app.py

"""
🚀 ARF Ultimate Investor Demo v3.8.0 - ENTERPRISE EDITION
With Audit Trail, Incident History, Memory Graph, and Enterprise Features
"""

import logging
import datetime
import random
import uuid
import json
import tempfile
from typing import Dict, List, Optional, Any, Tuple
from collections import deque
import gradio as gr
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from plotly.subplots import make_subplots

# Import ARF OSS if available
try:
    from agentic_reliability_framework.arf_core.models.healing_intent import (
        HealingIntent,
        create_scale_out_intent
    )
    from agentic_reliability_framework.arf_core.engine.simple_mcp_client import OSSMCPClient
    ARF_OSS_AVAILABLE = True
except ImportError:
    ARF_OSS_AVAILABLE = False
    # Mock classes for demo
    class HealingIntent:
        def __init__(self, **kwargs):
            self.intent_type = kwargs.get("intent_type", "scale_out")
            self.parameters = kwargs.get("parameters", {})
        
        def to_dict(self) -> Dict[str, Any]:
            return {
                "intent_type": self.intent_type,
                "parameters": self.parameters,
                "created_at": datetime.datetime.now().isoformat()
            }
    
    def create_scale_out_intent(resource_type: str, scale_factor: float = 2.0) -> HealingIntent:
        return HealingIntent(
            intent_type="scale_out",
            parameters={
                "resource_type": resource_type,
                "scale_factor": scale_factor,
                "action": "Increase capacity"
            }
        )
    
    class OSSMCPClient:
        def analyze_incident(self, metrics: Dict, pattern: str = "") -> Dict[str, Any]:
            return {
                "status": "analysis_complete",
                "recommendations": [
                    "Increase resource allocation",
                    "Implement monitoring",
                    "Add circuit breakers",
                    "Optimize configuration"
                ],
                "confidence": 0.92
            }

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

# ===========================================
# COMPREHENSIVE DATA
# ===========================================

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%"
        }
    },
    "API Rate Limit Exceeded": {
        "description": "Global API rate limit exceeded causing 429 errors for external clients",
        "severity": "MEDIUM",
        "metrics": {
            "429 Error Rate": "42.5%",
            "Successful Requests": "58.3%",
            "API Latency": "120ms",
            "Queue Depth": "1250",
            "Client Satisfaction": "65/100"
        },
        "impact": {
            "Revenue Loss": "$1,800/hour",
            "Affected Partners": "8",
            "Partner SLA Violations": "3",
            "Business Impact": "Medium"
        }
    },
    "Microservice Cascading Failure": {
        "description": "Order service failure causing cascading failures in dependent services",
        "severity": "CRITICAL",
        "metrics": {
            "Order Failure Rate": "68.2%",
            "Circuit Breakers Open": "4",
            "Retry Storm Intensity": "425",
            "Error Propagation": "85%",
            "System Stability": "15/100"
        },
        "impact": {
            "Revenue Loss": "$25,000/hour",
            "Abandoned Carts": "12,500",
            "Affected Users": "75,000",
            "Brand Damage": "High"
        }
    }
}

# ===========================================
# AUDIT TRAIL & HISTORY MANAGEMENT
# ===========================================

class AuditTrailManager:
    """Manage audit trail and execution history"""
    
    def __init__(self) -> None:
        self.execution_history = deque(maxlen=50)
        self.incident_history = deque(maxlen=100)
        self._initialize_sample_data()
    
    def _initialize_sample_data(self) -> None:
        """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._create_execution_entry(
                base_time - datetime.timedelta(minutes=30),
                "Cascading Failure", 5, 12500, "✅ Executed", "Isolated services"
            ),
            self._create_execution_entry(
                base_time - datetime.timedelta(minutes=15),
                "Cache Miss Storm", 4, 7200, "✅ Executed", "Optimized cache"
            )
        ]
        
        for execution in sample_executions:
            self.execution_history.append(execution)
        
        # Sample incident history
        services = ["API Gateway", "Database", "Cache", "Auth Service", "Payment Service", 
                   "Order Service", "User Service", "Session Service"]
        
        for _ in range(25):
            incident_time = base_time - datetime.timedelta(minutes=random.randint(5, 120))
            self.incident_history.append({
                "timestamp": incident_time,
                "time_str": incident_time.strftime("%H:%M"),
                "service": random.choice(services),
                "type": random.choice(list(INCIDENT_SCENARIOS.keys())),
                "severity": random.randint(1, 3),
                "description": f"{random.choice(['High latency', 'Connection failed', 'Memory spike', 'Timeout'])} on {random.choice(services)}",
                "id": str(uuid.uuid4())[:8]
            })
    
    def _create_execution_entry(self, timestamp: datetime.datetime, scenario: str, 
                               actions: int, savings: int, status: str, details: str) -> Dict[str, Any]:
        """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": str(uuid.uuid4())[:8]
        }
    
    def add_execution(self, scenario: str, actions: List[str], 
                     savings: int, approval_required: bool, details: str = "") -> Dict[str, Any]:
        """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.appendleft(entry)  # Newest first
        return entry
    
    def add_incident(self, scenario_name: str, metrics: Dict) -> Dict[str, Any]:
        """Add incident to history"""
        severity = 2 if "MEDIUM" in INCIDENT_SCENARIOS.get(scenario_name, {}).get("severity", "") else 3
        entry = {
            "timestamp": datetime.datetime.now(),
            "time_str": datetime.datetime.now().strftime("%H:%M"),
            "service": "Demo System",
            "type": scenario_name,
            "severity": severity,
            "description": f"Demo incident: {scenario_name}",
            "id": str(uuid.uuid4())[:8]
        }
        self.incident_history.appendleft(entry)
        return entry
    
    def get_execution_history_table(self, limit: int = 10) -> List[List[str]]:
        """Get execution history for table display"""
        return [
            [entry["time_str"], entry["scenario"], entry["actions"], 
             entry["status"], entry["savings"], entry["details"]]
            for entry in list(self.execution_history)[:limit]
        ]
    
    def get_incident_history_table(self, limit: int = 15) -> List[List[str]]:
        """Get incident history for table display"""
        return [
            [entry["time_str"], entry["service"], entry["type"], 
             f"{entry['severity']}/3", entry["description"]]
            for entry in list(self.incident_history)[:limit]
        ]
    
    def clear_history(self) -> Tuple[List[List[str]], List[List[str]]]:
        """Clear all history"""
        self.execution_history.clear()
        self.incident_history.clear()
        self._initialize_sample_data()  # Restore sample data
        return self.get_execution_history_table(), self.get_incident_history_table()
    
    def export_audit_trail(self) -> str:
        """Export audit trail as JSON"""
        total_savings = 0
        for e in self.execution_history:
            if "$" in e["savings"]:
                try:
                    total_savings += int(e["savings"].replace("$", "").replace(",", ""))
                except ValueError:
                    continue
        
        return json.dumps({
            "executions": list(self.execution_history),
            "incidents": list(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
        }, indent=2, default=str)

