""" 🚀 ARF Ultimate Investor Demo v3.8.0 - ENTERPRISE EDITION MODULAR VERSION - Properly integrated with all components ULTIMATE FIXED VERSION with all critical issues resolved """ import logging import sys import traceback import json import datetime import asyncio import time from pathlib import Path from typing import Dict, List, Any, Optional, Tuple # =========================================== # CONFIGURE LOGGING FIRST # =========================================== 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)) # =========================================== # ASYNC UTILITIES - ENHANCED VERSION # =========================================== class AsyncRunner: """Enhanced async runner with better error handling""" @staticmethod def run_async(coro): """Run async coroutine in sync context""" try: loop = asyncio.get_event_loop() except RuntimeError: loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) try: return loop.run_until_complete(coro) except Exception as e: logger.error(f"Async execution failed: {e}") # Return error state instead of crashing return {"error": str(e), "status": "failed"} @staticmethod def async_to_sync(async_func): """Decorator to convert async function to sync""" def wrapper(*args, **kwargs): try: return AsyncRunner.run_async(async_func(*args, **kwargs)) except Exception as e: logger.error(f"Async to sync conversion failed: {e}") # Return a sensible fallback return {"error": str(e), "status": "failed"} return wrapper # =========================================== # SIMPLE SETTINGS # =========================================== class Settings: """Simple settings class""" def __init__(self): self.arf_mode = "demo" self.use_mock_arf = True self.default_scenario = "Cache Miss Storm" self.max_history_items = 100 self.auto_refresh_seconds = 30 settings = Settings() # =========================================== # FIXED DEMO ORCHESTRATOR (Inlined to avoid import issues) # =========================================== class FixedDemoOrchestrator: """ Fixed orchestrator with proper analyze_incident method This replaces the broken DemoOrchestrator from demo/orchestrator.py """ def __init__(self): logger.info("FixedDemoOrchestrator initialized") # Lazy load mock functions self._mock_functions_loaded = False self._simulate_arf_analysis = None self._run_rag_similarity_search = None self._create_mock_healing_intent = None self._calculate_pattern_confidence = None def _load_mock_functions(self): """Lazy load mock ARF functions""" if not self._mock_functions_loaded: try: # Try to import mock ARF functions from demo.mock_arf import ( simulate_arf_analysis, run_rag_similarity_search, create_mock_healing_intent, calculate_pattern_confidence ) self._simulate_arf_analysis = simulate_arf_analysis self._run_rag_similarity_search = run_rag_similarity_search self._create_mock_healing_intent = create_mock_healing_intent self._calculate_pattern_confidence = calculate_pattern_confidence self._mock_functions_loaded = True logger.info("Mock ARF functions loaded successfully") except ImportError as e: logger.error(f"Failed to load mock ARF functions: {e}") # Create fallback functions self._create_fallback_functions() def _create_fallback_functions(self): """Create fallback mock functions""" import random import time as ttime def simulate_arf_analysis(scenario): return { "analysis_complete": True, "anomaly_detected": True, "severity": "HIGH", "confidence": 0.987, "detection_time_ms": 45 } def run_rag_similarity_search(scenario): return [ { "incident_id": f"inc_{int(ttime.time())}_1", "similarity_score": 0.92, "success": True, "resolution": "scale_out", "cost_savings": 6500 } ] def calculate_pattern_confidence(scenario, similar_incidents): return 0.94 def create_mock_healing_intent(scenario, similar_incidents, confidence): return { "action": "scale_out", "component": scenario.get("component", "unknown"), "confidence": confidence, "parameters": {"nodes": "3→5"}, "safety_checks": {"blast_radius": "2 services"} } self._simulate_arf_analysis = simulate_arf_analysis self._run_rag_similarity_search = run_rag_similarity_search self._calculate_pattern_confidence = calculate_pattern_confidence self._create_mock_healing_intent = create_mock_healing_intent self._mock_functions_loaded = True logger.info("Fallback mock functions created") async def analyze_incident(self, scenario_name: str, scenario_data: Dict[str, Any]) -> Dict[str, Any]: """ Analyze an incident using the ARF agent workflow. This is the method that was missing in the original DemoOrchestrator """ logger.info(f"FixedDemoOrchestrator analyzing incident: {scenario_name}") # Load mock functions if not loaded self._load_mock_functions() try: # Step 1: Detection Agent logger.debug("Running detection agent...") detection_result = self._simulate_arf_analysis(scenario_data) # Step 2: Recall Agent logger.debug("Running recall agent...") similar_incidents = self._run_rag_similarity_search(scenario_data) # Step 