app.py

"""
🚀 ARF Ultimate Investor Demo v3.8.0 - ENTERPRISE EDITION
COMPLETE FIXED VERSION - All components integrated
"""

import logging
import sys
import traceback
import json
import datetime
import asyncio
import time
import numpy as np
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
sys.path.insert(0, str(Path(__file__).parent))

# Import Plotly early to ensure availability
try:
    import plotly.graph_objects as go
    import plotly.express as px
    from plotly.subplots import make_subplots
    PLOTLY_AVAILABLE = True
except ImportError:
    logger.warning("Plotly not available - visualizations will be simplified")
    PLOTLY_AVAILABLE = False

# ===========================================
# IMPORT MODULAR COMPONENTS
# ===========================================
try:
    # Import scenarios from your modular file
    from demo.scenarios import INCIDENT_SCENARIOS as SCENARIOS_DATA
    
    # Import orchestrator
    from demo.orchestrator import DemoOrchestrator
    
    # Import UI components
    from ui.components import (
        create_header, create_status_bar, create_tab1_incident_demo,
        create_tab2_business_roi, create_tab3_audit_trail,
        create_tab4_enterprise_features, create_tab5_learning_engine,
        create_footer
    )
    
    logger.info("✅ Successfully imported all modular components")
    
except ImportError as e:
    logger.error(f"Failed to import components: {e}")
    logger.error(traceback.format_exc())
    # Fallback to inline definitions
    SCENARIOS_DATA = {}
    DemoOrchestrator = None

# ===========================================
# ENHANCED SCENARIOS WITH OSS vs ENTERPRISE SEPARATION
# ===========================================
ENHANCED_SCENARIOS = {
    "Cache Miss Storm": {
        "description": "Redis cluster experiencing 80% cache miss rate causing database overload",
        "severity": "CRITICAL",
        "component": "redis_cache",
        "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 RESULTS - ADVISORY ONLY
        "oss_results": {
            "status": "✅ OSS Analysis Complete",
            "confidence": 0.87,
            "similar_incidents": 3,
            "rag_similarity_score": 0.72,
            "recommendations": [
                "Scale Redis cache memory from 4GB → 8GB",
                "Implement cache warming strategy",
                "Optimize key patterns with TTL adjustments",
                "Add circuit breaker for database fallback"
            ],
            "estimated_time": "60+ minutes manually",
            "engineers_needed": "2-3 SREs + 1 DBA",
            "advisory_only": True,
            "healing_intent": {
                "action": "scale_out",
                "component": "redis_cache",
                "parameters": {"scale_factor": 2.0},
                "confidence": 0.87,
                "requires_enterprise": True
            }
        },
        # ENTERPRISE RESULTS - ACTUAL EXECUTION
        "enterprise_results": {
            "execution_mode": "Autonomous",
            "actions_executed": [
                "✅ 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"
            ],
            "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"
            },
            "audit_info": {
                "execution_id": "exec_001",
                "timestamp": datetime.datetime.now().isoformat(),
                "approval_required": False,
                "success": True
            }
        }
    },
    
    "Database Connection Pool Exhaustion": {
        "description": "PostgreSQL connection pool exhausted causing API timeouts",
        "severity": "HIGH",
        "component": "postgresql_database",
        "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"
        },
        "oss_results": {
            "status": "✅ OSS Analysis Complete",
            "confidence": 0.82,
            "similar_incidents": 2,
            "rag_similarity_score": 0.65,
            "recommendations": [
                "Increase connection pool size from 100 → 200",
                "Implement connection pooling monitoring",
                "Add query timeout enforcement",
                "Deploy read replica for read-heavy queries"
            ],
            "estimated_time": "45+ minutes manually",
            "engineers_needed": "1 DBA + 1 Backend Engineer",
            "advisory_only": True,
            "healing_intent": {
                "action": "scale_connection_pool",
                "component": "postgresql_database",
                "parameters": {"max_connections": 200},
                "confidence": 0.82,
                "requires_enterprise": True
            }
        },
        "enterprise_results": {
            "execution_mode": "Approval Required",
            "actions_executed": [
                "✅ Increased connection pool: 100 → 200 connections",
                "✅ Deployed real-time connection monitoring",
                "✅ Implemented query timeout: 30s → 10s",
                "✅ Automated read replica traffic routing"
            ],
            "metrics_improvement": {
                "API Latency": "2450ms → 320ms",
                "Error Rate": "15.2% → 0.8%",
                "Connection Wait": "45s → 120ms",
                "Throughput": "850 → 2100 req/sec"
            },
            "business_impact": {
                "Recovery Time": "45 min → 8 min",
                "Cost Saved": "$3,150",
                "Failed Transactions": "12,500 → 0",
                "SLA Compliance": "Restored to 99.9%"
            },
            "audit_info": {
                "execution_id": "exec_002",
                "timestamp": datetime.datetime.now().isoformat(),
                "approval_required": True,
                "success": True
            }
        }
    },
    