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

class EnhancedVisualizationEngine:
    """Enhanced visualization engine with memory graph support"""
    
    @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": "red", "alert": "orange", "detection": "blue",
            "analysis": "purple", "action": "green", "recovery": "lightgreen",
            "stable": "darkgreen"
        }
        
        for event in events:
            fig.add_trace(go.Scatter(
                x=[event["time"]],
                y=[1],
                mode='markers+text',
                marker=dict(
                    size=15,
                    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",
                name=event["type"].capitalize(),
                hovertemplate="<b>%{text}</b><br>%{x|%H:%M:%S}<extra></extra>"
            ))
        
        fig.update_layout(
            title="<b>Incident Timeline - Cache Miss Storm Resolution</b>",
            xaxis_title="Time →",
            yaxis_title="Event Type",
            height=450,
            showlegend=True,
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            hovermode='closest',
            xaxis=dict(
                tickformat='%H:%M',
                gridcolor='rgba(200,200,200,0.2)'
            ),
            yaxis=dict(
                showticklabels=False,
                gridcolor='rgba(200,200,200,0.1)'
            )
        )
        
        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='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            title_text="<b>Executive Business Dashboard</b>",
            barmode='group'
        )
        
        return fig
    
    @staticmethod
    def create_execution_history_chart(audit_manager: AuditTrailManager) -> go.Figure:
        """Create execution history visualization"""
        executions = list(audit_manager.execution_history)[:10]  # Last 10 executions
        
        if not executions:
            fig = go.Figure()
            fig.update_layout(
                title="No execution history yet",
                height=400,
                paper_bgcolor='rgba(0,0,0,0)',
                plot_bgcolor='rgba(0,0,0,0)'
            )
            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 ValueError:
                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='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            showlegend=False
        )
        
        return fig
    
    @staticmethod
    def create_memory_graph(audit_manager: AuditTrailManager, graph_type: str = "Force Directed", 
                           show_weights: bool = True, auto_layout: bool = True) -> go.Figure:
        """Create interactive memory graph visualization"""
        fig = go.Figure()
        
        # Get incidents from history
        incidents = list(audit_manager.incident_history)[:20]  # Last 20 incidents
        
        if not incidents:
            # Create sample graph
            nodes = [
                {"id": "Incident_1", "label": "Cache Miss", "type": "incident", "size": 20},
                {"id": "Action_1", "label": "Scale Cache", "type": "action", "size": 15},
                {"id": "Outcome_1", "label": "Resolved", "type": "outcome", "size": 15},
                {"id": "Component_1", "label": "Redis", "type": "component", "size": 18},
            ]
            
            edges = [
                {"source": "Incident_1", "target": "Action_1", "weight": 0.9, "label": "resolved_by"},
                {"source": "Action_1", "target": "Outcome_1", "weight": 1.0, "label": "leads_to"},
                {"source": "Incident_1", "target": "Component_1", "weight": 0.8, "label": "affects"},
            ]
        else:
            # Create nodes from actual incidents
            nodes = []
            edges = []
            
            for i, incident in enumerate(incidents):
                node_id = f"Incident_{i}"
                nodes.append({
                    "id": node_id,
                    "label": incident["type"][:20],
                    "type": "incident",
                    "size": 15 + (incident.get("severity", 2) * 5),
                    "severity": incident.get("severity", 2)
                })
                
                # Create edges to previous incidents
                if i > 0:
                    prev_id = f"Incident_{i-1}"
                    edges.append({
                        "source": prev_id,
                        "target": node_id,
                        "weight": 0.7,
                        "label": "related_to"
                    })
        
        # Color mapping
        color_map = {
            "incident": "#FF6B6B",
            "action": "#4ECDC4",
            "outcome": "#45B7D1",
            "component": "#96CEB4"
        }
        
        # Add nodes
        node_x = []
        node_y = []
        node_text = []
        node_color = []
        node_size = []
        
        for i, node in enumerate(nodes):
            # Simple layout - could be enhanced with networkx
            angle = 2 * np.pi * i / len(nodes)
            radius = 1.0
            
            node_x.append(radius * np.cos(angle))
            node_y.append(radius * np.sin(angle))
            node_text.append(f"{node['label']}<br>Type: {node['type']}")
            node_color.append(color_map.get(node["type"], "#999999"))
            node_size.append(node.get("size", 15))
        
        fig.add_trace(go.Scatter(
            x=node_x,
            y=node_y,
            mode='markers+text',
            marker=dict(
                size=node_size,
                color=node_color,
                line=dict(width=2, color='white')
            ),
            text=[node["label"] for node in nodes],
            textposition="top center",
            hovertext=node_text,
            hoverinfo="text",
            name="Nodes"
        ))
        
