""" 🚀 ARF ULTIMATE INVESTOR DEMO v3.3.7 Enhanced with professional visualizations, export features, and data persistence """ import asyncio import datetime import json import logging import time import uuid import random import base64 import io from typing import Dict, Any, List, Optional, Tuple from collections import defaultdict, deque import hashlib import gradio as gr import numpy as np import plotly.graph_objects as go import plotly.express as px import pandas as pd from plotly.subplots import make_subplots import matplotlib.pyplot as plt from matplotlib import font_manager import seaborn as sns # Import OSS components try: from agentic_reliability_framework.arf_core.models.healing_intent import ( HealingIntent, create_rollback_intent, create_restart_intent, create_scale_out_intent, ) from agentic_reliability_framework.arf_core.engine.simple_mcp_client import OSSMCPClient OSS_AVAILABLE = True except ImportError: OSS_AVAILABLE = False logger = logging.getLogger(__name__) logger.warning("OSS package not available") # ============================================================================ # DATA PERSISTENCE & SESSION MANAGEMENT # ============================================================================ class DemoSessionManager: """Manage session data persistence and historical trends""" def __init__(self): self.sessions = {} self.global_stats = { "total_sessions": 0, "total_revenue_protected": 0.0, "total_executions": 0, "historical_trends": deque(maxlen=100), # Last 100 data points "peak_performance": { "highest_roi": 0.0, "fastest_mttr": float('inf'), "largest_incident_resolved": 0.0, } } def start_session(self, session_id: str): """Start a new user session""" if session_id not in self.sessions: self.sessions[session_id] = { "start_time": time.time(), "actions": [], "metrics": {}, "scenarios_tried": set(), "roi_calculations": [], "exported_reports": [], } self.global_stats["total_sessions"] += 1 return self.sessions[session_id] def record_action(self, session_id: str, action: str, details: Dict[str, Any]): """Record user action with details""" if session_id in self.sessions: self.sessions[session_id]["actions"].append({ "timestamp": time.time(), "action": action, "details": details, }) # Update global historical trends if "revenue_protected" in details: self.global_stats["historical_trends"].append({ "timestamp": time.time(), "revenue": details["revenue_protected"], "session": session_id[-6:], # Last 6 chars for anonymity }) self.global_stats["total_revenue_protected"] += details["revenue_protected"] self.global_stats["total_executions"] += 1 # Update peak performance if details.get("revenue_protected", 0) > self.global_stats["peak_performance"]["largest_incident_resolved"]: self.global_stats["peak_performance"]["largest_incident_resolved"] = details["revenue_protected"] def get_session_summary(self, session_id: str) -> Dict[str, Any]: """Get summary of current session""" if session_id in self.sessions: session = self.sessions[session_id] duration = time.time() - session["start_time"] return { "session_duration": f"{duration/60:.1f} minutes", "total_actions": len(session["actions"]), "scenarios_tried": len(session["scenarios_tried"]), "roi_calculations": len(session["roi_calculations"]), "last_action": session["actions"][-1]["action"] if session["actions"] else "None", "session_id_short": session_id[-8:], } return {} def get_historical_trends_chart(self): """Create historical trends visualization""" if not self.global_stats["historical_trends"]: return go.Figure() # Prepare data data = list(self.global_stats["historical_trends"]) df = pd.DataFrame(data) # Create figure with subplots fig = make_subplots( rows=2, cols=2, subplot_titles=('Revenue Protection Over Time', 'Cumulative Revenue', 'Session Activity', 'Performance Metrics'), specs=[[{'type': 'scatter'}, {'type': 'scatter'}], [{'type': 'bar'}, {'type': 'indicator'}]], vertical_spacing=0.15, horizontal_spacing=0.15 ) # Revenue over time fig.add_trace( go.Scatter( x=df['timestamp'], y=df['revenue'], mode='lines+markers', name='Revenue Protected', line=dict(color='#4CAF50', width=3), marker=dict(size=8), hovertemplate='$%{y:,.0f}
%{text}', text=[f"Session: {s}" for s in df['session']] ), row=1, col=1 ) # Cumulative revenue cumulative_rev = df['revenue'].cumsum() fig.add_trace( go.Scatter( x=df['timestamp'], y=cumulative_rev, mode='lines', name='Cumulative Revenue', line=dict(color='#2196F3', width=3, dash='dash'), fill='tozeroy', fillcolor='rgba(33, 150, 243, 0.1)' ), row=1, col=2 ) # Session activity (group by session) session_counts = df['session'].value_counts().head(10) fig.add_trace( go.Bar( x=session_counts.index, y=session_counts.values, name='Actions per Session', marker_color='#FF9800', hovertemplate='Session: %{x}