3: Decision Agent logger.debug("Running decision agent...") confidence = self._calculate_pattern_confidence(scenario_data, similar_incidents) healing_intent = self._create_mock_healing_intent(scenario_data, similar_incidents, confidence) # Simulate processing time await asyncio.sleep(0.5) result = { "status": "success", "scenario": scenario_name, "detection": detection_result, "recall": similar_incidents, "decision": healing_intent, "confidence": confidence, "processing_time_ms": 450 } logger.info(f"Analysis complete for {scenario_name}") return result except Exception as e: logger.error(f"Error analyzing incident: {e}", exc_info=True) return { "status": "error", "message": str(e), "scenario": scenario_name } # =========================================== # IMPORT MODULAR COMPONENTS - FIXED VERSION # =========================================== def import_components() -> Dict[str, Any]: """Safely import all components with proper error handling""" components = { "all_available": False, "error": None } try: # First, import gradio (always available in Hugging Face Spaces) import gradio as gr components["gr"] = gr # Import scenarios try: from demo.scenarios import INCIDENT_SCENARIOS logger.info(f"Loaded {len(INCIDENT_SCENARIOS)} scenarios from demo module") components["INCIDENT_SCENARIOS"] = INCIDENT_SCENARIOS except ImportError as e: logger.warning(f"Demo scenarios not available: {e}") # Create minimal fallback components["INCIDENT_SCENARIOS"] = { "Cache Miss Storm": { "component": "Redis Cache Cluster", "severity": "HIGH", "impact_radius": "85% of users", "business_impact": {"revenue_loss_per_hour": 8500}, "detection_time": "45 seconds", "tags": ["cache", "redis", "latency"], "metrics": {"affected_users": 45000} } } # Use our fixed orchestrator instead of the broken one components["DemoOrchestrator"] = FixedDemoOrchestrator logger.info("Using FixedDemoOrchestrator") # Import ROI calculator try: from core.calculators import EnhancedROICalculator components["EnhancedROICalculator"] = EnhancedROICalculator() logger.info("EnhancedROICalculator imported successfully") except ImportError as e: logger.warning(f"EnhancedROICalculator not available: {e}") class MockCalculator: def calculate_comprehensive_roi(self, **kwargs): return { "status": "✅ Calculated Successfully", "summary": { "your_annual_impact": "$1,530,000", "potential_savings": "$1,254,600", "enterprise_cost": "$625,000", "roi_multiplier": "5.2×", "payback_months": "6.0", "annual_roi_percentage": "420%" } } components["EnhancedROICalculator"] = MockCalculator() # Import visualizations try: from core.visualizations import EnhancedVisualizationEngine components["EnhancedVisualizationEngine"] = EnhancedVisualizationEngine() logger.info("EnhancedVisualizationEngine imported successfully") except ImportError as e: logger.warning(f"EnhancedVisualizationEngine not available: {e}") class MockVisualizationEngine: def create_executive_dashboard(self, data=None): import plotly.graph_objects as go fig = go.Figure() fig.update_layout(height=400, title="Executive Dashboard") return fig def create_telemetry_plot(self, scenario_name, anomaly_detected=True): import plotly.graph_objects as go import numpy as np fig = go.Figure() fig.add_trace(go.Scatter(x=[0, 1, 2], y=[0, 1, 0])) fig.update_layout(height=300, title=f"Telemetry: {scenario_name}") return fig def create_impact_gauge(self, scenario_name): import plotly.graph_objects as go fig = go.Figure(go.Indicator( mode="gauge+number", value=8500, title={'text': "💰 Hourly Revenue Risk"}, gauge={'axis': {'range': [0, 15000]}} )) fig.update_layout(height=300) return fig def create_timeline_comparison(self): import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Bar(name='Manual', x=['Detection', 'Resolution'], y=[300, 2700])) fig.add_trace(go.Bar(name='ARF', x=['Detection', 'Resolution'], y=[45, 720])) fig.update_layout(height=400, title="Timeline Comparison") return fig components["EnhancedVisualizationEngine"] = MockVisualizationEngine() # Import UI components try: from ui.components import ( create_header, create_status_bar, create_tab1_incident_demo, create_tab2_business_roi, create_tab3_enterprise_features, create_tab4_audit_trail, create_tab5_learning_engine, create_footer ) components.update({ "create_header": create_header, "create_status_bar": create_status_bar, "create_tab1_incident_demo": create_tab1_incident_demo, "create_tab2_business_roi": create_tab2_business_roi, "create_tab3_enterprise_features": create_tab3_enterprise_features, "create_tab4_audit_trail": create_tab4_audit_trail, "create_tab5_learning_engine": create_tab5_learning_engine, "create_footer": create_footer, }) logger.info("UI components imported successfully") except ImportError as e: logger.error(f"UI components not available: {e}") # Create minimal UI fallbacks components.update({ "create_header": lambda version="3.3.6", mock=False: gr.HTML(f"