    "Kubernetes Memory Leak": {
        "description": "Java microservice memory leak causing pod restarts",
        "severity": "HIGH",
        "component": "java_payment_service",
        "metrics": {
            "Memory Usage": "96% (Critical)",
            "GC Pause Time": "4500ms",
            "Error Rate": "28.5%",
            "Pod Restarts": "12/hour",
            "Heap Fragmentation": "42%"
        },
        "impact": {
            "Revenue Loss": "$5,500/hour",
            "Session Loss": "8,500 users",
            "Payment Failures": "3.2% of transactions",
            "Support Tickets": "+300%"
        },
        "oss_results": {
            "status": "✅ OSS Analysis Complete",
            "confidence": 0.79,
            "similar_incidents": 4,
            "rag_similarity_score": 0.68,
            "recommendations": [
                "Increase pod memory limits from 2GB → 4GB",
                "Implement memory leak detection",
                "Deploy canary with fixed version",
                "Add circuit breaker for graceful degradation"
            ],
            "estimated_time": "90+ minutes manually",
            "engineers_needed": "2 Java Devs + 1 SRE",
            "advisory_only": True,
            "healing_intent": {
                "action": "scale_memory",
                "component": "java_payment_service",
                "parameters": {"memory_limit_gb": 4},
                "confidence": 0.79,
                "requires_enterprise": True
            }
        },
        "enterprise_results": {
            "execution_mode": "Autonomous with Rollback",
            "actions_executed": [
                "✅ Scaled pod memory: 2GB → 4GB with monitoring",
                "✅ Deployed memory leak detection service",
                "✅ Rolled out canary with memory fixes",
                "✅ Implemented auto-rollback on failure"
            ],
            "metrics_improvement": {
                "Memory Usage": "96% → 68%",
                "GC Pause Time": "4500ms → 320ms",
                "Error Rate": "28.5% → 1.2%",
                "Pod Stability": "12/hour → 0 restarts"
            },
            "business_impact": {
                "Recovery Time": "90 min → 15 min",
                "Cost Saved": "$4,950",
                "Transaction Success": "96.8% → 99.9%",
                "User Impact": "8,500 → 0 affected"
            },
            "audit_info": {
                "execution_id": "exec_003",
                "timestamp": datetime.datetime.now().isoformat(),
                "approval_required": False,
                "success": True
            }
        }
    },
    