        # Add edges
        for edge in edges:
            try:
                source_idx = next(i for i, n in enumerate(nodes) if n["id"] == edge["source"])
                target_idx = next(i for i, n in enumerate(nodes) if n["id"] == edge["target"])
                
                fig.add_trace(go.Scatter(
                    x=[node_x[source_idx], node_x[target_idx], None],
                    y=[node_y[source_idx], node_y[target_idx], None],
                    mode='lines',
                    line=dict(
                        width=2 * edge.get("weight", 1.0),
                        color='rgba(100, 100, 100, 0.5)'
                    ),
                    hoverinfo='none',
                    showlegend=False
                ))
            except StopIteration:
                continue
        
        fig.update_layout(
            title="<b>Incident Memory Graph</b>",
            showlegend=True,
            height=600,
            paper_bgcolor='rgba(0,0,0,0)',
            plot_bgcolor='rgba(0,0,0,0)',
            hovermode='closest',
            xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
            yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
            margin=dict(l=20, r=20, t=40, b=20)
        )
        
        return fig
    
    @staticmethod
    def create_pattern_analysis_chart(analysis_data: Dict[str, Any]) -> go.Figure:
        """Create pattern analysis visualization"""
        fig = make_subplots(
            rows=2, cols=2,
            subplot_titles=('Incident Frequency', 'Resolution Times', 
                          'Success Rates', 'Pattern Correlation'),
            vertical_spacing=0.15
        )
        
        # Sample data - in real app this would come from analysis
        patterns = ['Cache Issues', 'DB Connections', 'Memory Leaks', 'API Limits', 'Cascading']
        frequencies = [12, 8, 5, 7, 3]
        resolution_times = [8.2, 15.5, 45.2, 5.1, 32.8]
        success_rates = [92, 85, 78, 96, 65]
        
        # Incident Frequency
        fig.add_trace(
            go.Bar(x=patterns, y=frequencies, name='Frequency'),
            row=1, col=1
        )
        
        # Resolution Times
        fig.add_trace(
            go.Bar(x=patterns, y=resolution_times, name='Resolution Time (min)'),
            row=1, col=2
        )
        
        # Success Rates
        fig.add_trace(
            go.Bar(x=patterns, y=success_rates, name='Success Rate %'),
            row=2, col=1
        )
        
        # Correlation Matrix
        corr_matrix = np.array([
            [1.0, 0.3, 0.1, 0.2, 0.05],
            [0.3, 1.0, 0.4, 0.1, 0.25],
            [0.1, 0.4, 1.0, 0.05, 0.6],
            [0.2, 0.1, 0.05, 1.0, 0.1],
            [0.05, 0.25, 0.6, 0.1, 1.0]
        ])
        
        fig.add_trace(
            go.Heatmap(z=corr_matrix, x=patterns, y=patterns),
            row=2, col=2
        )
        
        fig.update_layout(
            height=700,
            showlegend=False,
            title_text="<b>Pattern Analysis Dashboard</b>"
        )
        
        return fig

# ===========================================
# ENHANCED BUSINESS LOGIC
# ===========================================

class EnhancedBusinessLogic:
    """Enhanced business logic with enterprise features"""
    
    def __init__(self, audit_manager: AuditTrailManager):
        self.audit_manager = audit_manager
        self.viz_engine = EnhancedVisualizationEngine()
        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"
        }
        self.mcp_mode = "approval"
        self.learning_stats = {
            "total_incidents": 127,
            "resolved_automatically": 89,
            "average_resolution_time": "8.2 min",
            "success_rate": "92.1%",
            "patterns_detected": 24,
            "confidence_threshold": 0.85,
            "memory_size": "4.7 MB",
            "embeddings": 127,
            "graph_nodes": 89,
            "graph_edges": 245
        }
    
    def run_oss_analysis(self, scenario_name: str) -> Dict[str, Any]:
        """Run OSS analysis"""
        scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
        analysis = scenario.get("oss_analysis", {})
        
        if not analysis:
            analysis = {
                "status": "✅ Analysis Complete",
                "recommendations": [
                    "Increase resource allocation",
                    "Implement monitoring",
                    "Add circuit breakers",
                    "Optimize configuration"
                ],
                "estimated_time": "45-60 minutes",
                "engineers_needed": "2-3",
                "manual_effort": "Required",
                "total_cost": "$3,000 - $8,000"
            }
        
        # Add ARF context
        analysis["arf_context"] = {
            "oss_available": ARF_OSS_AVAILABLE,
            "version": "3.3.6",
            "mode": "advisory_only",
            "healing_intent": True
        }
        
        # 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[Any, ...]:
        """Execute enterprise healing"""
        scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
        results = scenario.get("enterprise_results", {})
        
        # Use default results if not available
        if not results:
            results = {
                "actions_completed": [
                    "✅ Auto-scaled resources based on ARF healing intent",
                    "✅ Implemented optimization recommendations",
                    "✅ Deployed monitoring and alerting",
                    "✅ Validated recovery with automated testing"
                ],
                "metrics_improvement": {
                    "Performance": "Dramatically improved",
                    "Stability": "Restored",
                    "Recovery": "Complete"
                },
                "business_impact": {
                    "Recovery Time": f"60 min → {random.randint(5, 15)} min",
                    "Cost Saved": f"${random.randint(2000, 10000):,}",
                    "Users Impacted": "45,000 → 0",
                    "Revenue Protected": f"${random.randint(1000, 5000):,}"
                }
            }
        
        # Calculate savings
        savings = 0
        if "Cost Saved" in results["business_impact"]:
            try:
                savings_str = results["business_impact"]["Cost Saved"]
                savings = int(''.join(filter(str.isdigit, savings_str)))
            except (ValueError, TypeError):
                savings = random.randint(2000, 10000)
        
        # Update status
        if approval_required:
            results["status"] = "✅ Approved and Executed"
            approval_html = self._create_approval_html(scenario_name, True)
        else:
            results["status"] = "✅ Auto-Executed"
            approval_html = self._create_approval_html(scenario_name, False)
        
        # Add to audit trail
        details = f"{len(results['actions_completed'])} actions executed"
        self.audit_manager.add_execution(
            scenario_name,
            results["actions_completed"],
            savings,
            approval_required,
            details
        )
        
        # Add enterprise context
        results["enterprise_context"] = {
            "approval_required": approval_required,
            "compliance_mode": "strict",
            "audit_trail": "created",
            "learning_applied": True,
            "roi_measured": True
        }
        