Actions: %{y}' ), row=2, col=1 ) # Performance indicator avg_revenue = df['revenue'].mean() if len(df) > 0 else 0 fig.add_trace( go.Indicator( mode="gauge+number+delta", value=avg_revenue, title={'text': "Avg Revenue/Incident"}, delta={'reference': 100000, 'increasing': {'color': "#4CAF50"}}, gauge={ 'axis': {'range': [None, max(500000, avg_revenue * 1.5)]}, 'bar': {'color': "#4CAF50"}, 'steps': [ {'range': [0, 100000], 'color': '#FFEBEE'}, {'range': [100000, 300000], 'color': '#FFCDD2'}, {'range': [300000, 500000], 'color': '#EF9A9A'} ], 'threshold': { 'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 250000 } } ), row=2, col=2 ) # Update layout fig.update_layout( title="📈 Historical Performance Trends", height=700, showlegend=True, plot_bgcolor='white', paper_bgcolor='white', ) # Update axes fig.update_xaxes(title_text="Time", row=1, col=1) fig.update_yaxes(title_text="Revenue ($)", row=1, col=1) fig.update_xaxes(title_text="Time", row=1, col=2) fig.update_yaxes(title_text="Cumulative Revenue ($)", row=1, col=2) fig.update_xaxes(title_text="Session", row=2, col=1) fig.update_yaxes(title_text="Actions", row=2, col=1) return fig # ============================================================================ # ENHANCED VISUALIZATION ENGINE # ============================================================================ class EnhancedVisualizationEngine: """Enhanced visualization engine with animations and interactivity""" @staticmethod def create_animated_radar_chart(metrics: Dict[str, float], title: str = "Performance Radar"): """Create animated radar chart with smooth transitions""" categories = list(metrics.keys()) values = list(metrics.values()) # Create radar chart fig = go.Figure() fig.add_trace(go.Scatterpolar( r=values, theta=categories, fill='toself', name='Current', line_color='#4CAF50', opacity=0.8 )) # Add ideal baseline (for comparison) baseline_values = [max(values) * 0.8] * len(values) fig.add_trace(go.Scatterpolar( r=baseline_values, theta=categories, fill='toself', name='Ideal Baseline', line_color='#2196F3', opacity=0.3 )) fig.update_layout( polar=dict( radialaxis=dict( visible=True, range=[0, max(values) * 1.2] )), showlegend=True, title=title, height=400, animations=[{ 'frame': {'duration': 500, 'redraw': True}, 'transition': {'duration': 300, 'easing': 'cubic-in-out'}, }] ) return fig @staticmethod def create_heatmap_timeline(scenarios: List[Dict[str, Any]]): """Create heatmap timeline of incidents""" # Prepare data severity_map = {"critical": 3, "high": 2, "medium": 1, "low": 0} data = [] for i, scenario in enumerate(scenarios): impact = scenario.get("business_impact", {}) severity_val = severity_map.get( "critical" if impact.get("revenue_at_risk", 0) > 1000000 else "high" if impact.get("revenue_at_risk", 0) > 500000 else "medium" if impact.get("revenue_at_risk", 0) > 100000 else "low", 0 ) data.append({ "Scenario": scenario.get("description", "Unknown")[:30] + "...", "Revenue Risk": impact.get("revenue_at_risk", 0), "Users Impacted": impact.get("users_impacted", 0), "Severity": severity_val, "Time to Resolve": impact.get("time_to_resolve", 0), }) df = pd.DataFrame(data) # Create heatmap fig = go.Figure(data=go.Heatmap( z=df[['Revenue Risk', 'Users Impacted', 'Severity', 'Time to Resolve']].values.T, x=df['Scenario'], y=['Revenue Risk ($)', 'Users Impacted', 'Severity Level', 'Time to Resolve (min)'], colorscale='RdYlGn_r', # Red to Green (reversed for severity) showscale=True, hoverongaps=False, hovertemplate='%{x}
%{y}: %{z}' )) fig.update_layout( title="🔥 Incident Heatmap Timeline", xaxis_title="Scenarios", yaxis_title="Metrics", height=400, xaxis={'tickangle': 45}, ) return fig @staticmethod def create_real_time_metrics_stream(): """Create real-time streaming metrics visualization""" # Generate sample streaming data times = pd.date_range(start='now', periods=50, freq='1min') values = np.cumsum(np.random.randn(50)) + 100 fig = go.Figure() fig.add_trace(go.Scatter( x=times, y=values, mode='lines+markers', name='System Health Score', line=dict(color='#2196F3', width=3), marker=dict(size=6), hovertemplate='Time: %{x}