    "API Rate Limit Storm": {
        "description": "Third-party API rate limiting causing cascading failures",
        "severity": "MEDIUM",
        "component": "external_api_gateway",
        "metrics": {
            "Rate Limit Hits": "95% of requests",
            "Error Rate": "42.8%",
            "Retry Storm": "Active",
            "Cascade Effect": "3 dependent services",
            "Queue Backlog": "8,500 requests"
        },
        "impact": {
            "Revenue Loss": "$3,800/hour",
            "Partner SLA Breach": "Yes",
            "Data Sync Delay": "4+ hours",
            "Customer Reports": "Delayed by 6 hours"
        },
        "oss_results": {
            "status": "✅ OSS Analysis Complete",
            "confidence": 0.85,
            "similar_incidents": 3,
            "rag_similarity_score": 0.71,
            "recommendations": [
                "Implement exponential backoff with jitter",
                "Deploy circuit breaker pattern",
                "Add request queuing with prioritization",
                "Implement adaptive rate limiting"
            ],
            "estimated_time": "75+ minutes manually",
            "engineers_needed": "2 Backend Engineers + 1 DevOps",
            "advisory_only": True,
            "healing_intent": {
                "action": "implement_rate_limiting",
                "component": "external_api_gateway",
                "parameters": {"backoff_strategy": "exponential"},
                "confidence": 0.85,
                "requires_enterprise": True
            }
        },
        "enterprise_results": {
            "execution_mode": "Autonomous",
            "actions_executed": [
                "✅ Implemented exponential backoff: 1s → 32s with jitter",
                "✅ Deployed circuit breaker with 80% success threshold",
                "✅ Added intelligent request queuing",
                "✅ Enabled adaptive rate limiting based on API health"
            ],
            "metrics_improvement": {
                "Rate Limit Hits": "95% → 12%",
                "Error Rate": "42.8% → 3.5%",
                "Successful Retries": "18% → 89%",
                "Queue Processing": "8,500 → 0 backlog"
            },
            "business_impact": {
                "Recovery Time": "75 min → 10 min",
                "Cost Saved": "$3,420",
                "SLA Compliance": "Restored within 5 minutes",
                "Data Freshness": "4+ hours → <5 minute delay"
            },
            "audit_info": {
                "execution_id": "exec_004",
                "timestamp": datetime.datetime.now().isoformat(),
                "approval_required": False,
                "success": True
            }
        }
    }
}

# ===========================================
# SIMPLE VISUALIZATION ENGINE (No external dependencies)
# ===========================================
class SimpleVizEngine:
    """Simple visualization engine that works without complex imports"""
    
    @staticmethod
    def create_timeline_plot(scenario_name="Incident"):
        """Create a simple timeline plot"""
        if not PLOTLY_AVAILABLE:
            # Return a placeholder if plotly not available
            import matplotlib.pyplot as plt
            import io
            import base64
            
            fig, ax = plt.subplots(figsize=(10, 4))
            events = ['Detection', 'Analysis', 'Action', 'Recovery']
            times = [0, 1, 2, 3]
            
            ax.plot(times, [1, 1, 1, 1], 'bo-', markersize=10)
            for i, (event, t) in enumerate(zip(events, times)):
                ax.text(t, 1.1, event, ha='center', fontsize=10)
            
            ax.set_ylim(0.5, 1.5)
            ax.set_xlim(-0.5, 3.5)
            ax.set_title(f'Timeline: {scenario_name}')
            ax.axis('off')
            
            buf = io.BytesIO()
            plt.savefig(buf, format='png', bbox_inches='tight')
            plt.close(fig)
            buf.seek(0)
            
            return f"data:image/png;base64,{base64.b64encode(buf.read()).decode()}"
        
        # Use Plotly if available
        fig = go.Figure()
        
        events = [
            {"time": "T-5m", "event": "Detection", "type": "detection"},
            {"time": "T-3m", "event": "OSS Analysis", "type": "analysis"},
            {"time": "T-2m", "event": "Enterprise Action", "type": "action"},
            {"time": "T-0m", "event": "Recovery", "type": "recovery"}
        ]
        
        for event in events:
            fig.add_trace(go.Scatter(
                x=[event["time"]],
                y=[1],
                mode='markers+text',
                marker=dict(size=20, color='#4ECDC4'),
                text=[event["event"]],
                textposition="top center"
            ))
        
        fig.update_layout(
            title=f"Timeline: {scenario_name}",
            height=300,
            showlegend=False,
            yaxis=dict(showticklabels=False, range=[0.5, 1.5]),
            margin=dict(l=20, r=20, t=40, b=20)
        )
        
        return fig
    
    @staticmethod
    def create_dashboard_plot():
        """Create simple dashboard plot"""
        if not PLOTLY_AVAILABLE:
            return None
        
        fig = make_subplots(rows=1, cols=2, subplot_titles=('Cost Savings', 'MTTR Improvement'))
        
        # Cost savings
        fig.add_trace(
            go.Bar(x=['Without ARF', 'With ARF'], y=[100, 25], name='Cost'),
            row=1, col=1
        )
        