        # Update visualizations
        execution_chart = self.viz_engine.create_execution_history_chart(self.audit_manager)
        
        return (
            approval_html,
            {"approval_required": approval_required, "compliance_mode": "strict"},
            results,
            execution_chart,
            self.audit_manager.get_execution_history_table(),
            self.audit_manager.get_incident_history_table()
        )
    
    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: #f8f9fa; border-radius: 10px; border-left: 4px solid #007bff; margin: 10px 0;'>
                <h4 style='margin: 0 0 10px 0;'>🛡️ Approval Required</h4>
                <p><b>Action:</b> Scale resources for {scenario_name}</p>
                <p><b>Risk Level:</b> Low (auto-rollback available)</p>
                <p><b>Blast Radius:</b> Limited to affected service</p>
                <p><b>Status:</b> ✅ <span style='color: green;'>Approved & Executed</span></p>
            </div>
            """
        else:
            return f"""
            <div style='padding: 20px; background: #e8f5e8; border-radius: 10px; border-left: 4px solid #28a745; margin: 10px 0;'>
                <h4 style='margin: 0 0 10px 0;'>⚡ Auto-Executed</h4>
                <p><b>Action:</b> Autonomous healing for {scenario_name}</p>
                <p><b>Mode:</b> Fully autonomous (safety guardrails active)</p>
                <p><b>Guardrails:</b> Blast radius limits, rollback ready, compliance logging</p>
                <p><b>Status:</b> ✅ <span style='color: green;'>Successfully completed</span></p>
            </div>
            """
    
    def calculate_roi(self, monthly_incidents: int, avg_impact: int, team_size: int) -> Dict[str, Any]:
        """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 = "🚀"
            elif roi_multiplier >= 2.0:
                recommendation = "✅ Good ROI with ARF Enterprise"
                icon = "✅"
            elif roi_multiplier >= 1.0:
                recommendation = "⚠️ Consider ARF OSS edition first"
                icon = "ℹ️"
            else:
                recommendation = "🆓 Start with ARF OSS (free)"
                icon = "🆓"
            
            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"${savings:,.0f}",
                    "your_roi_multiplier": f"{roi_multiplier:.1f}×",
                    "vs_industry_average": "5.2× average ROI",
                    "recommendation": f"{icon} {recommendation}",
                    "payback_period": f"{payback:.1f} months" if savings > 0 else "N/A",
                    "annual_savings_potential": f"${savings - team_cost:,.0f}" if savings > team_cost else "$0"
                }
            }
        except Exception as e:
            return {"error": f"Calculation error: {str(e)}"}
    
    def validate_license(self) -> Dict[str, Any]:
        """Validate current license"""
        # Simulate license validation
        self.license_info["last_validated"] = datetime.datetime.now().isoformat()
        self.license_info["validation_code"] = f"VAL-{random.randint(1000, 9999)}"
        return self.license_info
    
    def start_trial(self) -> Dict[str, Any]:
        """Start 30-day trial"""
        expires = datetime.datetime.now() + datetime.timedelta(days=30)
        
        self.license_info.update({
            "tier": "TRIAL",
            "expires_at": expires.isoformat(),
            "features": ["autonomous_healing", "compliance", "audit_trail"],
            "max_services": 10,
            "max_incidents_per_month": 100,
            "status": "🆓 Trial Active",
            "trial_started": datetime.datetime.now().isoformat(),
            "days_remaining": 30
        })
        
        return self.license_info
    
    def upgrade_license(self) -> Dict[str, Any]:
        """Upgrade license tier"""
        tiers = ["STARTER", "PROFESSIONAL", "ENTERPRISE", "PLATFORM"]
        current_tier = self.license_info.get("tier", "STARTER")
        current_idx = tiers.index(current_tier) if current_tier in tiers else 0
        
        if current_idx < len(tiers) - 1:
            new_tier = tiers[current_idx + 1]
            self.license_info["tier"] = new_tier
            self.license_info["status"] = f"✅ Upgraded to {new_tier}"
        
        return self.license_info
    
    def update_mcp_mode(self, mode: str) -> Dict[str, Any]:
        """Update MCP execution mode"""
        self.mcp_mode = mode
        
        # Update configuration
        config = {
            "mcp_mode": mode,
            "approval_required": mode == "approval",
            "autonomous": mode == "autonomous",
            "safety_guardrails": True,
            "rollback_enabled": True,
            "compliance_mode": "strict"
        }
        
        return config
    
    def get_learning_stats(self) -> Dict[str, Any]:
        """Get current learning engine statistics"""
        # Update stats based on history
        total_incidents = len(self.audit_manager.incident_history)
        resolved = len([e for e in self.audit_manager.execution_history 
                       if "Executed" in e.get("status", "")])
        
        if total_incidents > 0:
            success_rate = (resolved / total_incidents) * 100
            self.learning_stats.update({
                "total_incidents": total_incidents,
                "resolved_automatically": resolved,
                "success_rate": f"{success_rate:.1f}%",
                "graph_nodes": total_incidents,
                "graph_edges": total_incidents * 2  # Rough estimate
            })
        
        return self.learning_stats
    
    def update_learning_stats(self) -> Dict[str, Any]:
        """Update and return learning stats"""
        return self.get_learning_stats()
    
    def analyze_patterns(self) -> Dict[str, Any]:
        """Analyze patterns in incident history"""
        incidents = list(self.audit_manager.incident_history)
        
        if not incidents:
            return {"status": "No incidents to analyze"}
        
        # Analyze incident types
        type_counts = {}
        severity_counts = {1: 0, 2: 0, 3: 0}
        
        for incident in incidents:
            inc_type = incident.get("type", "Unknown")
            type_counts[inc_type] = type_counts.get(inc_type, 0) + 1
            severity_counts[incident.get("severity", 2)] += 1
        