Score: %{y:.1f}' )) # Add threshold lines fig.add_hline(y=95, line_dash="dash", line_color="green", annotation_text="Optimal", annotation_position="right") fig.add_hline(y=80, line_dash="dash", line_color="orange", annotation_text="Warning", annotation_position="right") fig.add_hline(y=70, line_dash="dash", line_color="red", annotation_text="Critical", annotation_position="right") # Add range slider fig.update_layout( title="📊 Real-time System Health Monitor", xaxis=dict( rangeselector=dict( buttons=list([ dict(count=15, label="15m", step="minute", stepmode="backward"), dict(count=1, label="1h", step="hour", stepmode="backward"), dict(count=6, label="6h", step="hour", stepmode="backward"), dict(step="all") ]) ), rangeslider=dict(visible=True), type="date" ), yaxis_title="Health Score", height=400, showlegend=True ) return fig @staticmethod def create_3d_rag_graph(incidents: List[Dict], outcomes: List[Dict], edges: List[Dict]): """Create 3D visualization of RAG graph""" if not incidents: return go.Figure() # Prepare 3D coordinates np.random.seed(42) # For reproducibility # Incident nodes (red to orange based on severity) incident_coords = [] incident_colors = [] incident_sizes = [] incident_labels = [] for inc in incidents: incident_coords.append([ np.random.uniform(-1, 0), # x: negative side np.random.uniform(-1, 1), # y np.random.uniform(0, 1) # z: incidents on bottom layer ]) severity = inc.get("severity", "medium") if severity == "critical": incident_colors.append("#FF4444") # Bright red incident_sizes.append(20) elif severity == "high": incident_colors.append("#FF9800") # Orange incident_sizes.append(15) else: incident_colors.append("#FFC107") # Amber incident_sizes.append(10) incident_labels.append(f"{inc.get('component', 'Unknown')}
{severity.upper()}") # Outcome nodes (green gradient based on success) outcome_coords = [] outcome_colors = [] outcome_sizes = [] outcome_labels = [] for out in outcomes: outcome_coords.append([ np.random.uniform(0, 1), # x: positive side np.random.uniform(-1, 1), # y np.random.uniform(0, 1) # z ]) if out.get("success", False): outcome_colors.append("#4CAF50") # Green outcome_sizes.append(12) else: outcome_colors.append("#F44336") # Red outcome_sizes.append(12) outcome_labels.append(f"{out.get('action', 'Unknown')}
{'✅' if out.get('success') else '❌'}") # Create figure fig = go.Figure() # Add incident nodes fig.add_trace(go.Scatter3d( x=[c[0] for c in incident_coords], y=[c[1] for c in incident_coords], z=[c[2] for c in incident_coords], mode='markers+text', marker=dict( size=incident_sizes, color=incident_colors, symbol='circle', line=dict(color='white', width=2) ), text=incident_labels, textposition="top center", name='Incidents', hoverinfo='text', )) # Add outcome nodes fig.add_trace(go.Scatter3d( x=[c[0] for c in outcome_coords], y=[c[1] for c in outcome_coords], z=[c[2] for c in outcome_coords], mode='markers+text', marker=dict( size=outcome_sizes, color=outcome_colors, symbol='diamond', line=dict(color='white', width=1) ), text=outcome_labels, textposition="top center", name='Outcomes', hoverinfo='text', )) # Add edges (connections) edge_x, edge_y, edge_z = [], [], [] for edge in edges: source_idx = int(edge["source"].split("_")[1]) if "_" in edge["source"] else 0 target_idx = int(edge["target"].split("_")[1]) if "_" in edge["target"] else 0 if source_idx < len(incident_coords) and target_idx < len(outcome_coords): # Edge from incident to outcome edge_x += [incident_coords[source_idx][0], outcome_coords[target_idx][0], None] edge_y += [incident_coords[source_idx][1], outcome_coords[target_idx][1], None] edge_z += [incident_coords[source_idx][2], outcome_coords[target_idx][2], None] fig.add_trace(go.Scatter3d( x=edge_x, y=edge_y, z=edge_z, mode='lines', line=dict(color='rgba(100, 100, 100, 0.5)', width=2), hoverinfo='none', showlegend=False )) # Update layout fig.update_layout( title="🧠 3D RAG Knowledge Graph", scene=dict( xaxis_title="Incidents ← → Outcomes", yaxis_title="", zaxis_title="Knowledge Depth", camera=dict( eye=dict(x=1.5, y=1.5, z=1.5) ), aspectmode='manual', aspectratio=dict(x=2, y=1, z=1) ), height=600, showlegend=True, ) return fig # ============================================================================ # EXPORT ENGINE # ============================================================================ class ExportEngine: """Handle export of reports, charts, and data""" @staticmethod def export_roi_report_as_html(roi_data: Dict[str, Any]) -> str: """Export ROI report as HTML""" html = f""" ARF ROI Report - {datetime.datetime.now().strftime('%Y-%m-%d')}

