app.py

"""
🚀 ARF Ultimate Investor Demo v3.8.0 - ENTERPRISE EDITION
Main entry point with comprehensive 5-tab interface
Uses actual ARF OSS v3.3.6 framework
"""

import logging
import sys
import traceback
import json
import datetime
from pathlib import Path
from typing import Dict, List, Any, Optional, Tuple

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout),
        logging.FileHandler('arf_demo.log')
    ]
)
logger = logging.getLogger(__name__)

# Add parent directory to path for module imports
sys.path.insert(0, str(Path(__file__).parent))

try:
    # Import ARF OSS framework (actual package)
    try:
        from agentic_reliability_framework import __version__ as arf_version
        from agentic_reliability_framework.arf_core.models.healing_intent import (
            HealingIntent, create_scale_out_intent, create_rollback_intent
        )
        from agentic_reliability_framework.arf_core.engine.simple_mcp_client import OSSMCPClient
        from agentic_reliability_framework.engine.mcp_server import MCPServer, MCPMode
        
        ARF_OSS_AVAILABLE = True
        OSS_VERSION = arf_version
        logger.info(f"✅ Successfully imported ARF OSS v{OSS_VERSION}")
        
        # Create OSS client instance
        oss_client = OSSMCPClient()
        
    except ImportError as e:
        logger.warning(f"Failed to import ARF OSS: {e}")
        ARF_OSS_AVAILABLE = False
        OSS_VERSION = "3.3.6 (Mock)"
        
        # Mock classes for demo
        class HealingIntent:
            def __init__(self, action: str, component: str, parameters: Dict, **kwargs):
                self.action = action
                self.component = component
                self.parameters = parameters
                self.justification = kwargs.get('justification', '')
                self.confidence = kwargs.get('confidence', 0.85)
                self.similar_incidents = kwargs.get('similar_incidents', [])
                self.rag_similarity_score = kwargs.get('rag_similarity_score')
                
            def to_enterprise_request(self) -> Dict:
                return {
                    'action': self.action,
                    'component': self.component,
                    'parameters': self.parameters,
                    'justification': self.justification,
                    'confidence': self.confidence,
                    'requires_enterprise': True,
                    'oss_metadata': {
                        'similar_incidents_count': len(self.similar_incidents),
                        'rag_used': self.rag_similarity_score is not None
                    }
                }
                
            def mark_as_oss_advisory(self):
                return self
        
        class OSSMCPClient:
            def __init__(self):
                self.mode = "advisory"
                
            async def analyze_and_recommend(self, tool_name: str, component: str, 
                                          parameters: Dict, context: Optional[Dict] = None) -> HealingIntent:
                # Simulate RAG similarity search
                similar_incidents = [
                    {"id": "inc_001", "similarity": 0.78, "resolution": "scaled_out", "component": "redis"},
                    {"id": "inc_045", "similarity": 0.65, "resolution": "restarted", "component": "database"},
                    {"id": "inc_089", "similarity": 0.59, "resolution": "circuit_breaker", "component": "api"}
                ]
                
                return HealingIntent(
                    action=tool_name,
                    component=component,
                    parameters=parameters,
                    justification=f"OSS Analysis: Based on {len(similar_incidents)} similar incidents, recommend {tool_name} for {component}",
                    confidence=0.82 + (len(similar_incidents) * 0.01),
                    similar_incidents=similar_incidents,
                    rag_similarity_score=0.72
                )
        
        oss_client = OSSMCPClient()
        
        MCPMode = type('MCPMode', (), {
            'ADVISORY': 'advisory',
            'APPROVAL': 'approval', 
            'AUTONOMOUS': 'autonomous'
        })
    
    # Import Gradio and visualization libraries
    import gradio as gr
    import plotly.graph_objects as go
    import plotly.express as px
    import pandas as pd
    import numpy as np
    from plotly.subplots import make_subplots
    
    # ===========================================
    # COMPREHENSIVE DATA MODELS
    # ===========================================
    
    class AuditTrailManager:
        """Manage audit trail and execution history"""
        
        def __init__(self):
            self.execution_history = []
            self.incident_history = []
            self._initialize_sample_data()
        
        def _initialize_sample_data(self):
            """Initialize with sample historical data"""
            base_time = datetime.datetime.now() - datetime.timedelta(hours=2)
            
            # Sample execution history
            sample_executions = [
                self._create_execution_entry(
                    base_time - datetime.timedelta(minutes=90),
                    "Cache Miss Storm", 4, 7200, "✅ Executed", "Auto-scaled cache"
                ),
                self._create_execution_entry(
                    base_time - datetime.timedelta(minutes=75),
                    "Memory Leak", 3, 5200, "✅ Executed", "Fixed memory leak"
                ),
                self._create_execution_entry(
                    base_time - datetime.timedelta(minutes=60),
                    "API Rate Limit", 4, 2800, "✅ Executed", "Increased rate limits"
                ),
                self._create_execution_entry(
                    base_time - datetime.timedelta(minutes=45),
                    "DB Connection Pool", 4, 3800, "✅ Executed", "Scaled connection pool"
                ),
            ]
            
            self.execution_history = sample_executions
            
            # Sample incident history
            services = ["API Gateway", "Database", "Redis Cache", "Auth Service", "Payment Service"]
            
            for i in range(10):
                incident_time = base_time - datetime.timedelta(minutes=i * 15)
                self.incident_history.append({
                    "timestamp": incident_time,
                    "time_str": incident_time.strftime("%H:%M"),
                    "service": services[i % len(services)],
                    "type": "Cache Miss Storm" if i % 3 == 0 else "Memory Leak",
                    "severity": 3 if i % 3 == 0 else 2,
                    "description": f"High latency on {services[i % len(services)]}",
                    "id": f"inc_{i:03d}"
                })
        
        def _create_execution_entry(self, timestamp, scenario, actions, savings, status, details):
            """Create an execution history entry"""
            return {
                "timestamp": timestamp,
                "time_str": timestamp.strftime("%H:%M"),
                "scenario": scenario,
                "actions": str(actions),
                "savings": f"${savings:,}",
                "status": status,
                "details": details,
                "id": f"exec_{len(self.execution_history):03d}"
            }
        
        def add_execution(self, scenario: str, actions: List[str], 
                         savings: int, approval_required: bool, details: str = ""):
            """Add new execution to history"""
            entry = self._create_execution_entry(
                datetime.datetime.now(),
                scenario,
                len(actions),
                savings,
                "✅ Approved & Executed" if approval_required else "✅ Auto-Executed",
                details
            )
            self.execution_history.insert(0, entry)
            return entry
        
        def add_incident(self, scenario_name: str, metrics: Dict):
            """Add incident to history"""
            entry = {
                "timestamp": datetime.datetime.now(),
                "time_str": datetime.datetime.now().strftime("%H:%M"),
                "service": "Demo System",
                "type": scenario_name,
                "severity": 3,
                "description": f"Demo incident: {scenario_name}",
                "id": f"inc_{len(self.incident_history):03d}"
            }
            self.incident_history.insert(0, entry)
            return entry
        
        def get_execution_history_table(self, limit: int = 10) -> List[List]:
            """Get execution history for table display"""
            return [
                [entry["time_str"], entry["scenario"], entry["actions"], 
                 entry["status"], entry["savings"], entry["details"]]
                for entry in self.execution_history[:limit]
            ]
        
        def get_incident_history_table(self, limit: int = 15) -> List[List]:
            """Get incident history for table display"""
            return [
                [entry["time_str"], entry["service"], entry["type"], 
                 f"{entry['severity']}/3", entry["description"]]
                for entry in self.incident_history[:limit]
            ]
        
        def export_audit_trail(self) -> str:
            """Export audit trail as JSON"""
            total_savings = sum(
                int(e["savings"].replace("$", "").replace(",", ""))
                for e in self.execution_history
                if "$" in e["savings"]
            )
            
            return json.dumps({
                "executions": self.execution_history,
                "incidents": self.incident_history,
                "exported_at": datetime.datetime.now().isoformat(),
                "total_executions": len(self.execution_history),
                "total_incidents": len(self.incident_history),
                "total_savings": total_savings,
                "arf_version": OSS_VERSION,
                "oss_available": ARF_OSS_AVAILABLE
            }, indent=2, default=str)
    
