity', 'Performance Metrics'),
            specs=[[{'type': 'scatter'}, {'type': 'scatter'}],
                   [{'type': 'bar'}, {'type': 'indicator'}]],
            vertical_spacing=0.15,
            horizontal_spacing=0.15
        )
        
        # Revenue over time
        fig.add_trace(
            go.Scatter(
                x=df['timestamp'],
                y=df['revenue'],
                mode='lines+markers',
                name='Revenue Protected',
                line=dict(color='#4CAF50', width=3),
                marker=dict(size=8),
                hovertemplate='$%{y:,.0f}<br>%{text}',
                text=[f"Session: {s}" for s in df['session']]
            ),
            row=1, col=1
        )
        
        # Cumulative revenue
        cumulative_rev = df['revenue'].cumsum()
        fig.add_trace(
            go.Scatter(
                x=df['timestamp'],
                y=cumulative_rev,
                mode='lines',
                name='Cumulative Revenue',
                line=dict(color='#2196F3', width=3, dash='dash'),
                fill='tozeroy',
                fillcolor='rgba(33, 150, 243, 0.1)'
            ),
            row=1, col=2
        )
        
        # Session activity (group by session)
        session_counts = df['session'].value_counts().head(10)
        fig.add_trace(
            go.Bar(
                x=session_counts.index,
                y=session_counts.values,
                name='Actions per Session',
                marker_color='#FF9800',
                hovertemplate='Session: %{x}<br>Actions: %{y}'
            ),
            row=2, col=1
        )
        
        # Performance indicator
        avg_revenue = df['revenue'].mean() if len(df) > 0 else 0
        fig.add_trace(
            go.Indicator(
                mode="gauge+number+delta",
                value=avg_revenue,
                title={'text': "Avg Revenue/Incident"},
                delta={'reference': 100000, 'increasing': {'color': "#4CAF50"}},
                gauge={
                    'axis': {'range': [None, max(500000, avg_revenue * 1.5)]},
                    'bar': {'color': "#4CAF50"},
                    'steps': [
                        {'range': [0, 100000], 'color': '#FFEBEE'},
                        {'range': [100000, 300000], 'color': '#FFCDD2'},
                        {'range': [300000, 500000], 'color': '#EF9A9A'}
                    ],
                    'threshold': {
                        'line': {'color': "red", 'width': 4},
                        'thickness': 0.75,
                        'value': 250000
                    }
                }
            ),
            row=2, col=2
        )
        
        # Update layout
        fig.update_layout(
            title="📈 Historical Performance Trends",
            height=700,
            showlegend=True,
            plot_bgcolor='white',
            paper_bgcolor='white',
        )
        
        # Update axes
        fig.update_xaxes(title_text="Time", row=1, col=1)
        fig.update_yaxes(title_text="Revenue ($)", row=1, col=1)
        fig.update_xaxes(title_text="Time", row=1, col=2)
        fig.update_yaxes(title_text="Cumulative Revenue ($)", row=1, col=2)
        fig.update_xaxes(title_text="Session", row=2, col=1)
        fig.update_yaxes(title_text="Actions", row=2, col=1)
        
        return fig

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

class EnhancedVisualizationEngine:
    """Enhanced visualization engine with animations and interactivity"""
    
    @staticmethod
    def create_animated_radar_chart(metrics: Dict[str, float], title: str = "Performance Radar"):
        """Create animated radar chart with smooth transitions"""
        
        categories = list(metrics.keys())
        values = list(metrics.values())
        
        # Create radar chart
        fig = go.Figure()
        
        fig.add_trace(go.Scatterpolar(
            r=values,
            theta=categories,
            fill='toself',
            name='Current',
            line_color='#4CAF50',
            opacity=0.8
        ))
        
        # Add ideal baseline (for comparison)
        baseline_values = [max(values) * 0.8] * len(values)
        fig.add_trace(go.Scatterpolar(
            r=baseline_values,
            theta=categories,
            fill='toself',
            name='Ideal Baseline',
            line_color='#2196F3',
            opacity=0.3
        ))
        
        fig.update_layout(
            polar=dict(
                radialaxis=dict(
                    visible=True,
                    range=[0, max(values) * 1.2]
                )),
            showlegend=True,
            title=title,
            height=400,
            animations=[{
                'frame': {'duration': 500, 'redraw': True},
                'transition': {'duration': 300, 'easing': 'cubic-in-out'},
            }]
        )
        
        return fig
    
    @staticmethod
    def create_heatmap_timeline(scenarios: List[Dict[str, Any]]):
        """Create heatmap timeline of incidents"""
        
        # Prepare data
        severity_map = {"critical": 3, "high": 2, "medium": 1, "low": 0}
        
        data = []
        for i, scenario in enumerate(scenarios):
            impact = scenario.get("business_impact", {})
            severity_val = severity_map.get(
                "critical" if impact.get("revenue_at_risk", 0) > 1000000 else
                "high" if impact.get("revenue_at_risk", 0) > 500000 else
                "medium" if impact.get("revenue_at_risk", 0) > 100000 else "low",
                0
            )
            
            data.append({
                "Scenario": scenario.get("description", "Unknown")[:30] + "...",
                "Revenue Risk": impact.get("revenue_at_risk", 0),
                "Users Impacted": impact.get("users_impacted", 0),
                "Severity": severity_val,
                "Time to Resolve": impact.get("time_to_resolve", 0),
            })
        
        df = pd.DataFrame(data)
        
        # Create heatmap
        fig = go.Figure(data=go.Heatmap(
            z=df[['Revenue Risk', 'Users Impacted', 'Severity', 'Time to Resolve']].values.T,
            x=df['Scenario'],
            y=['Revenue Risk ($)', 'Users Impacted', 'Severity Level', 'Time to Resolve (min)'],
            colorscale='RdYlGn_r',  # Red to Green (reversed for severity)
            showscale=True,
            hoverongaps=False,
            hovertemplate='<b>%{x}</b><br>%{y}: %{z}<extra></extra>'
        ))
        
        fig.update_layout(
            title="🔥 Incident Heatmap Timeline",
            xaxis_title="Scenarios",
            yaxis_title="Metrics",
            height=400,
            xaxis={'tickangle': 45},
        )
        
        return fig
    
    @staticmethod
    def create_real_time_metrics_stream():
        """Create real-time streaming metrics visualization"""
        
        # Generate sample streaming data
        times = pd.date_range(start='now', periods=50, freq='1min')
        values = np.cumsum(np.random.randn(50)) + 100
        
        fig = go.Figure()
        
        fig.add_trace(go.Scatter(
            x=times,
            y=values,
            mode='lines+markers',
            name='System Health Score',
            line=dict(color='#2196F3', width=3),
            marker=dict(size=6),
            hovertemplate='Time: %{x}<br>Score: %{y:.1f}<extra></extra>'
        ))
        
        # Add threshold lines
        fig.add_hline(y=95, line_dash="dash", line_color="green", 
                     annotation_text="Optimal", annotation_position="right")
        fig.add_hline(y=80, line_dash="dash", line_color="orange", 
                     annotation_text="Warning", annotation_position="right")
        fig.add_hline(y=70, line_dash="dash", line_color="red", 
                     annotation_text="Critical", annotation_position="right")
        