        # MTTR improvement
        fig.add_trace(
            go.Bar(x=['Manual', 'ARF OSS', 'ARF Enterprise'], y=[120, 25, 8], name='MTTR'),
            row=1, col=2
        )
        
        fig.update_layout(height=400, showlegend=False)
        return fig

# ===========================================
# AUDIT TRAIL MANAGER
# ===========================================
class AuditTrailManager:
    def __init__(self):
        self.executions = []
        self.incidents = []
        
    def add_execution(self, scenario_name, mode, success=True, savings=0):
        entry = {
            "id": f"exec_{len(self.executions):03d}",
            "time": datetime.datetime.now().strftime("%H:%M"),
            "scenario": scenario_name,
            "mode": mode,
            "status": "✅ Success" if success else "❌ Failed",
            "savings": f"${savings:,}",
            "details": f"{mode} execution"
        }
        self.executions.insert(0, entry)
        return entry
    
    def add_incident(self, scenario_name, severity="HIGH"):
        entry = {
            "id": f"inc_{len(self.incidents):03d}",
            "time": datetime.datetime.now().strftime("%H:%M"),
            "scenario": scenario_name,
            "severity": severity,
            "component": ENHANCED_SCENARIOS.get(scenario_name, {}).get("component", "unknown"),
            "status": "Analyzed"
        }
        self.incidents.insert(0, entry)
        return entry
    
    def get_execution_table(self):
        return [
            [e["time"], e["scenario"], e["mode"], e["status"], e["savings"], e["details"]]
            for e in self.executions[:10]
        ]
    
    def get_incident_table(self):
        return [
            [e["time"], e["component"], e["scenario"], e["severity"], e["status"]]
            for e in self.incidents[:15]
        ]

# ===========================================
# CREATE DEMO INTERFACE - FIXED VERSION
# ===========================================
def create_demo_interface():
    """Create the demo interface with all fixes applied"""
    
    import gradio as gr
    
    # Initialize components
    viz_engine = SimpleVizEngine()
    audit_manager = AuditTrailManager()
    
    # Initialize orchestrator if available
    orchestrator = None
    if DemoOrchestrator:
        try:
            orchestrator = DemoOrchestrator()
        except:
            pass
    
    # Custom CSS for OSS vs Enterprise separation
    custom_css = """
    .oss-section {
        background: linear-gradient(135deg, #e3f2fd 0%, #bbdefb 100%) !important;
        border-left: 4px solid #2196f3 !important;
        padding: 15px !important;
        border-radius: 8px !important;
        margin-bottom: 15px !important;
    }
    .enterprise-section {
        background: linear-gradient(135deg, #e8f5e8 0%, #c8e6c9 100%) !important;
        border-left: 4px solid #4caf50 !important;
        padding: 15px !important;
        border-radius: 8px !important;
        margin-bottom: 15px !important;
    }
    .critical { color: #d32f2f !important; font-weight: bold; }
    .success { color: #388e3c !important; font-weight: bold; }
    """
    
    with gr.Blocks(title="🚀 ARF Investor Demo v3.8.0", css=custom_css) as demo:
        
        # Use your modular header
        create_header("3.3.6", False)  # OSS version, Mock mode
        
        # Status bar
        create_status_bar()
        
        # Tabs
        with gr.Tabs():
            
            # TAB 1: Live Incident Demo (Fixed)
            with gr.TabItem("🔥 Live Incident Demo"):
                # Get components from your UI module
                (scenario_dropdown, scenario_description, metrics_display, impact_display,
                 timeline_output, oss_btn, enterprise_btn, approval_toggle, demo_btn,
                 approval_display, config_display, results_display) = create_tab1_incident_demo(
                    ENHANCED_SCENARIOS, "Cache Miss Storm"
                )
                
                # Add OSS and Enterprise results displays
                with gr.Row():
                    with gr.Column():
                        gr.Markdown("### 📋 OSS Analysis Results (Advisory Only)")
                        oss_results = gr.JSON(
                            value={},
                            label=""
                        )
                    
                    with gr.Column():
                        gr.Markdown("### 🎯 Enterprise Execution Results")
                        enterprise_results = gr.JSON(
                            value={},
                            label=""
                        )
            
            # TAB 2: Business Impact & ROI
            with gr.TabItem("💰 Business Impact & ROI"):
                (dashboard_output, monthly_slider, impact_slider, team_slider,
                 calculate_btn, roi_output) = create_tab2_business_roi()
            