        # Find most common incidents
        most_common = sorted(type_counts.items(), key=lambda x: x[1], reverse=True)[:5]
        
        # Calculate metrics
        total_incidents = len(incidents)
        avg_severity = sum(severity_counts.values()) / total_incidents if total_incidents > 0 else 0
        
        return {
            "total_incidents_analyzed": total_incidents,
            "most_common_incidents": dict(most_common),
            "severity_distribution": severity_counts,
            "average_severity": f"{avg_severity:.2f}",
            "time_period_covered": "Last 24 hours",
            "recommendations": [
                f"Focus on resolving {most_common[0][0]} incidents (most frequent)" if most_common else "No patterns found",
                "Implement proactive monitoring for high-severity patterns",
                "Review incident resolution times for optimization",
                "Update runbooks based on frequent patterns"
            ]
        }
    
    def search_similar_incidents(self, query: str) -> List[List[str]]:
        """Search for similar incidents"""
        if not query.strip():
            return []
        
        # Mock search - in real app this would use embeddings
        incidents = list(self.audit_manager.incident_history)[:10]
        
        results = []
        for i, incident in enumerate(incidents[:5]):  # Return top 5
            similarity = 0.7 + (i * 0.05)  # Mock similarity score
            results.append([
                incident.get("type", "Unknown"),
                f"{similarity:.0%}",
                "✅ Resolved" if i % 2 == 0 else "⚠️ Pending",
                f"{random.randint(1, 5)} actions"
            ])
        
        return results
    
    def export_graph_data(self) -> str:
        """Export graph data"""
        # Mock export
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.json')
        temp_file.write(json.dumps({
            "nodes": len(self.audit_manager.incident_history),
            "edges": len(self.audit_manager.incident_history) * 2,
            "exported_at": datetime.datetime.now().isoformat(),
            "incidents": list(self.audit_manager.incident_history)[:10]
        }, default=str).encode())
        temp_file.close()
        return temp_file.name
    
    def export_embeddings(self) -> str:
        """Export embeddings"""
        # Mock export
        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.bin')
        temp_file.write(b"Mock embeddings data")
        temp_file.close()
        return temp_file.name

# ===========================================
# MAIN INTERFACE
# ===========================================

def create_interface() -> gr.Blocks:
    """Create the comprehensive Gradio interface"""
    
    # Initialize managers
    audit_manager = AuditTrailManager()
    business_logic = EnhancedBusinessLogic(audit_manager)
    viz_engine = EnhancedVisualizationEngine()
    
    custom_css = """
    .gradio-container { max-width: 1400px !important; margin: auto !important; }
    h1, h2, h3 { color: #1a365d !important; }
    .critical { color: #FF6B6B !important; font-weight: bold; }
    .success { color: #4ECDC4 !important; font-weight: bold; }
    """
    
    with gr.Blocks(
        title="🚀 ARF Investor Demo v3.8.0",
        theme=gr.themes.Soft(),
        css=custom_css
    ) as demo:
        
        # ============ HEADER ============
        arf_status = "✅ ARF OSS v3.3.6" if ARF_OSS_AVAILABLE else "⚠️ Simulation Mode"
        
        gr.Markdown(f"""
        # 🚀 Agentic Reliability Framework - Investor Demo v3.8.0
        ## From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability
        
        <div style='color: #666; font-size: 16px; margin-top: 10px;'>
        {arf_status} | Experience: <b>OSS (Advisory)</b> ↔ <b>Enterprise (Autonomous)</b>
        </div>
        """)
        
        # ============ MAIN 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:"
                        )
                        
                        # Scenario description
                        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"
                        )
                        
                        gr.Markdown("### 💰 Business Impact")
                        impact_display = gr.JSON(
                            value=INCIDENT_SCENARIOS["Cache Miss Storm"]["impact"],
                            label="Impact Analysis"
                        )
                    
                    # Right Panel
                    with gr.Column(scale=2):
                        # Visualization
                        gr.Markdown("### 📈 Incident Timeline")
                        timeline_output = gr.Plot()
                        
                        # Action Section
                        gr.Markdown("### ⚡ Take Action")
                        with gr.Row():
                            oss_btn = gr.Button("🆓 Run OSS Analysis", variant="secondary")
                            enterprise_btn = gr.Button("🚀 Execute Enterprise Healing", variant="primary")
                        
                        # Approval Controls
                        with gr.Row():
                            approval_toggle = gr.Checkbox(
                                label="🔐 Require Manual Approval",
                                value=True,
                                info="Toggle to show approval workflow vs auto-execution"
                            )
                            demo_mode_btn = gr.Button("⚡ Quick Demo", variant="secondary", size="sm")
                        
                        # Approval Display
                        approval_display = gr.HTML(
                            value="<div style='padding: 10px; background: #f8f9fa; border-radius: 5px;'>Approval status will appear here</div>"
                        )
                        
                        # Configuration
                        config_display = gr.JSON(
                            label="⚙️ Enterprise Configuration",
                            value={"approval_required": True, "compliance_mode": "strict"}
                        )
                        
                        # Results
                        results_display = gr.JSON(
                            label="🎯 Execution Results",
                            value={"status": "Ready for execution..."}
                        )
            
            # TAB 2: BUSINESS IMPACT & ROI
            with gr.TabItem("💰 Business Impact & ROI"):
                with gr.Column():
                    # Business Dashboard
                    gr.Markdown("### 📊 Executive Business Dashboard")
                    dashboard_output = gr.Plot()
                    
                    # ROI Calculator
                    gr.Markdown("### 🧮 Interactive ROI Calculator")
                    with gr.Row():
                        with gr.Column(scale=1):
                            monthly_slider = gr.Slider(
                                1, 100, value=15, step=1,
                                label="Monthly incidents"
                            )
                            impact_slider = gr.Slider(
                                1000, 50000, value=8500, step=500,
                                label="Average incident impact ($)"
                            )
                            team_slider = gr.Slider(
                                1, 20, value=5, step=1,
                                label="Reliability team size"
                            )
                            calculate_btn = gr.Button("Calculate My ROI", variant="primary")
                        
                        with gr.Column(scale=2):
                            roi_output = gr.JSON(
                                label="Your ROI Analysis",
                                value={"analysis": "Adjust sliders and click Calculate"}
                            )
                    
                    # ROI Metrics
                    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)")
                        
                        # Controls
                        with gr.Row():
                            refresh_btn = gr.Button("🔄 Refresh", variant="secondary", size="sm")
                            clear_btn = gr.Button("🗑️ Clear History", variant="stop", size="sm")
                            export_btn = gr.Button("📥 Export JSON", variant="secondary", size="sm")
                            analyze_btn = gr.Button("🧠 AI Analysis", variant="primary", size="sm")
                        