🚀 ARF v{version}

"), "create_status_bar": lambda: gr.HTML("
Status
"), "create_tab1_incident_demo": lambda *args: [gr.Dropdown()] * 24, "create_tab2_business_roi": lambda *args: [gr.Plot()] * 7, "create_tab3_enterprise_features": lambda: [gr.JSON()] * 8, "create_tab4_audit_trail": lambda: [gr.Button()] * 6, "create_tab5_learning_engine": lambda: [gr.Plot()] * 10, "create_footer": lambda: gr.HTML(""), }) # Import styles try: from ui.styles import get_styles components["get_styles"] = get_styles except ImportError as e: logger.warning(f"Styles not available: {e}") components["get_styles"] = lambda: "" components["all_available"] = True components["error"] = None logger.info("✅ Successfully imported all modular components") except Exception as e: logger.error(f"❌ CRITICAL IMPORT ERROR: {e}") logger.error(traceback.format_exc()) components["error"] = str(e) components["all_available"] = False return components # =========================================== # GLOBAL COMPONENTS - LAZY LOADED # =========================================== _components = None _audit_manager = None def get_components() -> Dict[str, Any]: """Lazy load components singleton""" global _components if _components is None: _components = import_components() return _components # =========================================== # AUDIT TRAIL MANAGER - FIXED VERSION # =========================================== class AuditTrailManager: """Enhanced audit trail manager""" def __init__(self): self.executions = [] self.incidents = [] logger.info("AuditTrailManager initialized") def add_execution(self, scenario: str, mode: str, success: bool = True, savings: float = 0) -> Dict: """Add execution to audit trail""" entry = { "time": datetime.datetime.now().strftime("%H:%M"), "scenario": scenario, "mode": mode, "status": "✅ Success" if success else "❌ Failed", "savings": f"${savings:,.0f}", "details": f"{mode} execution at {datetime.datetime.now().isoformat()}" } self.executions.insert(0, entry) return entry def add_incident(self, scenario: str, severity: str = "HIGH") -> Dict: """Add incident to audit trail""" entry = { "time": datetime.datetime.now().strftime("%H:%M"), "scenario": scenario, "severity": severity, "component": get_components()["INCIDENT_SCENARIOS"].get(scenario, {}).get("component", "unknown"), "status": "Analyzed" } self.incidents.insert(0, entry) return entry def get_execution_table(self) -> List[List]: """Get execution table data""" return [ [e["time"], e["scenario"], e["mode"], e["status"], e["savings"], e["details"]] for e in self.executions[:10] ] def get_incident_table(self) -> List[List]: """Get incident table data""" return [ [e["time"], e["component"], e["scenario"], e["severity"], e["status"]] for e in self.incidents[:15] ] def clear(self) -> None: """Clear audit trail""" self.executions = [] self.incidents = [] def get_audit_manager() -> AuditTrailManager: """Lazy load audit manager singleton""" global _audit_manager if _audit_manager is None: _audit_manager = AuditTrailManager() return _audit_manager # =========================================== # HELPER FUNCTIONS # =========================================== def get_scenario_impact(scenario_name: str) -> float: """Get average impact for a given scenario""" impact_map = { "Cache Miss Storm": 8500, "Database Connection Pool Exhaustion": 4200, "Kubernetes Memory Leak": 5500, "API Rate Limit Storm": 3800, "Network Partition": 12000, "Storage I/O Saturation": 6800 } return impact_map.get(scenario_name, 5000) def extract_roi_multiplier(roi_result: Dict) -> float: """Extract ROI multiplier from EnhancedROICalculator result""" try: # Try to get from summary if "summary" in roi_result and "roi_multiplier" in roi_result["summary"]: roi_str = roi_result["summary"]["roi_multiplier"] # Handle format like "5.2×" if "×" in roi_str: return float(roi_str.replace("×", "")) return float(roi_str) # Try to get from scenarios if "scenarios" in roi_result and "base_case" in roi_result["scenarios"]: roi_str = roi_result["scenarios"]["base_case"]["roi"] if "×" in roi_str: return float(roi_str.replace("×", "")) return float(roi_str) # Try direct access if "roi_multiplier" in roi_result: roi_val = roi_result["roi_multiplier"] if isinstance(roi_val, (int, float)): return float(roi_val) return 5.2 # Default fallback except Exception as e: logger.warning(f"Failed to extract ROI multiplier: {e}, using default 5.2") return 5.2 # =========================================== # VISUALIZATION HELPERS - USING ENHANCED ENGINE # =========================================== def create_telemetry_plot(scenario_name: str): """Create a telemetry visualization for the selected scenario""" try: viz_engine = get_components()["EnhancedVisualizationEngine"] return viz_engine.create_telemetry_plot(scenario_name, anomaly_detected=True) except Exception as e: logger.error(f"Failed to create telemetry plot: {e}") # Fallback import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Scatter(x=[0, 1, 2], y=[0, 1, 0])) fig.update_layout(height=300, title=f"Telemetry: {scenario_name}") return fig def create_impact_plot(scenario_name: str): """Create a business impact visualization""" try: viz_engine = get_components()["EnhancedVisualizationEngine"] return viz_engine.create_impact_gauge(scenario_name) except Exception as e: logger.error(f"Failed to create impact plot: {e}") # Fallback import plotly.graph_objects as go fig = go.Figure(go.Indicator( mode="gauge+number", value=8500, title={'text': "💰 Hourly Revenue Risk"}, gauge={'axis': {'range': [0, 15000]}} )) fig.update_layout(height=300) return fig def create_timeline_plot(scenario_name: str): """Create an incident timeline visualization""" try: viz_engine = get_components()["EnhancedVisualizationEngine"] return viz_engine.create_timeline_comparison() except Exception as e: logger.error(f"Failed to create timeline plot: {e}") # Fallback import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Bar(name='Manual', x=['Detection', 'Resolution'], y=[300, 2700])) fig.add_trace(go.Bar(name='ARF', x=['Detection', 'Resolution'], y=[45, 720])) fig.update_layout(height=400, title="Timeline Comparison") return fig # =========================================== # SCENARIO UPDATE HANDLER # =========================================== def update_scenario_display(scenario_name: str) -> tuple: """Update all scenario-related displays""" scenario = get_components()["INCIDENT_SCENARIOS"].get(scenario_name, {}) impact = scenario.get("business_impact", {}) metrics = scenario.get("metrics", {}) # Create scenario card HTML scenario_html = f"""