        # Add range slider
        fig.update_layout(
            title="📊 Real-time System Health Monitor",
            xaxis=dict(
                rangeselector=dict(
                    buttons=list([
                        dict(count=15, label="15m", step="minute", stepmode="backward"),
                        dict(count=1, label="1h", step="hour", stepmode="backward"),
                        dict(count=6, label="6h", step="hour", stepmode="backward"),
                        dict(step="all")
                    ])
                ),
                rangeslider=dict(visible=True),
                type="date"
            ),
            yaxis_title="Health Score",
            height=400,
            showlegend=True
        )
        
        return fig
    
    @staticmethod
    def create_3d_rag_graph(incidents: List[Dict], outcomes: List[Dict], edges: List[Dict]):
        """Create 3D visualization of RAG graph"""
        
        if not incidents:
            return go.Figure()
        
        # Prepare 3D coordinates
        np.random.seed(42)  # For reproducibility
        
        # Incident nodes (red to orange based on severity)
        incident_coords = []
        incident_colors = []
        incident_sizes = []
        incident_labels = []
        
        for inc in incidents:
            incident_coords.append([
                np.random.uniform(-1, 0),  # x: negative side
                np.random.uniform(-1, 1),  # y
                np.random.uniform(0, 1)    # z: incidents on bottom layer
            ])
            
            severity = inc.get("severity", "medium")
            if severity == "critical":
                incident_colors.append("#FF4444")  # Bright red
                incident_sizes.append(20)
            elif severity == "high":
                incident_colors.append("#FF9800")  # Orange
                incident_sizes.append(15)
            else:
                incident_colors.append("#FFC107")  # Amber
                incident_sizes.append(10)
            
            incident_labels.append(f"{inc.get('component', 'Unknown')}<br>{severity.upper()}")
        
        # Outcome nodes (green gradient based on success)
        outcome_coords = []
        outcome_colors = []
        outcome_sizes = []
        outcome_labels = []
        
        for out in outcomes:
            outcome_coords.append([
                np.random.uniform(0, 1),   # x: positive side
                np.random.uniform(-1, 1),  # y
                np.random.uniform(0, 1)    # z
            ])
            
            if out.get("success", False):
                outcome_colors.append("#4CAF50")  # Green
                outcome_sizes.append(12)
            else:
                outcome_colors.append("#F44336")  # Red
                outcome_sizes.append(12)
            
            outcome_labels.append(f"{out.get('action', 'Unknown')}<br>{'✅' if out.get('success') else '❌'}")
        
        # Create figure
        fig = go.Figure()
        
        # Add incident nodes
        fig.add_trace(go.Scatter3d(
            x=[c[0] for c in incident_coords],
            y=[c[1] for c in incident_coords],
            z=[c[2] for c in incident_coords],
            mode='markers+text',
            marker=dict(
                size=incident_sizes,
                color=incident_colors,
                symbol='circle',
                line=dict(color='white', width=2)
            ),
            text=incident_labels,
            textposition="top center",
            name='Incidents',
            hoverinfo='text',
        ))
        
        # Add outcome nodes
        fig.add_trace(go.Scatter3d(
            x=[c[0] for c in outcome_coords],
            y=[c[1] for c in outcome_coords],
            z=[c[2] for c in outcome_coords],
            mode='markers+text',
            marker=dict(
                size=outcome_sizes,
                color=outcome_colors,
                symbol='diamond',
                line=dict(color='white', width=1)
            ),
            text=outcome_labels,
            textposition="top center",
            name='Outcomes',
            hoverinfo='text',
        ))
        
        # Add edges (connections)
        edge_x, edge_y, edge_z = [], [], []
        for edge in edges:
            source_idx = int(edge["source"].split("_")[1]) if "_" in edge["source"] else 0
            target_idx = int(edge["target"].split("_")[1]) if "_" in edge["target"] else 0
            
            if source_idx < len(incident_coords) and target_idx < len(outcome_coords):
                # Edge from incident to outcome
                edge_x += [incident_coords[source_idx][0], outcome_coords[target_idx][0], None]
                edge_y += [incident_coords[source_idx][1], outcome_coords[target_idx][1], None]
                edge_z += [incident_coords[source_idx][2], outcome_coords[target_idx][2], None]
        
        fig.add_trace(go.Scatter3d(
            x=edge_x,
            y=edge_y,
            z=edge_z,
            mode='lines',
            line=dict(color='rgba(100, 100, 100, 0.5)', width=2),
            hoverinfo='none',
            showlegend=False
        ))
        
        # Update layout
        fig.update_layout(
            title="🧠 3D RAG Knowledge Graph",
            scene=dict(
                xaxis_title="Incidents ← → Outcomes",
                yaxis_title="",
                zaxis_title="Knowledge Depth",
                camera=dict(
                    eye=dict(x=1.5, y=1.5, z=1.5)
                ),
                aspectmode='manual',
                aspectratio=dict(x=2, y=1, z=1)
            ),
            height=600,
            showlegend=True,
        )
        
        return fig

# ============================================================================
# EXPORT ENGINE
# ============================================================================

class ExportEngine:
    """Handle export of reports, charts, and data"""
    
    @staticmethod
    def export_roi_report_as_html(roi_data: Dict[str, Any]) -> str:
        """Export ROI report as HTML"""
        
        html = f"""
        <!DOCTYPE html>
        <html>
        <head>
            <title>ARF ROI Report - {datetime.datetime.now().strftime('%Y-%m-%d')}</title>
            <style>
                body {{ font-family: Arial, sans-serif; margin: 40px; }}
                .header {{ background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
                         color: white; padding: 30px; border-radius: 10px; margin-bottom: 30px; }}
                .metric-card {{ background: white; border-radius: 10px; padding: 20px; 
                              margin: 15px; box-shadow: 0 4px 6px rgba(0,0,0,0.1); display: inline-block; width: 200px; }}
                .metric-value {{ font-size: 24px; font-weight: bold; color: #4CAF50; }}
                .highlight {{ background: #E8F5E9; padding: 20px; border-left: 4px solid #4CAF50; margin: 20px 0; }}
                table {{ width: 100%; border-collapse: collapse; margin: 20px 0; }}
                th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #ddd; }}
                th {{ background-color: #f8f9fa; }}
                .footer {{ margin-top: 40px; padding-top: 20px; border-top: 1px solid #eee; 
                         color: #666; font-size: 12px; }}
            </style>
        </head>
        <body>
            <div class="header">
                <h1>🚀 ARF ROI Analysis Report</h1>
                <p>Generated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
            </div>
            
            <h2>📊 Executive Summary</h2>
            <div class="highlight">
                <h3>Investment Payback: {roi_data.get('payback_period', 'N/A')}</h3>
                <h3>First Year ROI: {roi_data.get('first_year_roi', 'N/A')}</h3>
            </div>
            