                        # Execution Table
                        execution_table = gr.Dataframe(
                            headers=["Time", "Scenario", "Actions", "Status", "Savings", "Details"],
                            value=audit_manager.get_execution_history_table(),
                            label="📋 Execution History",
                            interactive=False,
                            wrap=True,
                            datatype=["str", "str", "str", "str", "str", "str"]
                        )
                        
                        gr.Markdown("### 📈 Visual History")
                        execution_chart = gr.Plot()
                        
                        # AI Analysis Results
                        gr.Markdown("### 🧠 AI Pattern Analysis")
                        ai_analysis = gr.JSON(
                            value={"status": "Click 'AI Analysis' to run pattern detection"},
                            label="Pattern Recognition Results"
                        )
                    
                    # 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="📊 Incident History",
                            interactive=False,
                            wrap=True
                        )
                        
                        # Memory Graph Visualization
                        gr.Markdown("### 🧠 Memory Graph")
                        memory_graph = gr.Plot()
                        
                        # Export Area
                        gr.Markdown("### 📤 Export & Analytics")
                        export_text = gr.Textbox(
                            label="Full Audit Trail (JSON)",
                            value=audit_manager.export_audit_trail(),
                            lines=10,
                            max_lines=15
                        )
                        
                        with gr.Row():
                            download_btn = gr.Button("💾 Download JSON", variant="secondary")
                            compliance_btn = gr.Button("📋 Generate Compliance Report", variant="secondary")

            # TAB 4: ENTERPRISE FEATURES & LICENSE
            with gr.TabItem("🏢 Enterprise Features"):
                with gr.Row():
                    # Left Column - License Management
                    with gr.Column(scale=1):
                        gr.Markdown("### 🔐 License Management")
                        
                        # License Info Display
                        license_display = gr.JSON(
                            value=business_logic.license_info,
                            label="License Information"
                        )
                        
                        # License Controls
                        with gr.Row():
                            validate_btn = gr.Button("🔍 Validate License", variant="secondary")
                            trial_btn = gr.Button("🆓 Start 30-Day Trial", variant="primary")
                            upgrade_btn = gr.Button("🚀 Upgrade Tier", variant="secondary")
                        
                        # Feature Comparison
                        gr.Markdown("### ⚡ Feature Matrix")
                        
                        features_data = [
                            ["🤖 Autonomous Healing", "❌", "✅ Auto", "Enterprise Only"],
                            ["📊 Executive Dashboards", "Basic", "Advanced", "✅ Comprehensive"],
                            ["🔐 Compliance Automation", "❌", "✅", "✅ SOC2/GDPR"],
                            ["📈 Predictive Analytics", "❌", "Basic", "✅ ML-Powered"],
                            ["🔄 Auto-Remediation", "Manual", "✅ Auto", "✅ AI-Driven"],
                            ["🎯 SLA Guarantees", "❌", "❌", "✅ 99.9%"],
                            ["📊 Cost Optimization", "Basic", "Advanced", "✅ AI-Optimized"],
                            ["🔒 Role-Based Access", "❌", "✅", "✅ Granular"],
                            ["📝 Audit Trail", "Basic", "✅", "✅ Comprehensive"],
                            ["🔄 Multi-Cloud", "❌", "❌", "✅ Native"],
                            ["🧠 Learning Engine", "❌", "Basic", "✅ Continuous"],
                            ["📋 Compliance Reports", "❌", "❌", "✅ Automated"],
                        ]
                        
                        features_table = gr.Dataframe(
                            value=features_data,
                            headers=["Feature", "OSS", "Starter", "Enterprise"],
                            label="Feature Comparison Matrix",
                            interactive=False,
                            wrap=True
                        )
                    
                    # Right Column - Compliance & Integration
                    with gr.Column(scale=1):
                        # Compliance Status
                        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"},
                                "FedRAMP": {"status": "🔄 Moderate Pending", "phase": "Assessment"},
                                "CCPA": {"status": "✅ Compliant", "verified": True}
                            },
                            label="Compliance Certifications"
                        )
                        
                        # Integration Status
                        gr.Markdown("### 🔗 Integration Hub")
                        
                        integrations_data = [
                            ["AWS", "CloudWatch, S3, Lambda", "✅ Connected", "Last sync: 5min ago"],
                            ["Azure", "Monitor, Log Analytics", "✅ Connected", "Last sync: 8min ago"],
                            ["GCP", "Operations, BigQuery", "✅ Connected", "Last sync: 3min ago"],
                            ["Datadog", "Metrics, Logs, APM", "✅ Connected", "Active"],
                            ["New Relic", "Full-stack", "✅ Connected", "Active"],
                            ["PagerDuty", "Incident Response", "✅ Connected", "On-call active"],
                            ["ServiceNow", "ITSM & CMDB", "✅ Connected", "Last sync: 15min ago"],
                            ["Slack", "Notifications", "✅ Connected", "#arf-alerts"],
                            ["Teams", "Notifications", "✅ Connected", "General channel"],
                            ["Jira", "Issue Tracking", "✅ Connected", "ARF project"],
                            ["GitHub", "CI/CD", "✅ Connected", "Webhooks active"],
                            ["GitLab", "CI/CD", "✅ Connected", "Pipelines active"],
                        ]
                        
                        integrations_table = gr.Dataframe(
                            value=integrations_data,
                            headers=["Platform", "Services", "Status", "Details"],
                            label="Active Integrations",
                            interactive=False,
                            wrap=True
                        )
                        
                        # MCP Mode Selector
                        gr.Markdown("### ⚙️ MCP Execution Mode")
                        mcp_mode = gr.Radio(
                            choices=["advisory", "approval", "autonomous"],
                            value="approval",
                            label="Select MCP Mode:",
                            info="Controls how healing actions are executed"
                        )
                        
                        update_mode_btn = gr.Button("🔄 Update Mode", variant="secondary")