    # ===========================================
    # INCIDENT SCENARIOS
    # ===========================================
    
    INCIDENT_SCENARIOS = {
        "Cache Miss Storm": {
            "description": "Redis cluster experiencing 80% cache miss rate causing database overload",
            "severity": "CRITICAL",
            "metrics": {
                "Cache Hit Rate": "18.5% (Critical)",
                "Database Load": "92% (Overloaded)", 
                "Response Time": "1850ms (Slow)",
                "Affected Users": "45,000",
                "Eviction Rate": "125/sec"
            },
            "impact": {
                "Revenue Loss": "$8,500/hour",
                "Page Load Time": "+300%",
                "Users Impacted": "45,000",
                "SLA Violation": "Yes",
                "Customer Sat": "-40%"
            },
            "oss_analysis": {
                "status": "✅ ARF OSS Analysis Complete",
                "recommendations": [
                    "Increase Redis cache memory allocation",
                    "Implement cache warming strategy",
                    "Optimize key patterns (TTL adjustments)",
                    "Add circuit breaker for database fallback",
                    "Deploy monitoring for cache hit rate trends"
                ],
                "estimated_time": "60+ minutes",
                "engineers_needed": "2-3 SREs + 1 DBA",
                "manual_effort": "High",
                "total_cost": "$8,500",
                "healing_intent": "scale_out_cache"
            },
            "enterprise_results": {
                "actions_completed": [
                    "✅ Auto-scaled Redis cluster: 4GB → 8GB",
                    "✅ Deployed intelligent cache warming service",
                    "✅ Optimized 12 key patterns with ML recommendations",
                    "✅ Implemented circuit breaker with 95% success rate",
                    "✅ Validated recovery with automated testing"
                ],
                "metrics_improvement": {
                    "Cache Hit Rate": "18.5% → 72%",
                    "Response Time": "1850ms → 450ms",
                    "Database Load": "92% → 45%",
                    "Throughput": "1250 → 2450 req/sec"
                },
                "business_impact": {
                    "Recovery Time": "60 min → 12 min",
                    "Cost Saved": "$7,200",
                    "Users Impacted": "45,000 → 0",
                    "Revenue Protected": "$1,700",
                    "MTTR Improvement": "80% reduction"
                }
            }
        },
        "Database Connection Pool Exhaustion": {
            "description": "Database connection pool exhausted causing API timeouts and user failures",
            "severity": "HIGH",
            "metrics": {
                "Active Connections": "98/100 (Critical)",
                "API Latency": "2450ms",
                "Error Rate": "15.2%",
                "Queue Depth": "1250",
                "Connection Wait": "45s"
            },
            "impact": {
                "Revenue Loss": "$4,200/hour",
                "Affected Services": "API Gateway, User Service, Payment",
                "SLA Violation": "Yes",
                "Partner Impact": "3 external APIs"
            }
        },
        "Memory Leak in Production": {
            "description": "Java service memory leak causing gradual performance degradation",
            "severity": "HIGH",
            "metrics": {
                "Memory Usage": "96% (Critical)",
                "GC Pause Time": "4500ms",
                "Error Rate": "28.5%",
                "Restart Frequency": "12/hour",
                "Heap Fragmentation": "42%"
            },
            "impact": {
                "Revenue Loss": "$5,500/hour",
                "Session Loss": "8,500 users",
                "Customer Impact": "High",
                "Support Tickets": "+300%"
            }
        }
    }
    
    # ===========================================
    # BUSINESS LOGIC
    # ===========================================
    
    class BusinessLogic:
        """Business logic for the demo"""
        
        def __init__(self, audit_manager: AuditTrailManager, oss_client):
            self.audit_manager = audit_manager
            self.oss_client = oss_client
            self.license_info = {
                "valid": True,
                "customer_name": "Demo Enterprise Corp",
                "customer_email": "demo@enterprise.com",
                "tier": "ENTERPRISE",
                "expires_at": "2024-12-31T23:59:59",
                "features": ["autonomous_healing", "compliance", "audit_trail", "multi_cloud"],
                "max_services": 100,
                "max_incidents_per_month": 1000,
                "status": "✅ Active"
            }
        
        async def run_oss_analysis(self, scenario_name: str) -> Dict:
            """Run OSS analysis using actual ARF framework"""
            scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
            
            # Create HealingIntent using OSS client
            healing_intent = await self.oss_client.analyze_and_recommend(
                tool_name="scale_out",
                component="redis_cache",
                parameters={"scale_factor": 2.0, "resource_type": "memory"},
                context={
                    "incident_type": scenario_name,
                    "metrics": scenario.get("metrics", {}),
                    "severity": scenario.get("severity", "HIGH")
                }
            )
            
            # Build analysis response
            analysis = scenario.get("oss_analysis", {}).copy()
            analysis["healing_intent"] = healing_intent.to_enterprise_request()
            analysis["arf_oss_version"] = OSS_VERSION
            analysis["analysis_timestamp"] = datetime.datetime.now().isoformat()
            
            # Add to incident history
            self.audit_manager.add_incident(scenario_name, scenario.get("metrics", {}))
            
            return analysis
        
        def execute_enterprise_healing(self, scenario_name: str, approval_required: bool) -> Tuple[Dict, Dict]:
            """Execute enterprise healing"""
            scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
            results = scenario.get("enterprise_results", {}).copy()
            
            # Add enterprise context
            results["enterprise_context"] = {
                "approval_required": approval_required,
                "execution_mode": "autonomous" if not approval_required else "approval",
                "timestamp": datetime.datetime.now().isoformat(),
                "license_tier": self.license_info["tier"]
            }
            
            # Calculate savings
            savings = 7200 if scenario_name == "Cache Miss Storm" else 4200
            
            # Add to audit trail
            self.audit_manager.add_execution(
                scenario_name,
                results.get("actions_completed", []),
                savings,
                approval_required,
                f"Healed {scenario_name} incident"
            )
            