            <h2>💰 Financial Metrics</h2>
            <div style="display: flex; flex-wrap: wrap;">
        """
        
        # Add metric cards
        metrics_to_show = [
            ('monthly_savings', 'Monthly Savings'),
            ('annual_savings', 'Annual Savings'),
            ('implementation_cost', 'Implementation Cost'),
            ('auto_heal_rate', 'Auto-Heal Rate'),
            ('mttr_improvement', 'MTTR Improvement'),
        ]
        
        for key, label in metrics_to_show:
            if key in roi_data:
                html += f"""
                <div class="metric-card">
                    <div class="metric-label">{label}</div>
                    <div class="metric-value">{roi_data[key]}</div>
                </div>
                """
        
        html += """
            </div>
            
            <h2>📈 Detailed Breakdown</h2>
            <table>
                <tr><th>Metric</th><th>Without ARF</th><th>With ARF</th><th>Improvement</th></tr>
        """
        
        # Add comparison table
        comparisons = [
            ('Manual Incident Handling', '45 minutes', '2.3 minutes', '94% faster'),
            ('Engineer Hours/Month', '250 hours', '37.5 hours', '85% reduction'),
            ('Revenue at Risk/Month', '$450,000', '$82,350', '82% protection'),
            ('Compliance Audit Costs', '$50,000/year', '$5,000/year', '90% savings'),
        ]
        
        for comp in comparisons:
            html += f"""
            <tr>
                <td>{comp[0]}</td>
                <td>{comp[1]}</td>
                <td>{comp[2]}</td>
                <td><strong>{comp[3]}</strong></td>
            </tr>
            """
        
        html += f"""
            </table>
            
            <div class="footer">
                <p>ARF Ultimate Investor Demo v3.3.7 | Generated automatically</p>
                <p>Confidential - For investor review only</p>
                <p>Contact: enterprise@petterjuan.com | Website: https://arf.dev</p>
            </div>
        </body>
        </html>
        """
        
        return html
    
    @staticmethod
    def export_compliance_report(compliance_data: Dict[str, Any], format: str = "html") -> str:
        """Export compliance report in specified format"""
        
        if format == "html":
            return ExportEngine._compliance_to_html(compliance_data)
        else:
            # Return as JSON for other formats
            return json.dumps(compliance_data, indent=2)
    
    @staticmethod
    def _compliance_to_html(compliance_data: Dict[str, Any]) -> str:
        """Convert compliance data to HTML report"""
        
        html = f"""
        <!DOCTYPE html>
        <html>
        <head>
            <title>ARF {compliance_data.get('standard', 'Compliance')} Report</title>
            <style>
                body {{ font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; margin: 40px; }}
                .header {{ background: linear-gradient(135deg, #2c3e50 0%, #3498db 100%); 
                         color: white; padding: 30px; border-radius: 10px; margin-bottom: 30px; }}
                .status-pass {{ color: #27ae60; font-weight: bold; }}
                .status-fail {{ color: #e74c3c; font-weight: bold; }}
                .finding-card {{ background: white; border-radius: 8px; padding: 15px; 
                              margin: 10px 0; box-shadow: 0 2px 4px rgba(0,0,0,0.1); 
                              border-left: 4px solid #3498db; }}
                .footer {{ margin-top: 40px; padding-top: 20px; border-top: 1px solid #eee; 
                         color: #666; font-size: 12px; }}
            </style>
        </head>
        <body>
            <div class="header">
                <h1>📋 ARF {compliance_data.get('standard', 'Compliance')} Compliance Report</h1>
                <p>Report ID: {compliance_data.get('report_id', 'N/A')} | 
                   Generated: {compliance_data.get('generated_at', 'N/A')}</p>
                <p>Period: {compliance_data.get('period', 'N/A')}</p>
            </div>
            
            <h2>✅ Executive Summary</h2>
            <div class="finding-card">
                <h3>{compliance_data.get('summary', 'No summary available')}</h3>
                <p><strong>Estimated Audit Cost Savings:</strong> {compliance_data.get('estimated_audit_cost_savings', 'N/A')}</p>
            </div>
            
            <h2>🔍 Detailed Findings</h2>
        """
        
        # Add findings
        findings = compliance_data.get('findings', {})
        for key, value in findings.items():
            status_class = "status-pass" if value in [True, "99.95%", "Complete"] else "status-fail"
            display_value = "✅ PASS" if value is True else "❌ FAIL" if value is False else str(value)
            
            html += f"""
            <div class="finding-card">
                <h3>{key.replace('_', ' ').title()}</h3>
                <p class="{status_class}">{display_value}</p>
            </div>
            """
        
        html += """
            <div class="footer">
                <p>This report was automatically generated by ARF Compliance Auditor</p>
                <p>All findings are based on automated system analysis</p>
                <p>Contact: enterprise@petterjuan.com | Compliance Hotline: +1-555-COMPLY</p>
            </div>
        </body>
        </html>
        """
        
        return html
    
    @staticmethod
    def export_chart_as_image(fig, format: str = "png") -> bytes:
        """Export Plotly chart as image bytes"""
        try:
            # For Plotly figures
            img_bytes = fig.to_image(format=format, scale=2)
            return img_bytes
        except Exception as e:
            logging.error(f"Failed to export chart: {e}")
            # Return placeholder
            return b""

# ============================================================================
# ENHANCED DEMO SCENARIOS
# ============================================================================

ENTERPRISE_SCENARIOS = {
    "🚨 Black Friday Payment Crisis": {
        "description": "Payment processing failing during peak. $500K/minute at risk.",
        "component": "payment-service",
        "metrics": {
            "latency_ms": 450,
            "error_rate": 0.22,
            "cpu_util": 0.95,
            "memory_util": 0.88,
            "queue_depth": 2500,
            "throughput": 850,
        },
        "business_impact": {
            "revenue_at_risk": 2500000,
            "users_impacted": 45000,
            "time_to_resolve": 2.3,
            "auto_heal_possible": True,
            "customer_satisfaction_impact": "Critical",
            "brand_reputation_risk": "High",
        },
        "oss_action": "scale_out",
        "enterprise_action": "autonomous_scale",
        "prediction": "Database crash predicted in 8.5 minutes",
        "visualization_type": "radar",
    },
    
    "⚡ Database Connection Pool Exhaustion": {
        "description": "Database connections exhausted. 12 services affected.",
        "component": "database",
        "metrics": {
            "latency_ms": 850,
            "error_rate": 0.35,
            "cpu_util": 0.78,
            "memory_util": 0.98,
            "connections": 980,
            "deadlocks": 12,
        },
        "business_impact": {
            "revenue_at_risk": 1200000,
            "users_impacted": 12000,
            "time_to_resolve": 8.5,
            "auto_heal_possible": True,
            "customer_satisfaction_impact": "High",
            "brand_reputation_risk": "Medium",
        },
        "oss_action": "restart_container",
        "enterprise_action": "approval_workflow",
        "prediction": "Cascading failure in 3.2 minutes",
        "visualization_type": "heatmap",
    },
    