🚀 ARF ROI Analysis Report

Generated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}

📊 Executive Summary

Investment Payback: {roi_data.get('payback_period', 'N/A')}

First Year ROI: {roi_data.get('first_year_roi', 'N/A')}

💰 Financial Metrics

""" # Add metric cards metrics_to_show = [ ('monthly_savings', 'Monthly Savings'), ('annual_savings', 'Annual Savings'), ('implementation_cost', 'Implementation Cost'), ('auto_heal_rate', 'Auto-Heal Rate'), ('mttr_improvement', 'MTTR Improvement'), ] for key, label in metrics_to_show: if key in roi_data: html += f"""
{label}
{roi_data[key]}
""" html += """

📈 Detailed Breakdown

""" # Add comparison table comparisons = [ ('Manual Incident Handling', '45 minutes', '2.3 minutes', '94% faster'), ('Engineer Hours/Month', '250 hours', '37.5 hours', '85% reduction'), ('Revenue at Risk/Month', '$450,000', '$82,350', '82% protection'), ('Compliance Audit Costs', '$50,000/year', '$5,000/year', '90% savings'), ] for comp in comparisons: html += f""" """ html += f"""
MetricWithout ARFWith ARFImprovement
{comp[0]} {comp[1]} {comp[2]} {comp[3]}

ARF Ultimate Investor Demo v3.3.7 | Generated automatically

Confidential - For investor review only

Contact: enterprise@petterjuan.com | Website: https://arf.dev

""" return html @staticmethod def export_compliance_report(compliance_data: Dict[str, Any], format: str = "html") -> str: """Export compliance report in specified format""" if format == "html": return ExportEngine._compliance_to_html(compliance_data) else: # Return as JSON for other formats return json.dumps(compliance_data, indent=2) @staticmethod def _compliance_to_html(compliance_data: Dict[str, Any]) -> str: """Convert compliance data to HTML report""" html = f""" ARF {compliance_data.get('standard', 'Compliance')} Report

📋 ARF {compliance_data.get('standard', 'Compliance')} Compliance Report

Report ID: {compliance_data.get('report_id', 'N/A')} | Generated: {compliance_data.get('generated_at', 'N/A')}

Period: {compliance_data.get('period', 'N/A')}

✅ Executive Summary

{compliance_data.get('summary', 'No summary available')}

Estimated Audit Cost Savings: {compliance_data.get('estimated_audit_cost_savings', 'N/A')}

🔍 Detailed Findings

""" # Add findings findings = compliance_data.get('findings', {}) for key, value in findings.items(): status_class = "status-pass" if value in [True, "99.95%", "Complete"] else "status-fail" display_value = "✅ PASS" if value is True else "❌ FAIL" if value is False else str(value) html += f"""

{key.replace('_', ' ').title()}

{display_value}

""" html += """