            # Create approval HTML
            approval_html = self._create_approval_html(scenario_name, approval_required)
            
            return approval_html, results
        
        def _create_approval_html(self, scenario_name: str, approval_required: bool) -> str:
            """Create approval workflow HTML"""
            if approval_required:
                return f"""
                <div style='padding: 20px; background: linear-gradient(135deg, #f8f9fa 0%, #e9ecef 100%); border-radius: 10px; border-left: 4px solid #007bff; margin: 10px 0;'>
                    <div style='display: flex; align-items: center; gap: 10px; margin-bottom: 15px;'>
                        <div style='background: #007bff; color: white; width: 32px; height: 32px; border-radius: 50%; display: flex; align-items: center; justify-content: center; font-weight: bold;'>
                            🛡️
                        </div>
                        <h4 style='margin: 0; color: #1a365d;'>Approval Required</h4>
                    </div>
                    <div style='background: white; padding: 15px; border-radius: 8px; margin-bottom: 10px;'>
                        <div style='display: grid; grid-template-columns: repeat(2, 1fr); gap: 10px;'>
                            <div><strong>Action:</strong> Scale resources for {scenario_name}</div>
                            <div><strong>Risk Level:</strong> <span style='color: #28a745;'>Low</span></div>
                            <div><strong>Blast Radius:</strong> Limited to affected service</div>
                            <div><strong>Auto-rollback:</strong> Available</div>
                        </div>
                    </div>
                    <div style='display: flex; justify-content: space-between; align-items: center;'>
                        <div>
                            <div style='font-size: 0.9rem; color: #6c757d;'>Status</div>
                            <div style='font-weight: bold; color: #28a745;'>✅ Approved & Executed</div>
                        </div>
                        <div style='font-size: 0.85rem; color: #6c757d;'>
                            {datetime.datetime.now().strftime("%H:%M:%S")}
                        </div>
                    </div>
                </div>
                """
            else:
                return f"""
                <div style='padding: 20px; background: linear-gradient(135deg, #e8f5e8 0%, #d4edda 100%); border-radius: 10px; border-left: 4px solid #28a745; margin: 10px 0;'>
                    <div style='display: flex; align-items: center; gap: 10px; margin-bottom: 15px;'>
                        <div style='background: #28a745; color: white; width: 32px; height: 32px; border-radius: 50%; display: flex; align-items: center; justify-content: center; font-weight: bold;'>
                            ⚡
                        </div>
                        <h4 style='margin: 0; color: #1a365d;'>Auto-Executed</h4>
                    </div>
                    <div style='background: white; padding: 15px; border-radius: 8px; margin-bottom: 10px;'>
                        <div style='display: grid; grid-template-columns: repeat(2, 1fr); gap: 10px;'>
                            <div><strong>Action:</strong> Autonomous healing for {scenario_name}</div>
                            <div><strong>Mode:</strong> Fully autonomous</div>
                            <div><strong>Guardrails:</strong> Safety limits active</div>
                            <div><strong>Rollback:</strong> Ready if needed</div>
                        </div>
                    </div>
                    <div style='display: flex; justify-content: space-between; align-items: center;'>
                        <div>
                            <div style='font-size: 0.9rem; color: #6c757d;'>Status</div>
                            <div style='font-weight: bold; color: #28a745;'>✅ Successfully completed</div>
                        </div>
                        <div style='font-size: 0.85rem; color: #6c757d;'>
                            {datetime.datetime.now().strftime("%H:%M:%S")}
                        </div>
                    </div>
                </div>
                """
        
        def calculate_roi(self, monthly_incidents: int, avg_impact: int, team_size: int) -> Dict:
            """Calculate ROI"""
            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
                
                roi_multiplier = savings / team_cost if team_cost > 0 else 0
                
                if roi_multiplier >= 5.0:
                    recommendation = "🚀 Excellent fit for ARF Enterprise"
                    icon = "🚀"
                    color = "#28a745"
                elif roi_multiplier >= 2.0:
                    recommendation = "✅ Good ROI with ARF Enterprise"
                    icon = "✅"
                    color = "#20c997"
                elif roi_multiplier >= 1.0:
                    recommendation = "⚠️ Consider ARF OSS edition first"
                    icon = "ℹ️"
                    color = "#ffc107"
                else:
                    recommendation = "🆓 Start with ARF OSS (free)"
                    icon = "🆓"
                    color = "#6c757d"
                
                payback = (team_cost / (savings / 12)) if savings > 0 else 0
                
                return {
                    "analysis": {
                        "your_annual_impact": f"${annual_impact:,.0f}",
                        "your_team_cost": f"${team_cost:,.0f}",
                        "potential_savings": f"${savings:,.0f}",
                        "your_roi_multiplier": f"{roi_multiplier:.1f}×",
                        "vs_industry_average": "5.2× average ROI",
                        "recommendation": f"{icon} {recommendation}",
                        "recommendation_color": color,
                        "payback_period": f"{payback:.1f} months" if savings > 0 else "N/A",
                        "annual_savings_potential": f"${savings - team_cost:,.0f}" if savings > team_cost else "$0"
                    }
                }
            except Exception as e:
                return {"error": f"Calculation error: {str(e)}"}
    
    # ===========================================
    # VISUALIZATION ENGINE
    # ===========================================
    
    class VisualizationEngine:
        """Enhanced visualization engine"""
        
        @staticmethod
        def create_incident_timeline() -> go.Figure:
            """Create interactive incident timeline"""
            fig = go.Figure()
            
            # Create timeline events
            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": "⚡ Healing actions 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"}
            ]
            
            color_map = {
                "problem": "#FF6B6B", "alert": "#FFA726", "detection": "#42A5F5",
                "analysis": "#AB47BC", "action": "#66BB6A", "recovery": "#26A69A",
                "stable": "#2E7D32"
            }
            
            # Add events
            for event in events:
                fig.add_trace(go.Scatter(
                    x=[event["time"]],
                    y=[1],
                    mode='markers+text',
                    marker=dict(
                        size=20,
                        color=color_map[event["type"]],
                        symbol='circle' if event["type"] in ['problem', 'alert'] else 'diamond',
                        line=dict(width=2, color='white')
                    ),
                    text=[event["event"]],
                    textposition="top center",
                    hoverinfo="text",
                    hovertemplate="<b>%{text}</b><br>%{x|%H:%M:%S}<extra></extra>",
                    showlegend=False
                ))
            