            # TAB 5: MEMORY & LEARNING ENGINE
            with gr.TabItem("🧠 Memory & Learning"):
                with gr.Row():
                    # Left Column - Memory Graph
                    with gr.Column(scale=2):
                        gr.Markdown("### 🧠 Incident Memory Graph")
                        
                        # Graph Controls
                        with gr.Row():
                            graph_type = gr.Radio(
                                choices=["Force Directed", "Hierarchical", "Timeline"],
                                value="Force Directed",
                                label="Graph Type"
                            )
                            show_weights = gr.Checkbox(label="Show Edge Weights", value=True)
                            auto_layout = gr.Checkbox(label="Auto-Layout", value=True)
                        
                        # Graph Visualization
                        memory_graph_plot = gr.Plot()
                        
                        # Node Details
                        gr.Markdown("### 🔍 Selected Node Details")
                        node_details = gr.JSON(
                            value={"select": "a node in the graph to see details"},
                            label="Node Information"
                        )
                    
                    # Right Column - Learning Engine
                    with gr.Column(scale=1):
                        # Similarity Search
                        gr.Markdown("### 🔎 Similarity Search")
                        
                        search_query = gr.Textbox(
                            label="Search for similar incidents",
                            placeholder="Describe incident or paste metrics...",
                            lines=3
                        )
                        
                        with gr.Row():
                            search_btn = gr.Button("🔍 Search Memory", variant="primary")
                            clear_search_btn = gr.Button("Clear", variant="secondary")
                        
                        search_results = gr.Dataframe(
                            headers=["Incident", "Similarity", "Resolution", "Actions"],
                            value=[],
                            label="Search Results",
                            interactive=False
                        )
                        
                        # Learning Statistics
                        gr.Markdown("### 📊 Learning Engine Stats")
                        
                        learning_stats = gr.JSON(
                            value=business_logic.learning_stats,
                            label="Learning Engine Statistics"
                        )
                        
                        # Export Learning Data
                        gr.Markdown("### 📤 Export Learning Data")
                        with gr.Row():
                            export_graph_btn = gr.Button("💾 Export Graph", variant="secondary")
                            export_embeddings_btn = gr.Button("💾 Export Embeddings", variant="secondary")
                        
                        export_status = gr.Textbox(
                            label="Export Status",
                            value="Ready for export...",
                            interactive=False
                        )

        # ===========================================
        # EVENT HANDLERS
        # ===========================================
        
        def update_scenario(scenario_name: str) -> Tuple[str, Dict, Dict, go.Figure, Dict]:
            """Update scenario display"""
            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(),
                {"status": "Ready to analyze..."}
            )
        
        scenario_dropdown.change(
            fn=update_scenario,
            inputs=[scenario_dropdown],
            outputs=[scenario_description, metrics_display, impact_display, timeline_output, results_display]
        )
        
        def run_oss_analysis(scenario_name: str) -> Tuple[Dict, List[List[str]], go.Figure]:
            """Run OSS analysis"""
            analysis = business_logic.run_oss_analysis(scenario_name)
            incident_table_data = audit_manager.get_incident_history_table()
            graph_plot = viz_engine.create_memory_graph(audit_manager)
            
            return analysis, incident_table_data, graph_plot
        
        oss_btn.click(
            fn=run_oss_analysis,
            inputs=[scenario_dropdown],
            outputs=[results_display, incident_table, memory_graph]
        )
        
        def execute_healing(scenario_name: str, approval_required: bool) -> Tuple[str, Dict, Dict, go.Figure, List[List[str]], List[List[str]], Dict]:
            """Execute enterprise healing"""
            results = business_logic.execute_enterprise_healing(scenario_name, approval_required)
            new_stats = business_logic.update_learning_stats()
            
            return results + (new_stats,)
        
        enterprise_btn.click(
            fn=execute_healing,
            inputs=[scenario_dropdown, approval_toggle],
            outputs=[
                approval_display, config_display, results_display, 
                execution_chart, execution_table, incident_table,
                learning_stats
            ]
        )
        
        def run_quick_demo() -> Tuple[str, Dict, Dict, str, Dict, go.Figure, List[List[str]], List[List[str]], go.Figure, gr.Checkbox, Dict, go.Figure]:
            """Run quick demo"""
            analysis = business_logic.run_oss_analysis("Cache Miss Storm")
            results = business_logic.execute_enterprise_healing("Cache Miss Storm", False)
            new_stats = business_logic.update_learning_stats()
            
            return (
                "<div style='padding: 15px; background: #d4edda; border-radius: 8px; border-left: 4px solid #28a745;'>"
                "⚡ Quick Demo Completed!<br>"
                "1. ✅ OSS Analysis Completed<br>"
                "2. ✅ Enterprise Healing Executed<br>"
                "3. ✅ Audit Trail Updated<br>"
                "4. ✅ ROI Calculated<br>"
                "5. ✅ Learning Engine Updated"
                "</div>",
                {"status": "demo_completed", "mode": "autonomous"},
                analysis,
                results[0],
                results[2],
                viz_engine.create_execution_history_chart(audit_manager),
                audit_manager.get_execution_history_table(),
                audit_manager.get_incident_history_table(),
                viz_engine.create_incident_timeline(),
                approval_toggle.update(value=False),
                new_stats,
                viz_engine.create_memory_graph(audit_manager)
            )
        
        demo_mode_btn.click(
            fn=run_quick_demo,
            outputs=[
                scenario_description,
                config_display,
                results_display,
                approval_display,
                results_display,
                execution_chart,
                execution_table,
                incident_table,
                timeline_output,
                approval_toggle,
                learning_stats,
                memory_graph
            ]
        )
        
        def calculate_roi(monthly_incidents: int, avg_impact: int, team_size: int) -> Dict:
            """Calculate ROI"""
            return business_logic.calculate_roi(monthly_incidents, avg_impact, team_size)
        
        calculate_btn.click(
            fn=calculate_roi,
            inputs=[monthly_slider, impact_slider, team_slider],
            outputs=[roi_output]
        )
        
        # Slider updates
        for slider in [monthly_slider, impact_slider, team_slider]:
            slider.change(
                fn=calculate_roi,
                inputs=[monthly_slider, impact_slider, team_slider],
                outputs=[roi_output]
            )
        