            # TAB 3: Audit Trail
            with gr.TabItem("📜 Audit Trail & History"):
                (refresh_btn, clear_btn, export_btn, execution_table, savings_chart,
                 incident_table, memory_graph, export_text) = create_tab3_audit_trail()
            
            # Other tabs...
            with gr.TabItem("🏢 Enterprise Features"):
                create_tab4_enterprise_features()
            
            with gr.TabItem("🧠 Learning Engine"):
                create_tab5_learning_engine()
        
        # Footer
        create_footer()
        
        # ============ EVENT HANDLERS (FIXED) ============
        
        # Update scenario (FIXED: Proper parameter handling)
        def update_scenario(scenario_name):
            scenario = ENHANCED_SCENARIOS.get(scenario_name, {})
            
            # Get timeline plot
            if PLOTLY_AVAILABLE:
                timeline = viz_engine.create_timeline_plot(scenario_name)
            else:
                timeline = None
            
            return (
                f"### {scenario_name}\n{scenario.get('description', 'No description')}",
                scenario.get("metrics", {}),
                scenario.get("impact", {}),
                timeline if timeline else gr.Plot(visible=False),
                {},  # Clear OSS results
                {}   # Clear Enterprise results
            )
        
        scenario_dropdown.change(
            fn=update_scenario,
            inputs=[scenario_dropdown],
            outputs=[scenario_description, metrics_display, impact_display,
                    timeline_output, oss_results, enterprise_results]
        )
        
        # Run OSS Analysis (FIXED: Proper async handling)
        async def run_oss_analysis(scenario_name):
            scenario = ENHANCED_SCENARIOS.get(scenario_name, {})
            
            # Add to audit trail
            audit_manager.add_incident(scenario_name, scenario.get("severity", "HIGH"))
            
            # Get OSS results
            oss_result = scenario.get("oss_results", {})
            
            # Update tables
            incident_table_data = audit_manager.get_incident_table()
            
            return oss_result, incident_table_data
        
        oss_btn.click(
            fn=run_oss_analysis,
            inputs=[scenario_dropdown],
            outputs=[oss_results, incident_table]
        )
        
        # Execute Enterprise Healing (FIXED: Proper parameter matching)
        def execute_enterprise_healing(scenario_name, approval_required):
            scenario = ENHANCED_SCENARIOS.get(scenario_name, {})
            
            # Get enterprise results
            enterprise_result = scenario.get("enterprise_results", {})
            
            # Determine mode
            mode = "Approval" if approval_required else "Autonomous"
            
            # Calculate savings from impact
            impact = scenario.get("impact", {})
            revenue_loss = impact.get("Revenue Loss", "$0")
            try:
                savings = int(revenue_loss.replace("$", "").replace(",", "").split("/")[0]) * 0.85
            except:
                savings = 5000
            
            # Add to audit trail
            audit_manager.add_execution(
                scenario_name,
                mode,
                savings=int(savings)
            )
            
            # Create approval display
            if approval_required:
                approval_html = f"""
                <div class='enterprise-section'>
                    <h4>✅ Approved & Executed</h4>
                    <p>Action for <strong>{scenario_name}</strong> was approved by system administrator and executed successfully.</p>
                    <p><strong>Mode:</strong> Manual Approval</p>
                    <p><strong>Cost Saved:</strong> ${int(savings):,}</p>
                </div>
                """
            else:
                approval_html = f"""
                <div class='enterprise-section'>
                    <h4>⚡ Auto-Executed</h4>
                    <p>Action for <strong>{scenario_name}</strong> was executed autonomously by ARF Enterprise.</p>
                    <p><strong>Mode:</strong> Fully Autonomous</p>
                    <p><strong>Cost Saved:</strong> ${int(savings):,}</p>
                </div>
                """
            
            # Update execution table
            execution_table_data = audit_manager.get_execution_table()
            
            return approval_html, enterprise_result, execution_table_data
        
        enterprise_btn.click(
            fn=execute_enterprise_healing,
            inputs=[scenario_dropdown, approval_toggle],
            outputs=[approval_display, enterprise_results, execution_table]
        )
        
        # Quick Demo (FIXED: Proper async)
        async def run_quick_demo():
            # Run OSS analysis
            scenario = ENHANCED_SCENARIOS["Cache Miss Storm"]
            oss_result = scenario.get("oss_results", {})
            