            # Add connecting lines
            times = [event["time"] for event in events]
            fig.add_trace(go.Scatter(
                x=times,
                y=[1] * len(times),
                mode='lines',
                line=dict(color='rgba(100, 100, 100, 0.3)', width=2, dash='dash'),
                hoverinfo='none',
                showlegend=False
            ))
            
            fig.update_layout(
                title="<b>Incident Timeline - Cache Miss Storm Resolution</b>",
                xaxis_title="Time →",
                yaxis=dict(
                    showticklabels=False,
                    range=[0.5, 1.5]
                ),
                height=450,
                showlegend=False,
                paper_bgcolor='white',
                plot_bgcolor='white',
                hovermode='closest',
                xaxis=dict(
                    tickformat='%H:%M',
                    gridcolor='rgba(200,200,200,0.2)',
                    showgrid=True
                ),
                margin=dict(l=50, r=50, t=80, b=50)
            )
            
            return fig
        
        @staticmethod
        def create_business_dashboard() -> go.Figure:
            """Create executive business dashboard"""
            fig = make_subplots(
                rows=2, cols=2,
                subplot_titles=('Annual Cost Impact', 'Team Capacity Shift',
                              '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. Team Capacity Shift
            labels = ['Firefighting', 'Innovation', 'Strategic Work']
            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 = ['Manual', 'Traditional', 'ARF OSS', 'ARF Enterprise']
            mttr_values = [120, 45, 25, 8]
            
            fig.add_trace(
                go.Bar(
                    x=mttr_categories,
                    y=mttr_values,
                    marker_color=['#FF6B6B', '#FFE66D', '#45B7D1', '#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='white',
                plot_bgcolor='white',
                title_text="<b>Executive Business Dashboard</b>",
                barmode='group',
                margin=dict(l=50, r=50, t=100, b=50)
            )
            
            return fig
        
        @staticmethod
        def create_execution_history_chart(audit_manager: AuditTrailManager) -> go.Figure:
            """Create execution history visualization"""
            executions = audit_manager.execution_history[:10]
            
            if not executions:
                fig = go.Figure()
                fig.update_layout(
                    title="<b>No execution history yet</b>",
                    height=400,
                    paper_bgcolor='white',
                    plot_bgcolor='white',
                    xaxis_showgrid=True,
                    yaxis_showgrid=True
                )
                return fig
            
            # Extract data
            scenarios = [e["scenario"] for e in executions]
            savings = []
            for e in executions:
                try:
                    savings.append(int(e["savings"].replace("$", "").replace(",", "")))
                except:
                    savings.append(0)
            
            fig = go.Figure(data=[
                go.Bar(
                    x=scenarios,
                    y=savings,
                    marker_color='#4ECDC4',
                    text=[f'${s:,.0f}' for s in savings],
                    textposition='outside',
                    name='Cost Saved',
                    hovertemplate="<b>%{x}</b><br>Savings: %{text}<extra></extra>"
                )
            ])
            
            fig.update_layout(
                title="<b>Execution History - Cost Savings</b>",
                xaxis_title="Scenario",
                yaxis_title="Cost Saved ($)",
                height=500,
                paper_bgcolor='white',
                plot_bgcolor='white',
                showlegend=False,
                xaxis_showgrid=True,
                yaxis_showgrid=True,
                margin=dict(l=50, r=50, t=80, b=100)
            )
            
            return fig
        
        @staticmethod
        def create_memory_graph(audit_manager: AuditTrailManager) -> go.Figure:
            """Create incident memory graph"""
            incidents = audit_manager.incident_history[:15]
            
            if not incidents:
                # Create sample graph
                fig = go.Figure()
                
                # Create nodes in a circle
                angles = np.linspace(0, 2*np.pi, 5, endpoint=False)
                radius = 1
                x = radius * np.cos(angles)
                y = radius * np.sin(angles)
                
                fig.add_trace(go.Scatter(
                    x=x,
                    y=y,
                    mode='markers+text',
                    marker=dict(size=30, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFE66D']),
                    text=['Cache', 'DB', 'API', 'Auth', 'Payment'],
                    textposition="top center"
                ))
                
                fig.update_layout(
                    title="<b>Incident Memory Graph (Sample)</b>",
                    showlegend=False,
                    height=600,
                    paper_bgcolor='white',
                    plot_bgcolor='white',
                    xaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]),
                    yaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]),
                    margin=dict(l=20, r=20, t=60, b=20)
                )
                return fig
            
            # Create actual graph from incidents
            nodes = []
            for i, incident in enumerate(incidents):
                nodes.append({
                    "x": np.cos(2 * np.pi * i / len(incidents)),
                    "y": np.sin(2 * np.pi * i / len(incidents)),
                    "size": 15 + incident["severity"] * 5,
                    "color": "#FF6B6B" if incident["severity"] == 3 else "#FFA726" if incident["severity"] == 2 else "#42A5F5",
                    "label": incident["type"][:15],
                    "service": incident["service"]
                })
            
            fig = go.Figure()
            
            # Add nodes
            fig.add_trace(go.Scatter(
                x=[node["x"] for node in nodes],
                y=[node["y"] for node in nodes],
                mode='markers+text',
                marker=dict(
                    size=[node["size"] for node in nodes],
                    color=[node["color"] for node in nodes],
                    line=dict(width=2, color='white')
                ),
                text=[node["label"] for node in nodes],
                textposition="top center",
                hovertext=[f"Service: {node['service']}" for node in nodes],
                hoverinfo="text",
                name="Incidents"
            ))
            
            # Add edges (connect similar types)
            for i in range(len(nodes)):
                for j in range(i + 1, len(nodes)):
                    if incidents[i]["type"] == incidents[j]["type"]:
                        fig.add_trace(go.Scatter(
                            x=[nodes[i]["x"], nodes[j]["x"], None],
                            y=[nodes[i]["y"], nodes[j]["y"], None],
                            mode='lines',
                            line=dict(width=1, color='rgba(100, 100, 100, 0.2)'),
                            hoverinfo='none',
                            showlegend=False
                        ))
            
            fig.update_layout(
                title=f"<b>Incident Memory Graph ({len(incidents)} incidents)</b>",
                showlegend=False,
                height=600,
                paper_bgcolor='white',
                plot_bgcolor='white',
                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]),
                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]),
                margin=dict(l=50, r=50, t=80, b=50)
            )
            
            return fig
    
    # ===========================================
    # CREATE DEMO INTERFACE
    # ===========================================
    
    def create_demo_interface():
        """Create the 5-tab demo interface"""
        
        # Initialize components
        audit_manager = AuditTrailManager()
        business_logic = BusinessLogic(audit_manager, oss_client)
        viz_engine = VisualizationEngine()
        
        # Custom CSS
        custom_css = """
        .gradio-container {
            max-width: 1800px !important;
            margin: auto !important;
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif !important;
        }
        h1 {
            background: linear-gradient(90deg, #1a365d 0%, #2d3748 100%);
            -webkit-background-clip: text;
            -webkit-text-fill-color: transparent;
            background-clip: text;
            font-weight: 800 !important;
            font-size: 2.5rem !important;
            margin-bottom: 0.5rem !important;
        }
        .critical { 
            color: #FF6B6B !important; 
            font-weight: 900 !important;
        }
        .success { 
            color: #4ECDC4 !important; 
            font-weight: 900 !important;
        }
        .tab-nav {
            background: linear-gradient(90deg, #f8fafc 0%, #ffffff 100%) !important;
            border-radius: 10px !important;
            padding: 5px !important;
            margin-bottom: 20px !important;
        }
        .metric-card {
            background: white !important;
            border-radius: 10px !important;
            padding: 20px !important;
            box-shadow: 0 2px 8px rgba(0,0,0,0.06) !important;
            border-left: 4px solid #4ECDC4 !important;
            margin-bottom: 15px !important;
        }
        .enterprise-badge {
            background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
            color: white !important;
            padding: 8px 16px !important;
            border-radius: 20px !important;
            font-weight: 700 !important;
            font-size: 0.85rem !important;
            display: inline-block !important;
            margin: 5px 0 !important;
        }
        .oss-badge {
            background: linear-gradient(135deg, #4299e1 0%, #38b2ac 100%) !important;
            color: white !important;
            padding: 8px 16px !important;
            border-radius: 20px !important;
            font-weight: 700 !important;
            font-size: 0.85rem !important;
            display: inline-block !important;
            margin: 5px 0 !important;
        }
        """
        
        with gr.Blocks(
            title=f"🚀 ARF Investor Demo v3.8.0",
            theme=gr.themes.Soft(
                primary_hue="blue",
                secondary_hue="teal",
                font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"]
            ),
            css=custom_css
        ) as demo:
            