This report was automatically generated by ARF Compliance Auditor

All findings are based on automated system analysis

Contact: enterprise@petterjuan.com | Compliance Hotline: +1-555-COMPLY

""" return html @staticmethod def export_chart_as_image(fig, format: str = "png") -> bytes: """Export Plotly chart as image bytes""" try: # For Plotly figures img_bytes = fig.to_image(format=format, scale=2) return img_bytes except Exception as e: logging.error(f"Failed to export chart: {e}") # Return placeholder return b"" # ============================================================================ # ENHANCED DEMO SCENARIOS # ============================================================================ ENTERPRISE_SCENARIOS = { "🚨 Black Friday Payment Crisis": { "description": "Payment processing failing during peak. $500K/minute at risk.", "component": "payment-service", "metrics": { "latency_ms": 450, "error_rate": 0.22, "cpu_util": 0.95, "memory_util": 0.88, "queue_depth": 2500, "throughput": 850, }, "business_impact": { "revenue_at_risk": 2500000, "users_impacted": 45000, "time_to_resolve": 2.3, "auto_heal_possible": True, "customer_satisfaction_impact": "Critical", "brand_reputation_risk": "High", }, "oss_action": "scale_out", "enterprise_action": "autonomous_scale", "prediction": "Database crash predicted in 8.5 minutes", "visualization_type": "radar", }, "⚡ Database Connection Pool Exhaustion": { "description": "Database connections exhausted. 12 services affected.", "component": "database", "metrics": { "latency_ms": 850, "error_rate": 0.35, "cpu_util": 0.78, "memory_util": 0.98, "connections": 980, "deadlocks": 12, }, "business_impact": { "revenue_at_risk": 1200000, "users_impacted": 12000, "time_to_resolve": 8.5, "auto_heal_possible": True, "customer_satisfaction_impact": "High", "brand_reputation_risk": "Medium", }, "oss_action": "restart_container", "enterprise_action": "approval_workflow", "prediction": "Cascading failure in 3.2 minutes", "visualization_type": "heatmap", }, "🔮 Predictive Memory Leak": { "description": "Memory leak detected. $250K at risk in 18 minutes.", "component": "cache-service", "metrics": { "latency_ms": 320, "error_rate": 0.05, "cpu_util": 0.45, "memory_util": 0.94, "cache_hit_rate": 0.12, "garbage_collection": 45, }, "business_impact": { "revenue_at_risk": 250000, "users_impacted": 65000, "time_to_resolve": 0.8, "auto_heal_possible": True, "customer_satisfaction_impact": "Medium", "brand_reputation_risk": "Low", }, "oss_action": "restart_container", "enterprise_action": "predictive_prevention", "prediction": "Outage prevented 17 minutes before crash", "visualization_type": "radar", }, "📈 API Error Rate Spike": { "description": "API errors increasing. Requires investigation.", "component": "api-service", "metrics": { "latency_ms": 120, "error_rate": 0.25, "cpu_util": 0.35, "memory_util": 0.42, "requests_per_second": 4500, "timeout_rate": 0.15, }, "business_impact": { "revenue_at_risk": 150000, "users_impacted": 8000, "time_to_resolve": 45.0, "auto_heal_possible": False, "customer_satisfaction_impact": "Low", "brand_reputation_risk": "Low", }, "oss_action": "rollback", "enterprise_action": "root_cause_analysis", "prediction": "Error rate will reach 35% in 22 minutes", "visualization_type": "stream", }, "🌐 Global CDN Outage": { "description": "CDN failing across 3 regions affecting 200K users", "component": "cdn-service", "metrics": { "latency_ms": 1200, "error_rate": 0.65, "cpu_util": 0.25, "memory_util": 0.35, "bandwidth_util": 0.98, "regional_availability": 0.33, }, "business_impact": { "revenue_at_risk": 3500000, "users_impacted": 200000, "time_to_resolve": 15.5, "auto_heal_possible": True, "customer_satisfaction_impact": "Critical", "brand_reputation_risk": "Critical", }, "oss_action": "failover_regions", "enterprise_action": "geo_load_balancing", "prediction": "Global outage spreading to 5 regions in 12 minutes", "visualization_type": "heatmap", }, "🔐 Authentication Service Failure": { "description": "OAuth service failing - users cannot login", "component": "auth-service", "metrics": { "latency_ms": 2500, "error_rate": 0.85, "cpu_util": 0.95, "memory_util": 0.99, "token_generation_rate": 5, "active_sessions": 45000, }, "business_impact": { "revenue_at_risk": 1800000, "users_impacted": 95000, "time_to_resolve": 5.2, "auto_heal_possible": True, "customer_satisfaction_impact": "Critical", "brand_reputation_risk": "High", }, "oss_action": "restart_service", "enterprise_action": "circuit_breaker_auto", "prediction": "Complete service failure in 4.8 minutes", "visualization_type": "radar", }, "📊 Analytics Pipeline Crash": { "description": "Real-time analytics pipeline crashed during reporting", "component": "analytics-service", "metrics": { "latency_ms": 5000, "error_rate": 0.95, "cpu_util": 0.15, "memory_util": 0.99, "data_lag_minutes": 45, "queue_backlog": 1200000, }, "business_impact": { "revenue_at_risk": 750000, "users_impacted": 25000, "time_to_resolve": 25.0, "auto_heal_possible": True, "customer_satisfaction_impact": "Medium", "brand_reputation_risk": "Medium", }, "oss_action": "restart_pipeline", "enterprise_action": "data_recovery_auto", "prediction": "Data loss exceeding SLA in 18 minutes", "visualization_type": "stream", }, } # ============================================================================ # MAIN DEMO UI - ENHANCED VERSION # ============================================================================ def create_enhanced_demo(): """Create enhanced ultimate investor demo UI""" # Initialize enhanced components business_calc = BusinessImpactCalculator() rag_visualizer = RAGGraphVisualizer() predictive_viz = PredictiveVisualizer() live_dashboard = LiveDashboard() viz_engine = EnhancedVisualizationEngine() export_engine = ExportEngine() session_manager = DemoSessionManager() enterprise_servers = {} # Generate session ID for this user session_id = f"session_{uuid.uuid4().hex[:16]}" session_manager.start_session(session_id) with gr.Blocks(title="🚀 ARF Ultimate Investor Demo v3.3.7") as demo: # Store session data in Gradio state session_state = gr.State({ "session_id": session_id, "current_scenario": None, "exported_files": [], "visualization_cache": {}, }) gr.Markdown(""" # 🚀 Agentic Reliability Framework - Ultimate Investor Demo v3.3.7 ### **From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability**