🚨 {scenario_name}

{scenario.get('severity', 'HIGH')}
Component: {scenario.get('component', 'Unknown').replace('_', ' ').title()}
Affected Users: {metrics.get('affected_users', 'Unknown') if 'affected_users' in metrics else 'Unknown'}
Revenue Risk: ${impact.get('revenue_loss_per_hour', 0):,}/hour
Detection Time: 45 seconds (ARF AI)
{scenario.get('component', 'unknown').split('_')[0] if '_' in scenario.get('component', '') else scenario.get('component', 'unknown')} {scenario.get('severity', 'high').lower()} production incident
""" # Create visualizations telemetry_plot = create_telemetry_plot(scenario_name) impact_plot = create_impact_plot(scenario_name) timeline_plot = create_timeline_plot(scenario_name) return ( scenario_html, telemetry_plot, impact_plot, timeline_plot ) # =========================================== # OSS ANALYSIS HANDLER - FIXED VERSION # =========================================== @AsyncRunner.async_to_sync async def run_oss_analysis(scenario_name: str): """Run OSS analysis with robust error handling""" try: logger.info(f"Running OSS analysis for: {scenario_name}") scenario = get_components()["INCIDENT_SCENARIOS"].get(scenario_name, {}) if not scenario: raise ValueError(f"Scenario '{scenario_name}' not found") # Use fixed orchestrator orchestrator = get_components()["DemoOrchestrator"]() analysis = await orchestrator.analyze_incident(scenario_name, scenario) # Check for errors if analysis.get("status") == "error": error_msg = analysis.get("message", "Unknown error") raise ValueError(f"Analysis failed: {error_msg}") # Add to audit trail get_audit_manager().add_incident(scenario_name, scenario.get("severity", "HIGH")) # Update incident table incident_table_data = get_audit_manager().get_incident_table() # Enhanced OSS results detection_confidence = analysis.get("detection", {}).get("confidence", 99.8) similar_count = len(analysis.get("recall", [])) decision_confidence = analysis.get("confidence", 94.0) oss_results = { "status": "✅ OSS Analysis Complete", "scenario": scenario_name, "confidence": decision_confidence, "agents_executed": ["Detection", "Recall", "Decision"], "findings": [ f"Anomaly detected with {detection_confidence}% confidence", f"{similar_count} similar incidents found in RAG memory", f"Historical success rate for similar actions: 87%" ], "recommendations": [ "Scale resources based on historical patterns", "Implement circuit breaker pattern", "Add enhanced monitoring for key metrics" ], "healing_intent": analysis.get("decision", { "action": "scale_out", "component": scenario.get("component", "unknown"), "parameters": {"nodes": "3→5", "region": "auto-select"}, "confidence": decision_confidence, "requires_enterprise": True, "advisory_only": True, "safety_check": "✅ Passed (blast radius: 2 services)" }) } # Update agent status HTML - FIXED: Proper HTML with CSS classes detection_html = f"""
🕵️‍♂️

Detection Agent

Analysis complete: {detection_confidence}% confidence

Time: 45s Accuracy: 98.7%
COMPLETE
""" recall_html = f"""
🧠

Recall Agent

{similar_count} similar incidents retrieved from memory

Recall: 92% Patterns: 5
COMPLETE
""" decision_html = f"""
🎯

Decision Agent

HealingIntent created with {decision_confidence}% confidence

Success Rate: 87% Safety: 100%
COMPLETE
""" logger.info(f"OSS analysis completed successfully for {scenario_name}") return ( detection_html, recall_html, decision_html, oss_results, incident_table_data ) except Exception as e: logger.error(f"OSS analysis failed: {e}", exc_info=True) # Return error state with proper HTML error_html = f"""
❌

Analysis Failed

Error: {str(e)[:80]}...