    "🔮 Predictive Memory Leak": {
        "description": "Memory leak detected. $250K at risk in 18 minutes.",
        "component": "cache-service",
        "metrics": {
            "latency_ms": 320,
            "error_rate": 0.05,
            "cpu_util": 0.45,
            "memory_util": 0.94,
            "cache_hit_rate": 0.12,
            "garbage_collection": 45,
        },
        "business_impact": {
            "revenue_at_risk": 250000,
            "users_impacted": 65000,
            "time_to_resolve": 0.8,
            "auto_heal_possible": True,
            "customer_satisfaction_impact": "Medium",
            "brand_reputation_risk": "Low",
        },
        "oss_action": "restart_container",
        "enterprise_action": "predictive_prevention",
        "prediction": "Outage prevented 17 minutes before crash",
        "visualization_type": "radar",
    },
    
    "📈 API Error Rate Spike": {
        "description": "API errors increasing. Requires investigation.",
        "component": "api-service",
        "metrics": {
            "latency_ms": 120,
            "error_rate": 0.25,
            "cpu_util": 0.35,
            "memory_util": 0.42,
            "requests_per_second": 4500,
            "timeout_rate": 0.15,
        },
        "business_impact": {
            "revenue_at_risk": 150000,
            "users_impacted": 8000,
            "time_to_resolve": 45.0,
            "auto_heal_possible": False,
            "customer_satisfaction_impact": "Low",
            "brand_reputation_risk": "Low",
        },
        "oss_action": "rollback",
        "enterprise_action": "root_cause_analysis",
        "prediction": "Error rate will reach 35% in 22 minutes",
        "visualization_type": "stream",
    },
    
    "🌐 Global CDN Outage": {
        "description": "CDN failing across 3 regions affecting 200K users",
        "component": "cdn-service",
        "metrics": {
            "latency_ms": 1200,
            "error_rate": 0.65,
            "cpu_util": 0.25,
            "memory_util": 0.35,
            "bandwidth_util": 0.98,
            "regional_availability": 0.33,
        },
        "business_impact": {
            "revenue_at_risk": 3500000,
            "users_impacted": 200000,
            "time_to_resolve": 15.5,
            "auto_heal_possible": True,
            "customer_satisfaction_impact": "Critical",
            "brand_reputation_risk": "Critical",
        },
        "oss_action": "failover_regions",
        "enterprise_action": "geo_load_balancing",
        "prediction": "Global outage spreading to 5 regions in 12 minutes",
        "visualization_type": "heatmap",
    },
    
    "🔐 Authentication Service Failure": {
        "description": "OAuth service failing - users cannot login",
        "component": "auth-service",
        "metrics": {
            "latency_ms": 2500,
            "error_rate": 0.85,
            "cpu_util": 0.95,
            "memory_util": 0.99,
            "token_generation_rate": 5,
            "active_sessions": 45000,
        },
        "business_impact": {
            "revenue_at_risk": 1800000,
            "users_impacted": 95000,
            "time_to_resolve": 5.2,
            "auto_heal_possible": True,
            "customer_satisfaction_impact": "Critical",
            "brand_reputation_risk": "High",
        },
        "oss_action": "restart_service",
        "enterprise_action": "circuit_breaker_auto",
        "prediction": "Complete service failure in 4.8 minutes",
        "visualization_type": "radar",
    },
    
    "📊 Analytics Pipeline Crash": {
        "description": "Real-time analytics pipeline crashed during reporting",
        "component": "analytics-service",
        "metrics": {
            "latency_ms": 5000,
            "error_rate": 0.95,
            "cpu_util": 0.15,
            "memory_util": 0.99,
            "data_lag_minutes": 45,
            "queue_backlog": 1200000,
        },
        "business_impact": {
            "revenue_at_risk": 750000,
            "users_impacted": 25000,
            "time_to_resolve": 25.0,
            "auto_heal_possible": True,
            "customer_satisfaction_impact": "Medium",
            "brand_reputation_risk": "Medium",
        },
        "oss_action": "restart_pipeline",
        "enterprise_action": "data_recovery_auto",
        "prediction": "Data loss exceeding SLA in 18 minutes",
        "visualization_type": "stream",
    },
}

# ============================================================================
# MAIN DEMO UI - ENHANCED VERSION
# ============================================================================

def create_enhanced_demo():
    """Create enhanced ultimate investor demo UI"""
    
    # Initialize enhanced components
    business_calc = BusinessImpactCalculator()
    rag_visualizer = RAGGraphVisualizer()
    predictive_viz = PredictiveVisualizer()
    live_dashboard = LiveDashboard()
    viz_engine = EnhancedVisualizationEngine()
    export_engine = ExportEngine()
    session_manager = DemoSessionManager()
    enterprise_servers = {}
    
    # Generate session ID for this user
    session_id = f"session_{uuid.uuid4().hex[:16]}"
    session_manager.start_session(session_id)
    
    with gr.Blocks(title="🚀 ARF Ultimate Investor Demo v3.3.7") as demo:
        # Store session data in Gradio state
        session_state = gr.State({
            "session_id": session_id,
            "current_scenario": None,
            "exported_files": [],
            "visualization_cache": {},
        })
        
        gr.Markdown("""
        # 🚀 Agentic Reliability Framework - Ultimate Investor Demo v3.3.7
        ### **From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability**
        
        <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); 
                    color: white; padding: 20px; border-radius: 10px; margin: 20px 0;">
            <div style="display: flex; justify-content: space-between; align-items: center;">
                <div>
                    <h3 style="margin: 0;">🎯 Live Demo Session: <span id="session-id"></span></h3>
                    <p style="margin: 5px 0;">Experience the full spectrum: <strong>OSS (Free) ↔ Enterprise (Paid)</strong></p>
                </div>
                <div style="text-align: right;">
                    <p style="margin: 0;">🔗 <a href="#export-section" style="color: white; text-decoration: underline;">Export Reports</a></p>
                    <p style="margin: 0;">📊 <a href="#analytics-section" style="color: white; text-decoration: underline;">View Analytics</a></p>
                </div>
            </div>
        </div>
        
        <script>
            document.getElementById('session-id').textContent = '""" + session_id[-8:] + """';
        </script>
        
        *Watch as ARF transforms reliability from a $2M cost center to a $10M profit engine*
        """)
        
        # ================================================================
        # ENHANCED EXECUTIVE DASHBOARD TAB
        # ================================================================
        with gr.TabItem("🏢 Executive Dashboard", elem_id="dashboard-tab"):
            gr.Markdown("""
            ## 📊 Real-Time Business Impact Dashboard
            **Live metrics showing ARF's financial impact in enterprise deployments**
            """)
            
            with gr.Row():
                with gr.Column(scale=2):
                    # Enhanced metrics display with tooltips
                    with gr.Row():
                        with gr.Column(scale=1):
                            revenue_protected = gr.Markdown(
                                "### 💰 Revenue Protected\n**$0**",
                                elem_id="revenue-protected"
                            )
                            gr.HTML("""
                            <div style="background: #E8F5E9; padding: 10px; border-radius: 5px; margin-top: -15px;">
                                <small>💡 <strong>Tooltip:</strong> Total revenue protected from potential outages</small>
                            </div>
                            """)
                        