🎯 Live Demo Session:

Experience the full spectrum: OSS (Free) ↔ Enterprise (Paid)

🔗 Export Reports

📊 View Analytics

*Watch as ARF transforms reliability from a $2M cost center to a $10M profit engine* """) # ================================================================ # ENHANCED EXECUTIVE DASHBOARD TAB # ================================================================ with gr.TabItem("🏢 Executive Dashboard", elem_id="dashboard-tab"): gr.Markdown(""" ## 📊 Real-Time Business Impact Dashboard **Live metrics showing ARF's financial impact in enterprise deployments** """) with gr.Row(): with gr.Column(scale=2): # Enhanced metrics display with tooltips with gr.Row(): with gr.Column(scale=1): revenue_protected = gr.Markdown( "### 💰 Revenue Protected\n**$0**", elem_id="revenue-protected" ) gr.HTML("""
💡 Tooltip: Total revenue protected from potential outages
""") with gr.Column(scale=1): auto_heal_rate = gr.Markdown( "### ⚡ Auto-Heal Rate\n**0%**", elem_id="auto-heal-rate" ) gr.HTML("""
💡 Tooltip: Percentage of incidents resolved automatically
""") with gr.Row(): with gr.Column(scale=1): mttr_improvement = gr.Markdown( "### 🚀 MTTR Improvement\n**94% faster**", elem_id="mttr-improvement" ) gr.HTML("""
💡 Tooltip: Mean Time To Recovery improvement vs industry
""") with gr.Column(scale=1): engineer_hours = gr.Markdown( "### 👷 Engineer Hours Saved\n**0 hours**", elem_id="engineer-hours" ) gr.HTML("""
💡 Tooltip: Engineering time saved through automation
""") with gr.Column(scale=1): # Quick stats card gr.Markdown(""" ### 📈 Session Statistics

🆔 **Session:** """ + session_id[-8:] + """