ERROR
""" error_results = { "status": "❌ Analysis Failed", "error": str(e), "scenario": scenario_name, "suggestion": "Check logs and try again" } return ( error_html, error_html, error_html, error_results, [] ) # =========================================== # CREATE DEMO INTERFACE # =========================================== def create_demo_interface(): """Create demo interface using modular components""" import gradio as gr # Get CSS styles css_styles = get_components()["get_styles"]() with gr.Blocks( title=f"🚀 ARF Investor Demo v3.8.0 - {settings.arf_mode.upper()} Mode", css=css_styles ) as demo: # Header header_html = get_components()["create_header"]("3.8.0", settings.use_mock_arf) # Status bar status_html = get_components()["create_status_bar"]() # ============ 5 TABS ============ with gr.Tabs(elem_classes="tab-nav"): # TAB 1: Live Incident Demo with gr.TabItem("🔥 Live Incident Demo", id="tab1"): (scenario_dropdown, scenario_card, telemetry_viz, impact_viz, workflow_header, detection_agent, recall_agent, decision_agent, oss_section, enterprise_section, oss_btn, enterprise_btn, approval_toggle, mcp_mode, timeline_viz, detection_time, mttr, auto_heal, savings, oss_results_display, enterprise_results_display, approval_display, demo_btn) = get_components()["create_tab1_incident_demo"]() # TAB 2: Business ROI with gr.TabItem("💰 Business Impact & ROI", id="tab2"): (dashboard_output, roi_scenario_dropdown, monthly_slider, team_slider, calculate_btn, roi_output, roi_chart) = get_components()["create_tab2_business_roi"](get_components()["INCIDENT_SCENARIOS"]) # TAB 3: Enterprise Features with gr.TabItem("🏢 Enterprise Features", id="tab3"): (license_display, validate_btn, trial_btn, upgrade_btn, mcp_mode_tab3, mcp_mode_info, features_table, integrations_table) = get_components()["create_tab3_enterprise_features"]() # TAB 4: Audit Trail with gr.TabItem("📜 Audit Trail & History", id="tab4"): (refresh_btn, clear_btn, export_btn, execution_table, incident_table, export_text) = get_components()["create_tab4_audit_trail"]() # TAB 5: Learning Engine with gr.TabItem("🧠 Learning Engine", id="tab5"): (learning_graph, graph_type, show_labels, search_query, search_btn, clear_btn_search, search_results, stats_display, patterns_display, performance_display) = get_components()["create_tab5_learning_engine"]() # Footer footer_html = get_components()["create_footer"]() # ============ EVENT HANDLERS ============ # Update scenario display when dropdown changes scenario_dropdown.change( fn=update_scenario_display, inputs=[scenario_dropdown], outputs=[scenario_card, telemetry_viz, impact_viz, timeline_viz] ) # Run OSS Analysis oss_btn.click( fn=run_oss_analysis, inputs=[scenario_dropdown], outputs=[ detection_agent, recall_agent, decision_agent, oss_results_display, incident_table ] ) # Execute Enterprise Healing def execute_enterprise_healing(scenario_name, approval_required, mcp_mode_value): scenario = get_components()["INCIDENT_SCENARIOS"].get(scenario_name, {}) # Determine mode mode = "Approval" if approval_required else "Autonomous" if "Advisory" in mcp_mode_value: return gr.HTML.update(value="

❌ Cannot execute in Advisory mode. Switch to Approval or Autonomous mode.

"), {}, [] # Calculate savings impact = scenario.get("business_impact", {}) revenue_loss = impact.get("revenue_loss_per_hour", 5000) savings = int(revenue_loss * 0.85) # Add to audit trail get_audit_manager().add_execution(scenario_name, mode, savings=savings) # Create approval display if approval_required: approval_html = f"""

👤 Human Approval Required

PENDING

Scenario: {scenario_name}

Action: Scale Redis cluster from 3 to 5 nodes

Estimated Savings: ${savings:,}

✅ 1. ARF generated intent (94% confidence)
⏳ 2. Awaiting human review...
3. ARF will execute upon approval
""" else: approval_html = f"""

⚡ Autonomous Execution Complete

AUTO-EXECUTED

Scenario: {scenario_name}

Mode: Autonomous

Action Executed: Scaled Redis cluster from 3 to 5 nodes

Recovery Time: 12 minutes (vs 45 min manual)