                        with gr.Column(scale=1):
                            auto_heal_rate = gr.Markdown(
                                "### ⚡ Auto-Heal Rate\n**0%**",
                                elem_id="auto-heal-rate"
                            )
                            gr.HTML("""
                            <div style="background: #FFF3E0; padding: 10px; border-radius: 5px; margin-top: -15px;">
                                <small>💡 <strong>Tooltip:</strong> Percentage of incidents resolved automatically</small>
                            </div>
                            """)
                    
                    with gr.Row():
                        with gr.Column(scale=1):
                            mttr_improvement = gr.Markdown(
                                "### 🚀 MTTR Improvement\n**94% faster**",
                                elem_id="mttr-improvement"
                            )
                            gr.HTML("""
                            <div style="background: #E3F2FD; padding: 10px; border-radius: 5px; margin-top: -15px;">
                                <small>💡 <strong>Tooltip:</strong> Mean Time To Recovery improvement vs industry</small>
                            </div>
                            """)
                        
                        with gr.Column(scale=1):
                            engineer_hours = gr.Markdown(
                                "### 👷 Engineer Hours Saved\n**0 hours**",
                                elem_id="engineer-hours"
                            )
                            gr.HTML("""
                            <div style="background: #F3E5F5; padding: 10px; border-radius: 5px; margin-top: -15px;">
                                <small>💡 <strong>Tooltip:</strong> Engineering time saved through automation</small>
                            </div>
                            """)
                
                with gr.Column(scale=1):
                    # Quick stats card
                    gr.Markdown("""
                    ### 📈 Session Statistics
                    <div style="background: white; padding: 15px; border-radius: 10px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);">
                        <p>🆔 **Session:** """ + session_id[-8:] + """</p>
                        <p>🕐 **Duration:** 0.0 min</p>
                        <p>🔥 **Incidents Handled:** 0</p>
                        <p>📊 **Scenarios Tried:** 0</p>
                    </div>
                    """)
            
            # Real-time streaming metrics
            gr.Markdown("### 📈 Real-time System Health Monitor")
            real_time_metrics = gr.Plot(
                label="",
                elem_id="real-time-metrics"
            )
            
            # Enhanced incident feed with filtering
            with gr.Row():
                with gr.Column(scale=3):
                    gr.Markdown("### 🔥 Live Incident Feed")
                    incident_feed = gr.Dataframe(
                        headers=["Time", "Service", "Impact", "Status", "Value Protected"],
                        value=[],
                        interactive=False,
                        elem_id="incident-feed"
                    )
                
                with gr.Column(scale=1):
                    gr.Markdown("### 🔍 Quick Filters")
                    filter_severity = gr.Dropdown(
                        choices=["All", "Critical", "High", "Medium", "Low"],
                        value="All",
                        label="Filter by Severity"
                    )
                    filter_status = gr.Dropdown(
                        choices=["All", "Resolved", "In Progress", "Failed"],
                        value="All",
                        label="Filter by Status"
                    )
            
            # Top customers with enhanced visualization
            gr.Markdown("### 🏆 Top Customers Protected")
            with gr.Row():
                with gr.Column(scale=2):
                    customers_table = gr.Dataframe(
                        headers=["Customer", "Industry", "Revenue Protected", "Uptime", "ROI"],
                        value=[
                            ["FinTech Corp", "Financial Services", "$2.1M", "99.99%", "8.3×"],
                            ["HealthSys Inc", "Healthcare", "$1.8M", "99.995%", "Priceless"],
                            ["SaaSPlatform", "SaaS", "$1.5M", "99.98%", "6.8×"],
                            ["MediaStream", "Media", "$1.2M", "99.97%", "7.1×"],
                            ["LogisticsPro", "Logistics", "$900K", "99.96%", "6.5×"],
                        ],
                        interactive=False,
                    )
                
                with gr.Column(scale=1):
                    # Customer ROI visualization
                    gr.Markdown("#### 📊 ROI Distribution")
                    roi_distribution = gr.Plot(
                        label="Customer ROI Distribution"
                    )
        
        # ================================================================
        # ENHANCED LIVE WAR ROOM TAB
        # ================================================================
        with gr.TabItem("🔥 Live War Room", elem_id="war-room-tab"):
            gr.Markdown("""
            ## 🔥 Multi-Incident War Room
            **Watch ARF handle 8+ simultaneous incidents across different services**
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    # Enhanced scenario selector with search
                    scenario_selector = gr.Dropdown(
                        choices=list(ENTERPRISE_SCENARIOS.keys()),
                        value="🚨 Black Friday Payment Crisis",
                        label="🎬 Select Incident Scenario",
                        info="Choose an enterprise incident scenario",
                        filterable=True,
                        allow_custom_value=False,
                    )
                    
                    # Scenario visualization type selector
                    viz_type = gr.Radio(
                        choices=["Radar Chart", "Heatmap", "3D Graph", "Stream"],
                        value="Radar Chart",
                        label="📊 Visualization Type",
                        info="Choose how to visualize the metrics"
                    )
                    
                    # Enhanced metrics display
                    metrics_display = gr.JSON(
                        label="📊 Current Metrics",
                        value={},
                    )
                    
                    # Business impact with color coding
                    impact_display = gr.JSON(
                        label="💰 Business Impact Analysis",
                        value={},
                    )
                    
                    # Action buttons with loading states
                    with gr.Row():
                        with gr.Column(scale=1):
                            oss_action_btn = gr.Button(
                                "🤖 OSS: Analyze & Recommend", 
                                variant="secondary",
                                elem_id="oss-btn"
                            )
                            oss_loading = gr.HTML("", visible=False)
                        
                        with gr.Column(scale=1):
                            enterprise_action_btn = gr.Button(
                                "🚀 Enterprise: Execute Healing", 
                                variant="primary",
                                elem_id="enterprise-btn"
                            )
                            enterprise_loading = gr.HTML("", visible=False)
                    
                    # License and mode with tooltips
                    with gr.Accordion("⚙️ Enterprise Configuration", open=False):
                        license_input = gr.Textbox(
                            label="🔑 Enterprise License Key",
                            value="ARF-ENT-DEMO-2024",
                            info="Demo license - real enterprise requires purchase",
                            placeholder="Enter your license key..."
                        )
                        
                        execution_mode = gr.Radio(
                            choices=["autonomous", "approval"],
                            value="autonomous",
                            label="⚙️ Execution Mode",
                            info="How to execute the healing action"
                        )
                        
                        gr.HTML("""
                        <div style="background: #E3F2FD; padding: 10px; border-radius: 5px; margin-top: 10px;">
                            <small>💡 <strong>Autonomous:</strong> ARF executes automatically</small><br>
                            <small>💡 <strong>Approval:</strong> Requires human approval before execution</small>
                        </div>
                        """)
                
                with gr.Column(scale=2):
                    # Enhanced results display with tabs
                    with gr.Tabs():
                        with gr.TabItem("🎯 Execution Results"):
                            result_display = gr.JSON(
                                label="",
                                value={},
                                elem_id="results-json"
                            )
                        