            # ============ HEADER ============
            gr.Markdown(f"""
            <div style="text-align: center; padding: 30px 20px 20px 20px; background: linear-gradient(135deg, #f8fafc 0%, #ffffff 100%); border-radius: 0 0 20px 20px; margin-bottom: 30px; border-bottom: 3px solid #4ECDC4;">
                <h1 style="margin-bottom: 10px;">🚀 Agentic Reliability Framework</h1>
                <h2 style="color: #4a5568; font-weight: 600; margin-bottom: 20px;">Investor Demo v3.8.0 - Enterprise Edition</h2>
                
                <div style="display: flex; justify-content: center; gap: 20px; flex-wrap: wrap; margin-bottom: 20px;">
                    <div class="enterprise-badge">🏢 Enterprise Features</div>
                    <div class="oss-badge">🆓 OSS v{OSS_VERSION}</div>
                    <div style="background: #e8f5e8; color: #2d3748; padding: 8px 16px; border-radius: 20px; font-weight: 600; font-size: 0.85rem;">
                        📈 5.2× Average ROI
                    </div>
                    <div style="background: #fff3cd; color: #856404; padding: 8px 16px; border-radius: 20px; font-weight: 600; font-size: 0.85rem;">
                        ⚡ 85% MTTR Reduction
                    </div>
                </div>
                
                <div style="color: #718096; font-size: 16px; max-width: 800px; margin: 0 auto; line-height: 1.6;">
                    Experience the full journey from <span style="font-weight: 700; color: #4299e1;">OSS Advisory</span> 
                    to <span style="font-weight: 700; color: #764ba2;">Enterprise Autonomous Healing</span>. 
                    See how ARF transforms reliability operations.
                </div>
            </div>
            """)
            
            # ============ SYSTEM STATUS ============
            with gr.Row():
                with gr.Column(scale=1):
                    status_html = f"""
                    <div class="metric-card">
                        <div style="display: flex; justify-content: space-between; align-items: center;">
                            <div>
                                <h4 style="margin: 0 0 5px 0; color: #4a5568;">System Status</h4>
                                <div style="display: flex; align-items: center; gap: 10px;">
                                    <div style="width: 12px; height: 12px; background: #4ECDC4; border-radius: 50%;"></div>
                                    <span style="font-weight: 600; color: #2d3748;">Operational</span>
                                </div>
                            </div>
                            <div style="text-align: right;">
                                <div style="font-size: 0.9rem; color: #718096;">ARF OSS Integration</div>
                                <div style="font-weight: 700; color: {"#4ECDC4" if ARF_OSS_AVAILABLE else "#FF6B6B"}">
                                    {"✅ Connected v" + OSS_VERSION if ARF_OSS_AVAILABLE else "⚠️ Mock Mode"}
                                </div>
                            </div>
                        </div>
                    </div>
                    """
                    gr.HTML(status_html)
                
                with gr.Column(scale=2):
                    performance_html = """
                    <div class="metric-card">
                        <div style="display: flex; justify-content: space-between;">
                            <div>
                                <h4 style="margin: 0 0 5px 0; color: #4a5568;">Performance Metrics</h4>
                                <div style="display: grid; grid-template-columns: repeat(3, 1fr); gap: 15px;">
                                    <div>
                                        <div style="font-size: 0.85rem; color: #718096;">Auto-Heal Rate</div>
                                        <div style="font-weight: 800; color: #4ECDC4; font-size: 1.2rem;">81.7%</div>
                                    </div>
                                    <div>
                                        <div style="font-size: 0.85rem; color: #718096;">Avg Resolution</div>
                                        <div style="font-weight: 800; color: #4ECDC4; font-size: 1.2rem;">8.2 min</div>
                                    </div>
                                    <div>
                                        <div style="font-size: 0.85rem; color: #718096;">Cost Savings</div>
                                        <div style="font-weight: 800; color: #4ECDC4; font-size: 1.2rem;">$6.2M/yr</div>
                                    </div>
                                </div>
                            </div>
                        </div>
                    </div>
                    """
                    gr.HTML(performance_html)
                
                with gr.Column(scale=1):
                    license_html = """
                    <div class="metric-card">
                        <h4 style="margin: 0 0 10px 0; color: #4a5568;">License Status</h4>
                        <div style="display: flex; align-items: center; justify-content: space-between;">
                            <div>
                                <div style="font-weight: 700; color: #4ECDC4; font-size: 1.1rem;">ENTERPRISE</div>
                                <div style="font-size: 0.85rem; color: #718096;">Active • Expires 2024-12-31</div>
                            </div>
                            <div style="background: #e8f5e8; color: #276749; padding: 4px 10px; border-radius: 15px; font-size: 0.8rem; font-weight: 600;">
                                ✅ Valid
                            </div>
                        </div>
                    </div>
                    """
                    gr.HTML(license_html)
            
            # ============ 5 TABS ============
            with gr.Tabs():
                
                # TAB 1: LIVE INCIDENT DEMO
                with gr.TabItem("🔥 Live Incident 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:",
                                interactive=True
                            )
                            
                            scenario_description = gr.Markdown(
                                value=INCIDENT_SCENARIOS["Cache Miss Storm"]["description"]
                            )
                            
                            gr.Markdown("### 📊 Current Crisis Metrics")
                            metrics_display = gr.JSON(
                                value=INCIDENT_SCENARIOS["Cache Miss Storm"]["metrics"],
                                label="Live Metrics",
                                show_label=True
                            )
                            
                            gr.Markdown("### 💰 Business Impact")
                            impact_display = gr.JSON(
                                value=INCIDENT_SCENARIOS["Cache Miss Storm"]["impact"],
                                label="Impact Analysis",
                                show_label=True
                            )
                        
                        # Right Panel
                        with gr.Column(scale=2):
                            gr.Markdown("### 📈 Incident Timeline")
                            timeline_output = gr.Plot(
                                value=viz_engine.create_incident_timeline(),
                                label="",
                                show_label=False
                            )
                            
                            gr.Markdown("### ⚡ Take Action")
                            with gr.Row():
                                oss_btn = gr.Button(
                                    "🆓 Run OSS Analysis",
                                    variant="secondary",
                                    size="lg",
                                    elem_id="oss_btn"
                                )
                                enterprise_btn = gr.Button(
                                    "🚀 Execute Enterprise Healing",
                                    variant="primary",
                                    size="lg",
                                    elem_id="enterprise_btn"
                                )
                            
                            with gr.Row():
                                approval_toggle = gr.Checkbox(
                                    label="🔐 Require Manual Approval",
                                    value=True,
                                    info="Toggle to show approval workflow vs auto-execution",
                                    interactive=True
                                )
                                demo_mode_btn = gr.Button(
                                    "⚡ Quick Demo",
                                    variant="secondary",
                                    size="sm",
                                    elem_id="demo_btn"
                                )
                            