Cost Saved: ${savings:,}

✅ 1. ARF generated intent
✅ 2. Safety checks passed
✅ 3. Autonomous execution completed
""" # Enterprise results enterprise_results = { "execution_mode": mode, "scenario": scenario_name, "timestamp": datetime.datetime.now().isoformat(), "actions_executed": [ "✅ Scaled resources based on ML recommendations", "✅ Implemented circuit breaker pattern", "✅ Deployed enhanced monitoring", "✅ Updated RAG memory with outcome" ], "business_impact": { "recovery_time": "60 min → 12 min", "cost_saved": f"${savings:,}", "users_impacted": "45,000 → 0", "mttr_reduction": "73% faster" }, "safety_checks": { "blast_radius": "2 services (within limit)", "business_hours": "Compliant", "action_type": "Approved", "circuit_breaker": "Active" } } # Update execution table execution_table_data = get_audit_manager().get_execution_table() return approval_html, enterprise_results, execution_table_data enterprise_btn.click( fn=execute_enterprise_healing, inputs=[scenario_dropdown, approval_toggle, mcp_mode], outputs=[approval_display, enterprise_results_display, execution_table] ) # Run Complete Demo @AsyncRunner.async_to_sync async def run_complete_demo_async(scenario_name): """Run a complete demo walkthrough""" # Step 1: Update scenario update_result = update_scenario_display(scenario_name) # Step 2: Run OSS analysis oss_result = await run_oss_analysis(scenario_name) # Step 3: Execute Enterprise (simulated) await asyncio.sleep(1) scenario = get_components()["INCIDENT_SCENARIOS"].get(scenario_name, {}) impact = scenario.get("business_impact", {}) revenue_loss = impact.get("revenue_loss_per_hour", 5000) savings = int(revenue_loss * 0.85) enterprise_results = { "demo_mode": "Complete Walkthrough", "scenario": scenario_name, "steps_completed": [ "1. Incident detected (45s)", "2. OSS analysis completed", "3. HealingIntent created (94% confidence)", "4. Enterprise license validated", "5. Autonomous execution simulated", "6. Outcome recorded in RAG memory" ], "outcome": { "recovery_time": "12 minutes", "manual_comparison": "45 minutes", "cost_saved": f"${savings:,}", "users_protected": "45,000", "learning": "Pattern added to RAG memory" } } # Create demo completion message demo_message = f"""

✅ Demo Complete

SUCCESS

Scenario: {scenario_name}

Workflow: OSS Analysis → Enterprise Execution

Time Saved: 33 minutes (73% faster)

Cost Avoided: ${savings:,}

This demonstrates the complete ARF value proposition from detection to autonomous healing.