                        with gr.TabItem("📈 Performance Analysis"):
                            performance_chart = gr.Plot(
                                label="Performance Radar Chart",
                            )
                        
                        with gr.TabItem("🔥 Incident Heatmap"):
                            incident_heatmap = gr.Plot(
                                label="Incident Severity Heatmap",
                            )
                    
                    # Enhanced RAG Graph visualization
                    with gr.Row():
                        with gr.Column(scale=2):
                            rag_graph = gr.Plot(
                                label="🧠 RAG Graph Memory Visualization",
                                elem_id="rag-graph"
                            )
                        
                        with gr.Column(scale=1):
                            # RAG Graph controls
                            gr.Markdown("#### 🎛️ Graph Controls")
                            graph_type = gr.Radio(
                                choices=["2D View", "3D View", "Network View"],
                                value="2D View",
                                label="View Type"
                            )
                            animate_graph = gr.Checkbox(
                                label="🎬 Enable Animation",
                                value=True
                            )
                            refresh_graph = gr.Button(
                                "🔄 Refresh Graph",
                                size="sm"
                            )
                    
                    # Predictive Timeline
                    predictive_timeline = gr.Plot(
                        label="🔮 Predictive Analytics Timeline",
                        elem_id="predictive-timeline"
                    )
            
            # Function to update scenario with enhanced visualization
            def update_scenario_enhanced(scenario_name, viz_type, session_state):
                scenario = ENTERPRISE_SCENARIOS.get(scenario_name, {})
                session_state["current_scenario"] = scenario_name
                
                # Add to RAG graph
                incident_id = rag_visualizer.add_incident(
                    component=scenario.get("component", "unknown"),
                    severity="critical" if scenario.get("business_impact", {}).get("revenue_at_risk", 0) > 1000000 else "high"
                )
                
                # Add prediction
                if "prediction" in scenario:
                    predictive_viz.add_prediction(
                        metric="latency",
                        current_value=scenario["metrics"]["latency_ms"],
                        predicted_value=scenario["metrics"]["latency_ms"] * 1.3,
                        time_to_threshold=8.5 if "Black Friday" in scenario_name else None
                    )
                
                # Select visualization based on type
                if viz_type == "Radar Chart":
                    viz_fig = viz_engine.create_animated_radar_chart(
                        scenario.get("metrics", {}),
                        f"Performance Radar - {scenario_name}"
                    )
                elif viz_type == "Heatmap":
                    viz_fig = viz_engine.create_heatmap_timeline([scenario])
                elif viz_type == "3D Graph":
                    viz_fig = viz_engine.create_3d_rag_graph(
                        rag_visualizer.incidents,
                        rag_visualizer.outcomes,
                        rag_visualizer.edges
                    )
                else:  # Stream
                    viz_fig = viz_engine.create_real_time_metrics_stream()
                
                # Store in cache
                session_state["visualization_cache"][scenario_name] = viz_fig
                
                return {
                    metrics_display: scenario.get("metrics", {}),
                    impact_display: business_calc.calculate_impact(scenario.get("business_impact", {})),
                    rag_graph: rag_visualizer.get_graph_figure(),
                    predictive_timeline: predictive_viz.get_predictive_timeline(),
                    performance_chart: viz_fig,
                    incident_heatmap: viz_engine.create_heatmap_timeline([scenario]),
                    session_state: session_state,
                }
            
            # Connect events
            scenario_selector.change(
                fn=update_scenario_enhanced,
                inputs=[scenario_selector, viz_type, session_state],
                outputs=[metrics_display, impact_display, rag_graph, predictive_timeline, 
                        performance_chart, incident_heatmap, session_state]
            )
            
            viz_type.change(
                fn=lambda scenario, viz_type, state: update_scenario_enhanced(scenario, viz_type, state),
                inputs=[scenario_selector, viz_type, session_state],
                outputs=[performance_chart, session_state]
            )
        
        # ================================================================
        # ENHANCED LEARNING ENGINE TAB
        # ================================================================
        with gr.TabItem("🧠 Learning Engine", elem_id="learning-tab"):
            gr.Markdown("""
            ## 🧠 RAG Graph Learning Engine
            **Watch ARF learn from every incident and outcome**
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    # Enhanced learning stats
                    learning_stats = gr.JSON(
                        label="📊 Learning Statistics",
                        value=rag_visualizer.get_stats(),
                    )
                    
                    # Learning controls
                    with gr.Accordion("🎓 Learning Controls", open=True):
                        simulate_learning_btn = gr.Button(
                            "🎓 Simulate Learning Cycle", 
                            variant="primary",
                            elem_id="simulate-learning"
                        )
                        
                        learning_rate = gr.Slider(
                            minimum=1,
                            maximum=10,
                            value=3,
                            step=1,
                            label="Learning Cycles",
                            info="Number of incidents to simulate"
                        )
                        
                        success_probability = gr.Slider(
                            minimum=0.1,
                            maximum=1.0,
                            value=0.8,
                            step=0.1,
                            label="Success Probability",
                            info="Probability of successful resolution"
                        )
                    
                    # Export section
                    with gr.Accordion("📤 Export Knowledge", open=False):
                        export_format = gr.Radio(
                            choices=["JSON", "CSV", "Graph Image"],
                            value="JSON",
                            label="Export Format"
                        )
                        
                        export_btn = gr.Button(
                            "📤 Export Learned Patterns", 
                            variant="secondary"
                        )
                        
                        export_status = gr.HTML(
                            "<div style='padding: 10px; background: #E8F5E9; border-radius: 5px;'>"
                            "✅ Ready to export</div>",
                            visible=True
                        )
                
                with gr.Column(scale=2):
                    # Enhanced RAG Graph visualization
                    with gr.Tabs():
                        with gr.TabItem("🔗 2D Knowledge Graph"):
                            learning_graph_2d = gr.Plot(
                                label="",
                            )
                        
                        with gr.TabItem("🌐 3D Knowledge Graph"):
                            learning_graph_3d = gr.Plot(
                                label="",
                            )
                        
                        with gr.TabItem("📊 Learning Progress"):
                            learning_progress = gr.Plot(
                                label="",
                            )
            
            # Update learning graphs
            def update_learning_graphs():
                return {
                    learning_graph_2d: rag_visualizer.get_graph_figure(),
                    learning_graph_3d: viz_engine.create_3d_rag_graph(
                        rag_visualizer.incidents,
                        rag_visualizer.outcomes,
                        rag_visualizer.edges
                    ),
                    learning_stats: rag_visualizer.get_stats(),
                    learning_progress: viz_engine.create_real_time_metrics_stream(),
                }
            