                            approval_display = gr.HTML(
                                value="<div style='padding: 15px; background: #f8f9fa; border-radius: 8px;'>Approval status will appear here after execution</div>"
                            )
                            
                            config_display = gr.JSON(
                                label="⚙️ Enterprise Configuration",
                                value={"approval_required": True, "compliance_mode": "strict"},
                                show_label=True
                            )
                            
                            results_display = gr.JSON(
                                label="🎯 Execution Results",
                                value={"status": "Ready for execution..."},
                                show_label=True
                            )
                
                # TAB 2: BUSINESS IMPACT & ROI
                with gr.TabItem("💰 Business Impact & ROI"):
                    with gr.Column():
                        gr.Markdown("### 📊 Executive Business Dashboard")
                        dashboard_output = gr.Plot(
                            value=viz_engine.create_business_dashboard(),
                            label="",
                            show_label=False
                        )
                        
                        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",
                                    interactive=True
                                )
                                impact_slider = gr.Slider(
                                    1000, 50000, value=8500, step=500,
                                    label="Average incident impact ($)",
                                    interactive=True
                                )
                                team_slider = gr.Slider(
                                    1, 20, value=5, step=1,
                                    label="Reliability team size",
                                    interactive=True
                                )
                                calculate_btn = gr.Button(
                                    "Calculate My ROI",
                                    variant="primary",
                                    size="lg"
                                )
                            
                            with gr.Column(scale=2):
                                roi_output = gr.JSON(
                                    label="Your ROI Analysis",
                                    value={"analysis": "Adjust sliders and click Calculate"},
                                    show_label=True
                                )
                        
                        with gr.Row():
                            with gr.Column():
                                gr.Markdown("""
                                **📈 ARF Enterprise ROI Metrics**
                                - **Average ROI:** 5.2× first year
                                - **Payback Period:** 2-3 months
                                - **Auto-Heal Rate:** 81.7%
                                - **MTTR Reduction:** 85%
                                - **Cost Savings:** $6.2M average annually
                                """)
                            with gr.Column():
                                gr.Markdown("""
                                **🎯 Business Impact**
                                - **Engineer Time:** 325+ hours reclaimed annually
                                - **SLA Compliance:** 99.9% maintained
                                - **Customer Satisfaction:** +40% improvement
                                - **Revenue Protection:** $8,500+/hour saved
                                - **Innovation Capacity:** 60% increase
                                """)
                
                # TAB 3: AUDIT TRAIL & HISTORY
                with gr.TabItem("📜 Audit Trail & History"):
                    with gr.Row():
                        # Left Column - Execution History
                        with gr.Column(scale=1):
                            gr.Markdown("### 📋 Execution History (Audit Trail)")
                            
                            with gr.Row():
                                refresh_btn = gr.Button("🔄 Refresh", variant="secondary", size="sm")
                                clear_btn = gr.Button("🗑️ Clear", variant="stop", size="sm")
                                export_btn = gr.Button("📥 Export", variant="secondary", size="sm")
                            
                            execution_table = gr.Dataframe(
                                headers=["Time", "Scenario", "Actions", "Status", "Savings", "Details"],
                                value=audit_manager.get_execution_history_table(),
                                label="",
                                interactive=False,
                                wrap=True
                            )
                            
                            gr.Markdown("### 📈 Visual History")
                            execution_chart = gr.Plot(
                                value=viz_engine.create_execution_history_chart(audit_manager),
                                label="",
                                show_label=False
                            )
                        
                        # Right Column - Incident History
                        with gr.Column(scale=1):
                            gr.Markdown("### 📊 Incident History")
                            
                            incident_table = gr.Dataframe(
                                headers=["Time", "Service", "Type", "Severity", "Description"],
                                value=audit_manager.get_incident_history_table(),
                                label="",
                                interactive=False,
                                wrap=True
                            )
                            
                            gr.Markdown("### 🧠 Memory Graph")
                            memory_graph = gr.Plot(
                                value=viz_engine.create_memory_graph(audit_manager),
                                label="",
                                show_label=False
                            )
                            
                            gr.Markdown("### 📤 Export & Analytics")
                            export_text = gr.Textbox(
                                label="Full Audit Trail (JSON)",
                                value=audit_manager.export_audit_trail(),
                                lines=8,
                                interactive=False
                            )
                
                # TAB 4: ENTERPRISE FEATURES
                with gr.TabItem("🏢 Enterprise Features"):
                    with gr.Row():
                        # Left Column
                        with gr.Column(scale=1):
                            gr.Markdown("### 🔐 License Management")
                            
                            license_display = gr.JSON(
                                value=business_logic.license_info,
                                label="License Information",
                                show_label=True
                            )
                            
                            with gr.Row():
                                validate_btn = gr.Button("🔍 Validate", variant="secondary")
                                trial_btn = gr.Button("🆓 Start Trial", variant="primary")
                                upgrade_btn = gr.Button("🚀 Upgrade", variant="secondary")
                            
                            gr.Markdown("### ⚡ Feature Matrix")
                            
                            features_data = [
                                ["🤖 Autonomous Healing", "❌", "✅ Auto", "✅ AI-Driven"],
                                ["📊 Executive Dashboards", "Basic", "Advanced", "✅ Comprehensive"],
                                ["🔐 Compliance Automation", "❌", "✅", "✅ SOC2/GDPR"],
                                ["📈 Predictive Analytics", "❌", "Basic", "✅ ML-Powered"],
                                ["🔄 Auto-Remediation", "Manual", "✅ Auto", "✅ Continuous"],
                                ["🎯 SLA Guarantees", "❌", "❌", "✅ 99.9%"],
                                ["📊 Cost Optimization", "Basic", "Advanced", "✅ AI-Optimized"],
                                ["🔒 Role-Based Access", "❌", "✅", "✅ Granular"],
                                ["📝 Audit Trail", "Basic", "✅", "✅ Comprehensive"],
                                ["🔄 Multi-Cloud", "❌", "❌", "✅ Native"],
                            ]
                            
                            features_table = gr.Dataframe(
                                value=features_data,
                                headers=["Feature", "OSS", "Starter", "Enterprise"],
                                label="",
                                interactive=False,
                                wrap=True
                            )
                        
                        # Right Column
                        with gr.Column(scale=1):
                            gr.Markdown("### 📋 Compliance Status")
                            
                            compliance_status = gr.JSON(
                                value={
                                    "SOC2": {"status": "✅ Certified", "expires": "2025-06-30"},
                                    "GDPR": {"status": "✅ Compliant", "last_audit": "2024-10-15"},
                                    "HIPAA": {"status": "🟡 In Progress", "eta": "2024-12-31"},
                                    "ISO27001": {"status": "✅ Certified", "cert_id": "ISO-2024-001"},
                                    "CCPA": {"status": "✅ Compliant", "verified": True}
                                },
                                label="Compliance Certifications",
                                show_label=True
                            )
                            
                            gr.Markdown("### 🔗 Integration Hub")
                            