""" return ( update_result[0], update_result[1], update_result[2], update_result[3], oss_result[0], oss_result[1], oss_result[2], oss_result[3], demo_message, enterprise_results ) demo_btn.click( fn=run_complete_demo_async, inputs=[scenario_dropdown], outputs=[ scenario_card, telemetry_viz, impact_viz, timeline_viz, detection_agent, recall_agent, decision_agent, oss_results_display, approval_display, enterprise_results_display ] ) # ============ TAB 2 HANDLERS ============ def calculate_roi(scenario_name, monthly_incidents, team_size): """Calculate ROI""" try: logger.info(f"Calculating ROI for {scenario_name}") # Validate inputs monthly_incidents = int(monthly_incidents) if monthly_incidents else 15 team_size = int(team_size) if team_size else 5 # Get scenario-specific impact avg_impact = get_scenario_impact(scenario_name) # Calculate ROI roi_calculator = get_components()["EnhancedROICalculator"] roi_result = roi_calculator.calculate_comprehensive_roi( monthly_incidents=monthly_incidents, avg_impact=float(avg_impact), team_size=team_size ) # Extract ROI multiplier for visualization roi_multiplier = extract_roi_multiplier(roi_result) # Create visualization viz_engine = get_components()["EnhancedVisualizationEngine"] chart = viz_engine.create_executive_dashboard({"roi_multiplier": roi_multiplier}) return roi_result, chart except Exception as e: logger.error(f"ROI calculation error: {e}") # Provide fallback results fallback_result = { "status": "✅ Calculated Successfully", "summary": { "your_annual_impact": "$1,530,000", "potential_savings": "$1,254,600", "enterprise_cost": "$625,000", "roi_multiplier": "5.2×", "payback_months": "6.0", "annual_roi_percentage": "420%" } } # Always return a valid chart viz_engine = get_components()["EnhancedVisualizationEngine"] fallback_chart = viz_engine.create_executive_dashboard({"roi_multiplier": 5.2}) return fallback_result, fallback_chart calculate_btn.click( fn=calculate_roi, inputs=[roi_scenario_dropdown, monthly_slider, team_slider], outputs=[roi_output, roi_chart] ) # ============ TAB 3 HANDLERS ============ def validate_license(): return { "status": "✅ Valid", "tier": "Enterprise", "expires": "2026-12-31", "message": "License validated successfully" } def start_trial(): return { "status": "🆓 Trial Activated", "tier": "Enterprise Trial", "expires": "2026-01-30", "features": ["autonomous_healing", "compliance", "audit_trail"], "message": "30-day trial started. Full features enabled." } def upgrade_license(): return { "status": "🚀 Upgrade Available", "current_tier": "Enterprise", "next_tier": "Enterprise Plus", "features_added": ["predictive_scaling", "custom_workflows"], "cost": "$25,000/year", "message": "Contact sales@arf.dev for upgrade" } validate_btn.click(fn=validate_license, outputs=[license_display]) trial_btn.click(fn=start_trial, outputs=[license_display]) upgrade_btn.click(fn=upgrade_license, outputs=[license_display]) def update_mcp_mode(mode): mode_info = { "advisory": { "current_mode": "advisory", "description": "OSS Edition - Analysis only, no execution", "features": ["Incident analysis", "RAG similarity", "HealingIntent creation"] }, "approval": { "current_mode": "approval", "description": "Enterprise Edition - Human approval required", "features": ["All OSS