        # Tab 3: Audit Trail Handlers
        def refresh_history() -> Tuple[List[List[str]], List[List[str]], go.Figure, str, go.Figure]:
            """Refresh history display"""
            return (
                audit_manager.get_execution_history_table(),
                audit_manager.get_incident_history_table(),
                viz_engine.create_execution_history_chart(audit_manager),
                audit_manager.export_audit_trail(),
                viz_engine.create_memory_graph(audit_manager)
            )
        
        refresh_btn.click(
            fn=refresh_history,
            outputs=[execution_table, incident_table, execution_chart, export_text, memory_graph]
        )
        
        def clear_history() -> Tuple[List[List[str]], List[List[str]], go.Figure, str, go.Figure]:
            """Clear history"""
            execution_table_data, incident_table_data = audit_manager.clear_history()
            return (
                execution_table_data,
                incident_table_data,
                viz_engine.create_execution_history_chart(audit_manager),
                audit_manager.export_audit_trail(),
                viz_engine.create_memory_graph(audit_manager)
            )
        
        clear_btn.click(
            fn=clear_history,
            outputs=[execution_table, incident_table, execution_chart, export_text, memory_graph]
        )
        
        def run_ai_analysis() -> Tuple[Dict, go.Figure]:
            """Run AI pattern analysis"""
            analysis = business_logic.analyze_patterns()
            pattern_chart = viz_engine.create_pattern_analysis_chart(analysis)
            return analysis, pattern_chart
        
        analyze_btn.click(
            fn=run_ai_analysis,
            outputs=[ai_analysis, execution_chart]
        )
        
        def update_export() -> str:
            """Update export text"""
            return audit_manager.export_audit_trail()
        
        export_btn.click(
            fn=update_export,
            outputs=[export_text]
        )
        
        # Tab 4: Enterprise Features Handlers
        def validate_license() -> Dict:
            """Validate license"""
            return business_logic.validate_license()
        
        validate_btn.click(
            fn=validate_license,
            outputs=[license_display]
        )
        
        def start_trial() -> Dict:
            """Start trial"""
            return business_logic.start_trial()
        
        trial_btn.click(
            fn=start_trial,
            outputs=[license_display]
        )
        
        def upgrade_tier() -> Dict:
            """Upgrade license tier"""
            return business_logic.upgrade_license()
        
        upgrade_btn.click(
            fn=upgrade_tier,
            outputs=[license_display]
        )
        
        def update_mcp_mode(mode: str) -> Dict:
            """Update MCP mode"""
            return business_logic.update_mcp_mode(mode)
        
        update_mode_btn.click(
            fn=update_mcp_mode,
            inputs=[mcp_mode],
            outputs=[config_display]
        )
        
        # Tab 5: Memory & Learning Handlers
        def update_graph_view(graph_type: str, show_weights: bool, auto_layout: bool) -> go.Figure:
            """Update graph view"""
            return viz_engine.create_memory_graph(
                audit_manager, 
                graph_type=graph_type,
                show_weights=show_weights,
                auto_layout=auto_layout
            )
        
        for control in [graph_type, show_weights, auto_layout]:
            control.change(
                fn=update_graph_view,
                inputs=[graph_type, show_weights, auto_layout],
                outputs=[memory_graph_plot]
            )
        
        def search_memory(query: str) -> Tuple[List[List[str]], str]:
            """Search memory"""
            if not query.strip():
                return [], "Enter a search query"
            
            results = business_logic.search_similar_incidents(query)
            return results, f"Found {len(results)} similar incidents"
        
        search_btn.click(
            fn=search_memory,
            inputs=[search_query],
            outputs=[search_results, export_status]
        )
        
        def clear_search() -> Tuple[List, str]:
            """Clear search"""
            return [], "Search cleared"
        
        clear_search_btn.click(
            fn=clear_search,
            outputs=[search_results, export_status]
        )
        
        def export_graph_data() -> str:
            """Export graph data"""
            export_path = business_logic.export_graph_data()
            return f"Graph exported to: {export_path}"
        
        export_graph_btn.click(
            fn=export_graph_data,
            outputs=[export_status]
        )
        
        def export_embeddings() -> str:
            """Export embeddings"""
            export_path = business_logic.export_embeddings()
            return f"Embeddings exported to: {export_path}"
        
        export_embeddings_btn.click(
            fn=export_embeddings,
            outputs=[export_status]
        )
        
        # ===========================================
        # INITIALIZATION
        # ===========================================
        
        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),
                business_logic.get_learning_stats()
            ),
            outputs=[dashboard_output, timeline_output, execution_chart, memory_graph_plot, learning_stats]
        )
        
        # ============ FOOTER ============
        gr.Markdown("""
        ---
        ### 🚀 ARF Enterprise Platform
        
        **Tabs Overview:**
        1. **🔥 Live Incident Demo** - Experience OSS vs Enterprise healing
        2. **💰 Business Impact & ROI** - Calculate your savings potential
        3. **📜 Audit Trail & History** - Complete compliance logging
        4. **🏢 Enterprise Features** - License & compliance management
        5. **🧠 Memory & Learning** - AI-powered incident memory
        
        **Get Started:**
        • **Free Trial:** 30-day enterprise trial  
        • **Contact:** sales@arfinvestor.com  
        • **Docs:** docs.arfinvestor.com/enterprise
        • **Slack:** Join 2,500+ engineers
        
        <div style="text-align: center; color: #666; margin-top: 20px;">
        <small>© 2024 Agentic Reliability Framework. Demo v3.8.0 Enterprise Edition</small>
        </div>
        """)
    
    return demo

# ===========================================
# MAIN EXECUTION
# ===========================================

if __name__ == "__main__":
    print("🚀 Starting ARF Ultimate Investor Demo v3.8.0 (Enterprise Edition)...")
    print("📊 Features: 5 Tabs, Memory Graph, License Management, Compliance")
    print("🧠 Learning Engine: Pattern Analysis, Similarity Search")
    print("🌐 Opening web interface...")
    
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False,
        debug=True
    )