                            integrations_data = [
                                ["AWS", "CloudWatch, S3, Lambda", "✅ Connected"],
                                ["Azure", "Monitor, Log Analytics", "✅ Connected"],
                                ["GCP", "Operations, BigQuery", "✅ Connected"],
                                ["Datadog", "Metrics, Logs, APM", "✅ Connected"],
                                ["New Relic", "Full-stack", "✅ Connected"],
                                ["PagerDuty", "Incident Response", "✅ Connected"],
                                ["ServiceNow", "ITSM & CMDB", "✅ Connected"],
                                ["Slack", "Notifications", "✅ Connected"],
                            ]
                            
                            integrations_table = gr.Dataframe(
                                value=integrations_data,
                                headers=["Platform", "Services", "Status"],
                                label="",
                                interactive=False,
                                wrap=True
                            )
                
                # TAB 5: LEARNING ENGINE
                with gr.TabItem("🧠 Learning Engine"):
                    with gr.Row():
                        # Left Column
                        with gr.Column(scale=2):
                            gr.Markdown("### 🧠 Incident Memory Graph")
                            
                            memory_graph_plot = gr.Plot(
                                value=viz_engine.create_memory_graph(audit_manager),
                                label="",
                                show_label=False
                            )
                            
                            with gr.Row():
                                graph_type = gr.Radio(
                                    choices=["Force Directed", "Hierarchical", "Timeline"],
                                    value="Force Directed",
                                    label="Graph Type",
                                    interactive=True
                                )
                                show_weights = gr.Checkbox(label="Show Edge Weights", value=True, interactive=True)
                            
                            gr.Markdown("### 🔍 Similarity Search")
                            
                            search_query = gr.Textbox(
                                label="Search for similar incidents",
                                placeholder="Describe incident or paste metrics...",
                                lines=2,
                                interactive=True
                            )
                            
                            with gr.Row():
                                search_btn = gr.Button("🔍 Search", variant="primary")
                                clear_search_btn = gr.Button("Clear", variant="secondary")
                            
                            search_results = gr.Dataframe(
                                headers=["Incident", "Similarity", "Resolution", "Actions"],
                                value=[],
                                label="",
                                interactive=False,
                                wrap=True
                            )
                        
                        # Right Column
                        with gr.Column(scale=1):
                            gr.Markdown("### 📊 Learning Statistics")
                            
                            learning_stats = gr.JSON(
                                value={
                                    "total_incidents": len(audit_manager.incident_history),
                                    "resolved_automatically": len([e for e in audit_manager.execution_history if "Executed" in e["status"]]),
                                    "patterns_detected": 5,
                                    "confidence_threshold": 0.85,
                                    "memory_size": f"{len(audit_manager.incident_history) * 0.5:.1f} KB",
                                    "similar_incidents_found": 12
                                },
                                label="Learning Engine Statistics",
                                show_label=True
                            )
                            
                            gr.Markdown("### 🎯 Pattern Detection")
                            
                            pattern_analysis = gr.JSON(
                                value={
                                    "most_common": "Cache Miss Storm",
                                    "frequency": "45% of incidents",
                                    "avg_resolution_time": "8.2 minutes",
                                    "success_rate": "92%",
                                    "recommendations": [
                                        "Implement proactive cache monitoring",
                                        "Add circuit breaker for database fallback",
                                        "Optimize cache TTL settings"
                                    ]
                                },
                                label="Pattern Analysis",
                                show_label=True
                            )
            
            # ============ EVENT HANDLERS ============
            
            # Scenario dropdown change
            def update_scenario(scenario_name):
                scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
                return (
                    f"### {scenario_name}\n{scenario.get('description', 'No description')}",
                    scenario.get("metrics", {}),
                    scenario.get("impact", {}),
                    viz_engine.create_incident_timeline()
                )
            
            scenario_dropdown.change(
                fn=update_scenario,
                inputs=[scenario_dropdown],
                outputs=[scenario_description, metrics_display, impact_display, timeline_output]
            )
            
            # OSS Analysis button
            async def run_oss_analysis(scenario_name):
                analysis = await business_logic.run_oss_analysis(scenario_name)
                incident_table_data = audit_manager.get_incident_history_table()
                memory_plot = viz_engine.create_memory_graph(audit_manager)
                return analysis, incident_table_data, memory_plot
            
            oss_btn.click(
                fn=run_oss_analysis,
                inputs=[scenario_dropdown],
                outputs=[results_display, incident_table, memory_graph]
            )
            
            # Enterprise Healing button
            def execute_healing(scenario_name, approval_required):
                approval_html, results = business_logic.execute_enterprise_healing(scenario_name, approval_required)
                execution_table_data = audit_manager.get_execution_history_table()
                execution_chart_plot = viz_engine.create_execution_history_chart(audit_manager)
                return approval_html, results, execution_table_data, execution_chart_plot
            
            enterprise_btn.click(
                fn=execute_healing,
                inputs=[scenario_dropdown, approval_toggle],
                outputs=[approval_display, results_display, execution_table, execution_chart]
            )
            
            # Quick Demo button
            async def run_quick_demo():
                # Run OSS analysis
                analysis = await business_logic.run_oss_analysis("Cache Miss Storm")
                
                # Execute enterprise healing
                approval_html, results = business_logic.execute_enterprise_healing("Cache Miss Storm", False)
                
                # Update all displays
                execution_table_data = audit_manager.get_execution_history_table()
                incident_table_data = audit_manager.get_incident_history_table()
                execution_chart_plot = viz_engine.create_execution_history_chart(audit_manager)
                memory_plot = viz_engine.create_memory_graph(audit_manager)
                
                return (
                    analysis,
                    approval_html,
                    results,
                    execution_table_data,
                    incident_table_data,
                    execution_chart_plot,
                    memory_plot,
                    gr.Checkbox.update(value=False)
                )
            
            demo_mode_btn.click(
                fn=run_quick_demo,
                outputs=[
                    results_display,
                    approval_display,
                    results_display,
                    execution_table,
                    incident_table,
                    execution_chart,
                    memory_graph,
                    approval_toggle
                ]
            )
            
            # ROI Calculator
            def calculate_roi(monthly, impact, team):
                return business_logic.calculate_roi(monthly, impact, team)
            
            calculate_btn.click(
                fn=calculate_roi,
                inputs=[monthly_slider, impact_slider, team_slider],
                outputs=[roi_output]
            )
            
            # Audit Trail Refresh
            def refresh_audit_trail():
                execution_table_data = audit_manager.get_execution_history_table()
                incident_table_data = audit_manager.get_incident_history_table()
                execution_chart_plot = viz_engine.create_execution_history_chart(audit_manager)
                memory_plot = viz_engine.create_memory_graph(audit_manager)
                export_data = audit_manager.export_audit_trail()
                return execution_table_data, incident_table_data, execution_chart_plot, memory_plot, export_data
            
            refresh_btn.click(
                fn=refresh_audit_trail,
                outputs=[execution_table, incident_table, execution_chart, memory_graph, export_text]
            )
            
            # Clear History
            def clear_audit_trail():
                audit_manager.execution_history = []
                audit_manager.incident_history = []
                audit_manager._initialize_sample_data()
                return refresh_audit_trail()
            
            clear_btn.click(
                fn=clear_audit_trail,
                outputs=[execution_table, incident_table, execution_chart, memory_graph, export_text]
            )
            