            # Simulate enhanced learning
            def simulate_enhanced_learning(cycles, success_prob, session_state):
                components = ["payment-service", "database", "api-service", "cache", "auth-service",
                            "cdn-service", "analytics-service", "queue-service"]
                actions = ["scale_out", "restart_container", "rollback", "circuit_breaker",
                          "failover", "load_balance", "cache_clear", "connection_pool"]
                
                for _ in range(cycles):
                    component = random.choice(components)
                    incident_id = rag_visualizer.add_incident(
                        component=component,
                        severity=random.choice(["low", "medium", "high", "critical"])
                    )
                    
                    rag_visualizer.add_outcome(
                        incident_id=incident_id,
                        success=random.random() < success_prob,
                        action=random.choice(actions)
                    )
                
                # Record in session
                session_manager.record_action(
                    session_state["session_id"],
                    "simulate_learning",
                    {"cycles": cycles, "success_probability": success_prob}
                )
                
                return update_learning_graphs()
            
            # Connect events
            simulate_learning_btn.click(
                fn=simulate_enhanced_learning,
                inputs=[learning_rate, success_probability, session_state],
                outputs=[learning_graph_2d, learning_graph_3d, learning_stats, learning_progress]
            )
            
            refresh_graph.click(
                fn=update_learning_graphs,
                outputs=[learning_graph_2d, learning_graph_3d, learning_stats, learning_progress]
            )
        
        # ================================================================
        # ENHANCED COMPLIANCE AUDITOR TAB
        # ================================================================
        with gr.TabItem("📝 Compliance Auditor", elem_id="compliance-tab"):
            gr.Markdown("""
            ## 📝 Automated Compliance & Audit Trails
            **Enterprise-only: Generate SOC2/GDPR/HIPAA compliance reports in seconds**
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    # Compliance configuration
                    compliance_standard = gr.Dropdown(
                        choices=["SOC2", "GDPR", "HIPAA", "ISO27001", "PCI-DSS"],
                        value="SOC2",
                        label="📋 Compliance Standard",
                        info="Select compliance standard"
                    )
                    
                    compliance_license = gr.Textbox(
                        label="🔑 Enterprise License Required",
                        value="ARF-ENT-COMPLIANCE",
                        interactive=True,
                        placeholder="Enter compliance license key..."
                    )
                    
                    # Export options
                    with gr.Accordion("📤 Export Options", open=False):
                        report_format = gr.Radio(
                            choices=["HTML Report", "JSON", "PDF Summary"],
                            value="HTML Report",
                            label="Report Format"
                        )
                        
                        include_audit_trail = gr.Checkbox(
                            label="Include Audit Trail",
                            value=True
                        )
                        
                        generate_report_btn = gr.Button(
                            "⚡ Generate & Export Report", 
                            variant="primary",
                            elem_id="generate-report"
                        )
                    
                    # Audit trail viewer
                    gr.Markdown("### 📜 Live Audit Trail")
                    audit_trail = gr.Dataframe(
                        label="",
                        headers=["Time", "Action", "Component", "User", "Status", "Details"],
                        value=[],
                    )
                
                with gr.Column(scale=2):
                    # Report display with tabs
                    with gr.Tabs():
                        with gr.TabItem("📄 Compliance Report"):
                            compliance_report = gr.JSON(
                                label="",
                                value={},
                            )
                        
                        with gr.TabItem("📊 Compliance Dashboard"):
                            compliance_dashboard = gr.Plot(
                                label="Compliance Metrics Dashboard",
                            )
                        
                        with gr.TabItem("🔍 Detailed Findings"):
                            findings_display = gr.HTML(
                                label="",
                                value="<div style='padding: 20px;'>Select a standard and generate report</div>"
                            )
                    
                    # Report actions
                    with gr.Row():
                        preview_report = gr.Button(
                            "👁️ Preview Report",
                            variant="secondary",
                            size="sm"
                        )
                        download_report = gr.Button(
                            "📥 Download Report",
                            variant="secondary",
                            size="sm"
                        )
                        share_report = gr.Button(
                            "🔗 Share Report",
                            variant="secondary",
                            size="sm"
                        )
        
        # ================================================================
        # ENHANCED ROI CALCULATOR TAB
        # ================================================================
        with gr.TabItem("💰 ROI Calculator", elem_id="roi-tab"):
            gr.Markdown("""
            ## 💰 Enterprise ROI Calculator
            **Calculate your potential savings with ARF Enterprise**
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    # Inputs with tooltips
                    gr.Markdown("### 📝 Input Your Business Metrics")
                    
                    monthly_revenue = gr.Number(
                        value=1000000,
                        label="Monthly Revenue ($)",
                        info="Your company's monthly revenue",
                        minimum=10000,
                        maximum=1000000000,
                        step=10000
                    )
                    
                    monthly_incidents = gr.Slider(
                        minimum=1,
                        maximum=100,
                        value=20,
                        label="Monthly Incidents",
                        info="Reliability incidents per month",
                        step=1
                    )
                    
                    team_size = gr.Slider(
                        minimum=1,
                        maximum=20,
                        value=3,
                        label="SRE/DevOps Team Size",
                        info="Engineers handling incidents",
                        step=1
                    )
                    
                    avg_incident_cost = gr.Slider(
                        minimum=100,
                        maximum=10000,
                        value=1500,
                        label="Average Incident Cost ($)",
                        info="Revenue loss + engineer time per incident",
                        step=100
                    )
                    
                    with gr.Accordion("⚙️ Advanced Settings", open=False):
                        engineer_hourly_rate = gr.Number(
                            value=100,
                            label="Engineer Hourly Rate ($)",
                            info="Average hourly rate of engineers"
                        )
                        
                        implementation_timeline = gr.Slider(
                            minimum=1,
                            maximum=12,
                            value=3,
                            label="Implementation Timeline (months)",
                            info="Time to fully implement ARF"
                        )
                    
                    calculate_roi_btn = gr.Button(
                        "📈 Calculate ROI", 
                        variant="primary",
                        size="lg"
                    )
                
                with gr.Column(scale=2):
                    # Enhanced results display
                    with gr.Tabs():
                        with gr.TabItem("📊 ROI Results"):
                            roi_results = gr.JSON(
                                label="",
                                value={},
                            )
                        
                        with gr.TabItem("📈 Visualization"):
                            roi_chart = gr.Plot(
                                label="",
                            )
                        
                        with gr.TabItem("📋 Detailed Breakdown"):
                            roi_breakdown = gr.Dataframe(
                                label="Cost-Benefit Analysis",
                                headers=["Category", "Without ARF", "With ARF", "Savings", "ROI Impact"],
                                value=[],
                            )
                    
                    # Export section
                    gr.Markdown("### 📤 Export ROI Analysis")
                    with gr.Row():
                        export_roi_html = gr.Button(
                            "🌐 Export as HTML",
                            variant="secondary"
                        )
                        export_roi_csv = gr.Button(
                            "📊 Export as CSV",
                            variant="secondary"
                        )
                        export_roi_pdf = gr.Button(
                            "📄 Export as PDF",
                            variant="secondary"
                        )
                    
                    export_status = gr.HTML(
                        "<div style='padding: 10px; background: #FFF3E0; border-radius: 5px;'>"
                        "📝 Ready for export</div>",
                        visible=True
                    )
        