🕐 **Duration:** 0.0 min

🔥 **Incidents Handled:** 0

📊 **Scenarios Tried:** 0

""") # Real-time streaming metrics gr.Markdown("### 📈 Real-time System Health Monitor") real_time_metrics = gr.Plot( label="", elem_id="real-time-metrics" ) # Enhanced incident feed with filtering with gr.Row(): with gr.Column(scale=3): gr.Markdown("### 🔥 Live Incident Feed") incident_feed = gr.Dataframe( headers=["Time", "Service", "Impact", "Status", "Value Protected"], value=[], interactive=False, elem_id="incident-feed" ) with gr.Column(scale=1): gr.Markdown("### 🔍 Quick Filters") filter_severity = gr.Dropdown( choices=["All", "Critical", "High", "Medium", "Low"], value="All", label="Filter by Severity" ) filter_status = gr.Dropdown( choices=["All", "Resolved", "In Progress", "Failed"], value="All", label="Filter by Status" ) # Top customers with enhanced visualization gr.Markdown("### 🏆 Top Customers Protected") with gr.Row(): with gr.Column(scale=2): customers_table = gr.Dataframe( headers=["Customer", "Industry", "Revenue Protected", "Uptime", "ROI"], value=[ ["FinTech Corp", "Financial Services", "$2.1M", "99.99%", "8.3×"], ["HealthSys Inc", "Healthcare", "$1.8M", "99.995%", "Priceless"], ["SaaSPlatform", "SaaS", "$1.5M", "99.98%", "6.8×"], ["MediaStream", "Media", "$1.2M", "99.97%", "7.1×"], ["LogisticsPro", "Logistics", "$900K", "99.96%", "6.5×"], ], interactive=False, ) with gr.Column(scale=1): # Customer ROI visualization gr.Markdown("#### 📊 ROI Distribution") roi_distribution = gr.Plot( label="Customer ROI Distribution" ) # ================================================================ # ENHANCED LIVE WAR ROOM TAB # ================================================================ with gr.TabItem("🔥 Live War Room", elem_id="war-room-tab"): gr.Markdown(""" ## 🔥 Multi-Incident War Room **Watch ARF handle 8+ simultaneous incidents across different services** """) with gr.Row(): with gr.Column(scale=1): # Enhanced scenario selector with search scenario_selector = gr.Dropdown( choices=list(ENTERPRISE_SCENARIOS.keys()), value="🚨 Black Friday Payment Crisis", label="🎬 Select Incident Scenario", info="Choose an enterprise incident scenario", filterable=True, allow_custom_value=False, ) # Scenario visualization type selector viz_type = gr.Radio( choices=["Radar Chart", "Heatmap", "3D Graph", "Stream"], value="Radar Chart", label="📊 Visualization Type", info="Choose how to visualize the metrics" ) # Enhanced metrics display metrics_display = gr.JSON( label="📊 Current Metrics", value={}, ) # Business impact with color coding impact_display = gr.JSON( label="💰 Business Impact Analysis", value={}, ) # Action buttons with loading states with gr.Row(): with gr.Column(scale=1): oss_action_btn = gr.Button( "🤖 OSS: Analyze & Recommend", variant="secondary", elem_id="oss-btn" ) oss_loading = gr.HTML("", visible=False) with gr.Column(scale=1): enterprise_action_btn = gr.Button( "🚀 Enterprise: Execute Healing", variant="primary", elem_id="enterprise-btn" ) enterprise_loading = gr.HTML("", visible=False) # License and mode with tooltips with gr.Accordion("⚙️ Enterprise Configuration", open=False): license_input = gr.Textbox( label="🔑 Enterprise License Key", value="ARF-ENT-DEMO-2024", info="Demo license - real enterprise requires purchase", placeholder="Enter your license key..." ) execution_mode = gr.Radio( choices=["autonomous", "approval"], value="autonomous", label="⚙️ Execution Mode", info="How to execute the healing action" ) gr.HTML("""
💡 Autonomous: ARF executes automatically
💡 Approval: Requires human approval before execution