            # Execute enterprise
            enterprise_result = scenario.get("enterprise_results", {})
            
            # Update audit trail
            audit_manager.add_incident("Cache Miss Storm", "CRITICAL")
            audit_manager.add_execution("Cache Miss Storm", "Autonomous", savings=7200)
            
            # Get table data
            execution_table_data = audit_manager.get_execution_table()
            incident_table_data = audit_manager.get_incident_table()
            
            # Create approval display
            approval_html = """
            <div class='enterprise-section'>
                <h4>⚡ Quick Demo Completed</h4>
                <p>Full OSS analysis → Enterprise execution completed successfully.</p>
                <p><strong>Mode:</strong> Autonomous</p>
                <p><strong>Cost Saved:</strong> $7,200</p>
            </div>
            """
            
            return (
                oss_result,
                approval_html,
                enterprise_result,
                execution_table_data,
                incident_table_data,
                gr.Checkbox.update(value=False)
            )
        
        demo_btn.click(
            fn=run_quick_demo,
            outputs=[
                oss_results,
                approval_display,
                enterprise_results,
                execution_table,
                incident_table,
                approval_toggle
            ]
        )
        
        # ROI Calculator (FIXED)
        def calculate_roi(monthly, impact, team):
            if orchestrator:
                company_data = {
                    "monthly_incidents": monthly,
                    "avg_cost_per_incident": impact,
                    "team_size": team
                }
                roi_result = orchestrator.calculate_roi(company_data)
            else:
                # Simple calculation
                annual = monthly * 12 * impact
                savings = annual * 0.82
                team_cost = team * 150000
                roi_multiplier = savings / team_cost if team_cost > 0 else 0
                
                roi_result = {
                    "annual_impact": annual,
                    "team_cost": team_cost,
                    "potential_savings": savings,
                    "roi_multiplier": roi_multiplier,
                    "payback_months": (team_cost / (savings / 12)) if savings > 0 else 0
                }
            
            # Format for display
            formatted = {
                "Annual Impact": f"${roi_result.get('annual_impact', 0):,.0f}",
                "Team Cost": f"${roi_result.get('team_cost', 0):,.0f}",
                "Potential Savings": f"${roi_result.get('potential_savings', 0):,.0f}",
                "ROI Multiplier": f"{roi_result.get('roi_multiplier', 0):.1f}×",
                "Payback Period": f"{roi_result.get('payback_months', 0):.1f} months"
            }
            
            # Add dashboard
            dashboard = viz_engine.create_dashboard_plot()
            
            return formatted, dashboard
        
        calculate_btn.click(
            fn=calculate_roi,
            inputs=[monthly_slider, impact_slider, team_slider],
            outputs=[roi_output, dashboard_output]
        )
        
        # Audit Trail Refresh (FIXED)
        def refresh_audit_trail():
            return audit_manager.get_execution_table(), audit_manager.get_incident_table()
        
        refresh_btn.click(
            fn=refresh_audit_trail,
            outputs=[execution_table, incident_table]
        )
        
        # Clear History (FIXED)
        def clear_audit_trail():
            audit_manager.executions = []
            audit_manager.incidents = []
            return audit_manager.get_execution_table(), audit_manager.get_incident_table()
        
        clear_btn.click(
            fn=clear_audit_trail,
            outputs=[execution_table, incident_table]
        )
        
        # Initialize with first scenario
        demo.load(
            fn=lambda: update_scenario("Cache Miss Storm"),
            outputs=[scenario_description, metrics_display, impact_display,
                    timeline_output, oss_results, enterprise_results]
        )
    
    return demo

# ===========================================
# MAIN EXECUTION
# ===========================================
def main():
    """Main entry point"""
    print("🚀 Starting ARF Ultimate Investor Demo v3.8.0...")
    print("=" * 70)
    print("📊 Features:")
    print("  • 4 Enhanced Incident Scenarios")
    print("  • Clear OSS vs Enterprise Separation")
    print("  • Fixed Visualization Engine")
    print("  • Working Event Handlers")
    print("=" * 70)
    print("🌐 Opening web interface...")
    
    demo = create_demo_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=False
    )

if __name__ == "__main__":
    main()