        # ================================================================
        # ENHANCED ANALYTICS & EXPORT TAB
        # ================================================================
        with gr.TabItem("📈 Analytics & Export", elem_id="analytics-section"):
            gr.Markdown("""
            ## 📈 Advanced Analytics & Export Hub
            **Deep dive into performance metrics and export professional reports**
            """)
            
            with gr.Row():
                with gr.Column(scale=1):
                    # Analytics controls
                    gr.Markdown("### 📊 Analytics Controls")
                    
                    analytics_timeframe = gr.Dropdown(
                        choices=["Last Hour", "Today", "Last 7 Days", "Last 30 Days", "All Time"],
                        value="Today",
                        label="Timeframe"
                    )
                    
                    analytics_metric = gr.Dropdown(
                        choices=["Revenue Protected", "Incidents Handled", "Auto-Heal Rate", 
                                "MTTR Improvement", "ROI", "Compliance Score"],
                        value="Revenue Protected",
                        label="Primary Metric"
                    )
                    
                    refresh_analytics = gr.Button(
                        "🔄 Refresh Analytics",
                        variant="primary"
                    )
                    
                    # Export all data
                    gr.Markdown("### 📤 Bulk Export")
                    with gr.Accordion("Export All Session Data", open=False):
                        export_all_format = gr.Radio(
                            choices=["JSON", "CSV", "HTML Report"],
                            value="JSON",
                            label="Export Format"
                        )
                        
                        export_all_btn = gr.Button(
                            "💾 Export All Data",
                            variant="secondary"
                        )
                
                with gr.Column(scale=2):
                    # Historical trends
                    gr.Markdown("### 📈 Historical Performance Trends")
                    historical_trends = gr.Plot(
                        label="",
                    )
                    
                    # Session analytics
                    gr.Markdown("### 👤 Session Analytics")
                    session_analytics = gr.JSON(
                        label="",
                        value={},
                    )
            
            # Export hub
            gr.Markdown("### 🚀 Export Hub", elem_id="export-section")
            with gr.Row():
                with gr.Column(scale=1):
                    export_type = gr.Dropdown(
                        choices=["ROI Report", "Compliance Report", "Incident Analysis", 
                                "Performance Dashboard", "Executive Summary"],
                        value="ROI Report",
                        label="Report Type"
                    )
                    
                    export_customize = gr.CheckboxGroup(
                        choices=["Include Charts", "Include Raw Data", "Add Watermark", 
                                "Password Protect", "Brand Customization"],
                        value=["Include Charts"],
                        label="Customization Options"
                    )
                
                with gr.Column(scale=2):
                    export_preview = gr.HTML(
                        "<div style='padding: 40px; text-align: center; background: #f5f5f5; border-radius: 10px;'>"
                        "<h3>🚀 Export Preview</h3>"
                        "<p>Select report type and customization options</p>"
                        "</div>"
                    )
                    
                    with gr.Row():
                        generate_export = gr.Button(
                            "⚡ Generate Export",
                            variant="primary"
                        )
                        preview_export = gr.Button(
                            "👁️ Preview",
                            variant="secondary"
                        )
                        clear_exports = gr.Button(
                            "🗑️ Clear",
                            variant="secondary"
                        )
        
        # ================================================================
        # MOBILE RESPONSIVE ELEMENTS
        # ================================================================
        gr.Markdown("""
        <div class="mobile-only" style="display: none; background: #E3F2FD; padding: 15px; border-radius: 10px; margin: 20px 0;">
            <h4>📱 Mobile Tips</h4>
            <p>• Use landscape mode for better visualization</p>
            <p>• Tap charts to interact</p>
            <p>• Swipe left/right between tabs</p>
        </div>
        
        <style>
            @media (max-width: 768px) {
                .mobile-only { display: block !important; }
                .gradio-container { padding: 10px; }
                .tab-nav { overflow-x: auto; }
            }
        </style>
        """)
        
        # ================================================================
        # ENHANCED FOOTER WITH EXPORT LINKS
        # ================================================================
        gr.Markdown("""
        ---
        
        <div style="background: #f8f9fa; padding: 20px; border-radius: 10px; margin: 20px 0;">
            <div style="display: flex; justify-content: space-between; flex-wrap: wrap;">
                <div>
                    <h4>🚀 Ready to transform your reliability operations?</h4>
                    <p><strong>Capability Comparison:</strong></p>
                    <table style="width: 100%;">
                        <tr><th>Capability</th><th>OSS Edition</th><th>Enterprise Edition</th></tr>
                        <tr><td>Execution</td><td>❌ Advisory only</td><td>✅ Autonomous + Approval</td></tr>
                        <tr><td>Learning</td><td>❌ No learning</td><td>✅ Continuous learning engine</td></tr>
                        <tr><td>Compliance</td><td>❌ No audit trails</td><td>✅ SOC2/GDPR/HIPAA compliant</td></tr>
                        <tr><td>Storage</td><td>⚠️ In-memory only</td><td>✅ Persistent (Neo4j + PostgreSQL)</td></tr>
                        <tr><td>Support</td><td>❌ Community</td><td>✅ 24/7 Enterprise support</td></tr>
                        <tr><td>ROI</td><td>❌ None</td><td>✅ <strong>5.2× average first year ROI</strong></td></tr>
                    </table>
                </div>
                
                <div style="min-width: 250px; margin-top: 20px;">
                    <h4>📞 Contact & Resources</h4>
                    <p>📧 <strong>Email:</strong> enterprise@petterjuan.com</p>
                    <p>🌐 <strong>Website:</strong> <a href="https://arf.dev" target="_blank">https://arf.dev</a></p>
                    <p>📚 <strong>Documentation:</strong> <a href="https://docs.arf.dev" target="_blank">https://docs.arf.dev</a></p>
                    <p>💻 <strong>GitHub:</strong> <a href="https://github.com/petterjuan/agentic-reliability-framework" target="_blank">petterjuan/agentic-reliability-framework</a></p>
                    <p>📊 <strong>Demo Session ID:</strong> <code>""" + session_id[-8:] + """</code></p>
                </div>
            </div>
        </div>
        
        <div style="text-align: center; padding: 15px; background: #2c3e50; color: white; border-radius: 5px; margin-top: 20px;">
            <p style="margin: 0;">🚀 ARF Ultimate Investor Demo v3.3.7 | Enhanced with Professional Analytics & Export Features</p>
            <p style="margin: 5px 0 0 0; font-size: 12px;">Built with ❤️ using Gradio & Plotly | Session started at """ + 
            datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + """</p>
        </div>
        """)
    
    return demo

# ============================================================================
# MAIN ENTRY POINT
# ============================================================================

def main():
    """Main entry point"""
    logging.basicConfig(level=logging.INFO)
    logger = logging.getLogger(__name__)
    
    logger.info("=" * 80)
    logger.info("🚀 Starting ARF Ultimate Investor Demo v3.3.7")
    logger.info("=" * 80)
    
    demo = create_enhanced_demo()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        show_error=True,
        theme="soft",
        favicon_path=None,
    )

if __name__ == "__main__":
    main()