Update app.py

app.py

@@ -890,4 +890,931 @@ class EnhancedBusinessLogic:
         )
     
     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
+    )