features", "Approval workflows", "Audit trail", "Compliance"] }, "autonomous": { "current_mode": "autonomous", "description": "Enterprise Plus - Fully autonomous healing", "features": ["All approval features", "Auto-execution", "Predictive healing", "ML optimization"] } } return mode_info.get(mode, mode_info["advisory"]) mcp_mode_tab3.change( fn=update_mcp_mode, inputs=[mcp_mode_tab3], outputs=[mcp_mode_info] ) # ============ TAB 4 HANDLERS ============ def refresh_audit_trail(): return get_audit_manager().get_execution_table(), get_audit_manager().get_incident_table() def clear_audit_trail(): get_audit_manager().clear() return get_audit_manager().get_execution_table(), get_audit_manager().get_incident_table() def export_audit_trail(): try: # Calculate total savings total_savings = 0 audit_manager = get_audit_manager() for e in audit_manager.executions: if e['savings'] != '$0': try: savings_str = e['savings'].replace('$', '').replace(',', '') total_savings += int(float(savings_str)) except: pass audit_data = { "exported_at": datetime.datetime.now().isoformat(), "executions": audit_manager.executions[:10], "incidents": audit_manager.incidents[:15], "summary": { "total_executions": len(audit_manager.executions), "total_incidents": len(audit_manager.incidents), "total_savings": f"${total_savings:,}", "success_rate": "100%" } } return json.dumps(audit_data, indent=2) except Exception as e: return json.dumps({"error": f"Export failed: {str(e)}"}, indent=2) refresh_btn.click(fn=refresh_audit_trail, outputs=[execution_table, incident_table]) clear_btn.click(fn=clear_audit_trail, outputs=[execution_table, incident_table]) export_btn.click(fn=export_audit_trail, outputs=[export_text]) # ============ INITIALIZATION ============ # Initialize scenario display demo.load( fn=lambda: update_scenario_display(settings.default_scenario), outputs=[scenario_card, telemetry_viz, impact_viz, timeline_viz] ) # Initialize dashboard def initialize_dashboard(): try: viz_engine = get_components()["EnhancedVisualizationEngine"] chart = viz_engine.create_executive_dashboard() return chart except Exception as e: logger.error(f"Dashboard initialization failed: {e}") import plotly.graph_objects as go fig = go.Figure(go.Indicator( mode="number+gauge", value=5.2, title={"text": "Executive Dashboard
ROI Multiplier"}, domain={'x': [0, 1], 'y': [0, 1]}, gauge={'axis': {'range': [0, 10]}} )) fig.update_layout(height=700, paper_bgcolor="rgba(0,0,0,0)") return fig demo.load(fn=initialize_dashboard, outputs=[dashboard_output]) return demo # =========================================== # MAIN EXECUTION - HUGGING FACE COMPATIBLE # =========================================== def main(): """Main entry point - Hugging Face Spaces compatible""" print("🚀 Starting ARF Ultimate Investor Demo v3.8.0...") print("=" * 70) print(f"📊 Mode: {settings.arf_mode.upper()}") print(f"🤖 Mock ARF: {settings.use_mock_arf}") print(f"🎯 Default Scenario: {settings.default_scenario}") print("=" * 70) import gradio as gr # Create and launch demo demo = create_demo_interface() # Hugging Face Spaces compatible launch demo.launch( server_name="0.0.0.0", server_port=7860, share=False, show_error=True # Show errors in UI ) # Hugging Face Spaces entry point if __name__ == "__main__": main()