            # Export Audit Trail
            def update_export():
                return audit_manager.export_audit_trail()
            
            export_btn.click(
                fn=update_export,
                outputs=[export_text]
            )
            
            # License Management
            def validate_license():
                business_logic.license_info["last_validated"] = datetime.datetime.now().isoformat()
                business_logic.license_info["validation_code"] = "VAL-2024-001"
                return business_logic.license_info
            
            validate_btn.click(
                fn=validate_license,
                outputs=[license_display]
            )
            
            def start_trial():
                business_logic.license_info["tier"] = "TRIAL"
                business_logic.license_info["expires_at"] = (datetime.datetime.now() + datetime.timedelta(days=30)).isoformat()
                business_logic.license_info["status"] = "🆓 Trial Active (30 days)"
                return business_logic.license_info
            
            trial_btn.click(
                fn=start_trial,
                outputs=[license_display]
            )
            
            def upgrade_license():
                business_logic.license_info["tier"] = "PLATFORM"
                business_logic.license_info["status"] = "🚀 Upgraded to Platform Edition"
                return business_logic.license_info
            
            upgrade_btn.click(
                fn=upgrade_license,
                outputs=[license_display]
            )
            
            # Learning Engine Search
            def search_similar_incidents(query):
                if not query.strip():
                    return []
                
                # Mock search results
                results = [
                    ["Cache Miss Storm", "92%", "✅ Resolved", "Scale cache + circuit breaker"],
                    ["Database Connection Pool", "78%", "✅ Resolved", "Increase pool size"],
                    ["Memory Leak", "65%", "⚠️ Pending", "Restart + monitoring"],
                    ["API Rate Limit", "58%", "✅ Resolved", "Increase limits + caching"],
                ]
                
                return results
            
            search_btn.click(
                fn=search_similar_incidents,
                inputs=[search_query],
                outputs=[search_results]
            )
            
            clear_search_btn.click(
                fn=lambda: [],
                outputs=[search_results]
            )
            
            # Graph type change
            def update_graph_view(graph_type, show_weights):
                return viz_engine.create_memory_graph(audit_manager)
            
            graph_type.change(
                fn=update_graph_view,
                inputs=[graph_type, show_weights],
                outputs=[memory_graph_plot]
            )
            
            show_weights.change(
                fn=update_graph_view,
                inputs=[graph_type, show_weights],
                outputs=[memory_graph_plot]
            )
            
            # Initialize with default plots
            demo.load(
                fn=lambda: (
                    viz_engine.create_business_dashboard(),
                    viz_engine.create_incident_timeline(),
                    viz_engine.create_execution_history_chart(audit_manager),
                    viz_engine.create_memory_graph(audit_manager),
                    audit_manager.export_audit_trail()
                ),
                outputs=[dashboard_output, timeline_output, execution_chart, memory_graph, export_text]
            )
            
            # ============ FOOTER ============
            gr.Markdown("""
            <div style="margin-top: 40px; padding: 30px; background: linear-gradient(135deg, #1a365d 0%, #2d3748 100%); border-radius: 20px; color: white;">
                <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); gap: 30px; margin-bottom: 30px;">
                    <div>
                        <h4 style="color: white; margin-bottom: 15px;">🚀 Experience the Journey</h4>
                        <ul style="list-style: none; padding: 0; margin: 0; color: #cbd5e0;">
                            <li style="margin-bottom: 10px;">1. <strong>Start with OSS</strong> - Get recommendations</li>
                            <li style="margin-bottom: 10px;">2. <strong>Calculate ROI</strong> - See your savings</li>
                            <li style="margin-bottom: 10px;">3. <strong>Execute Healing</strong> - Experience autonomy</li>
                            <li style="margin-bottom: 10px;">4. <strong>View Audit Trail</strong> - Track everything</li>
                            <li>5. <strong>Explore Features</strong> - See enterprise power</li>
                        </ul>
                    </div>
                    <div>
                        <h4 style="color: white; margin-bottom: 15px;">📞 Get Started</h4>
                        <ul style="list-style: none; padding: 0; margin: 0; color: #cbd5e0;">
                            <li style="margin-bottom: 10px;">📧 <strong>Contact:</strong> sales@arfinvestor.com</li>
                            <li style="margin-bottom: 10px;">📚 <strong>Docs:</strong> docs.arfinvestor.com</li>
                            <li style="margin-bottom: 10px;">💬 <strong>Slack:</strong> Join 2,500+ engineers</li>
                            <li style="margin-bottom: 10px;">🆓 <strong>Trial:</strong> 30-day enterprise trial</li>
                            <li>🚀 <strong>Demo:</strong> Live demo available</li>
                        </ul>
                    </div>
                    <div>
                        <h4 style="color: white; margin-bottom: 15px;">🛡️ Trust & Security</h4>
                        <ul style="list-style: none; padding: 0; margin: 0; color: #cbd5e0;">
                            <li style="margin-bottom: 10px;">✅ SOC 2 Type II Certified</li>
                            <li style="margin-bottom: 10px;">✅ GDPR & CCPA Compliant</li>
                            <li style="margin-bottom: 10px;">✅ ISO 27001 Certified</li>
                            <li style="margin-bottom: 10px;">✅ HIPAA Ready</li>
                            <li>✅ Enterprise-grade Security</li>
                        </ul>
                    </div>
                </div>
                
                <div style="border-top: 1px solid #4a5568; padding-top: 20px; text-align: center; color: #a0aec0; font-size: 0.9rem;">
                    <p style="margin: 0 0 10px 0;">© 2024 Agentic Reliability Framework. Demo v3.8.0 Enterprise Edition.</p>
                    <p style="margin: 0; font-size: 0.8rem;">This demonstration uses ARF OSS v{OSS_VERSION}. Actual enterprise features require license activation.</p>
                </div>
            </div>
            """.format(OSS_VERSION=OSS_VERSION))
        
        return demo
    
    return create_demo_interface()

except Exception as e:
    logger.error(f"Failed to create demo: {e}")
    logger.error(traceback.format_exc())
    
    # Minimal fallback
    import gradio as gr
    
    with gr.Blocks(title="🚀 ARF Demo - Error") as demo:
        gr.Markdown(f"""
        # ⚠️ ARF Demo Initialization Error
        
        Failed to initialize the demo:
        
        ```python
        {str(e)}
        ```
        
        Please check the logs for details.
        """)
    
    return demo


def main():
    """Main entry point"""
    print("🚀 Starting ARF Ultimate Investor Demo v3.8.0...")
    print("=" * 70)
    print("📊 Features:")
    print("  • 5 Comprehensive Tabs with User-Focused Journey")
    print("  • Live Incident Demo with OSS Analysis")
    print("  • Business Impact & ROI Calculator")
    print("  • Audit Trail & History with Memory Graph")
    print("  • Enterprise Features & License Management")
    print("  • Learning Engine with Pattern Detection")
    print("=" * 70)
    print(f"\n📦 Using ARF OSS v{OSS_VERSION if 'OSS_VERSION' in locals() else '3.3.6'}")
    print("🌐 Opening web interface at http://localhost:7860...")
    
    demo = create_demo_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=False,
        show_error=True
    )


if __name__ == "__main__":
    main()