""") with gr.Column(scale=2): # Enhanced results display with tabs with gr.Tabs(): with gr.TabItem("🎯 Execution Results"): result_display = gr.JSON( label="", value={}, elem_id="results-json" ) with gr.TabItem("📈 Performance Analysis"): performance_chart = gr.Plot( label="Performance Radar Chart", ) with gr.TabItem("🔥 Incident Heatmap"): incident_heatmap = gr.Plot( label="Incident Severity Heatmap", ) # Enhanced RAG Graph visualization with gr.Row(): with gr.Column(scale=2): rag_graph = gr.Plot( label="🧠 RAG Graph Memory Visualization", elem_id="rag-graph" ) with gr.Column(scale=1): # RAG Graph controls gr.Markdown("#### 🎛️ Graph Controls") graph_type = gr.Radio( choices=["2D View", "3D View", "Network View"], value="2D View", label="View Type" ) animate_graph = gr.Checkbox( label="🎬 Enable Animation", value=True ) refresh_graph = gr.Button( "🔄 Refresh Graph", size="sm" ) # Predictive Timeline predictive_timeline = gr.Plot( label="🔮 Predictive Analytics Timeline", elem_id="predictive-timeline" ) # Function to update scenario with enhanced visualization def update_scenario_enhanced(scenario_name, viz_type, session_state): scenario = ENTERPRISE_SCENARIOS.get(scenario_name, {}) session_state["current_scenario"] = scenario_name # Add to RAG graph incident_id = rag_visualizer.add_incident( component=scenario.get("component", "unknown"), severity="critical" if scenario.get("business_impact", {}).get("revenue_at_risk", 0) > 1000000 else "high" ) # Add prediction if "prediction" in scenario: predictive_viz.add_prediction( metric="latency", current_value=scenario["metrics"]["latency_ms"], predicted_value=scenario["metrics"]["latency_ms"] * 1.3, time_to_threshold=8.5 if "Black Friday" in scenario_name else None ) # Select visualization based on type if viz_type == "Radar Chart": viz_fig = viz_engine.create_animated_radar_chart( scenario.get("metrics", {}), f"Performance Radar - {scenario_name}" ) elif viz_type == "Heatmap": viz_fig = viz_engine.create_heatmap_timeline([scenario]) elif viz_type == "3D Graph": viz_fig = viz_engine.create_3d_rag_graph( rag_visualizer.incidents, rag_visualizer.outcomes, rag_visualizer.edges ) else: # Stream viz_fig = viz_engine.create_real_time_metrics_stream() # Store in cache session_state["visualization_cache"][scenario_name] = viz_fig return { metrics_display: scenario.get("metrics", {}), impact_display: business_calc.calculate_impact(scenario.get("business_impact", {})), rag_graph: rag_visualizer.get_graph_figure(), predictive_timeline: predictive_viz.get_predictive_timeline(), performance_chart: viz_fig, incident_heatmap: viz_engine.create_heatmap_timeline([scenario]), session_state: session_state, } # Connect events scenario_selector.change( fn=update_scenario_enhanced, inputs=[scenario_selector, viz_type, session_state], outputs=[metrics_display, impact_display, rag_graph, predictive_timeline, performance_chart, incident_heatmap, session_state] ) viz_type.change( fn=lambda scenario, viz_type, state: update_scenario_enhanced(scenario, viz_type, state), inputs=[scenario_selector, viz_type, session_state], outputs=[performance_chart, session_state] ) # ================================================================ # ENHANCED LEARNING ENGINE TAB # ================================================================ with gr.TabItem("🧠 Learning Engine", elem_id="learning-tab"): gr.Markdown(""" ## 🧠 RAG Graph Learning Engine **Watch ARF learn from every incident and outcome** """) with gr.Row(): with gr.Column(scale=1): # Enhanced learning stats learning_stats = gr.JSON( label="📊 Learning Statistics", value=rag_visualizer.get_stats(), ) # Learning controls with gr.Accordion("🎓 Learning Controls", open=True): simulate_learning_btn = gr.Button( "🎓 Simulate Learning Cycle", variant="primary", elem_id="simulate-learning" ) learning_rate = gr.Slider( minimum=1, maximum=10, value=3, step=1, label="Learning Cycles", info="Number of incidents to simulate" ) success_probability = gr.Slider( minimum=0.1, maximum=1.0, value=0.8, step=0.1, label="Success Probability", info="Probability of successful resolution" ) # Export section with gr.Accordion("📤 Export Knowledge", open=False): export_format = gr.Radio( choices=["JSON", "CSV", "Graph Image"], value="JSON", label="Export Format" ) export_btn = gr.Button( "📤 Export Learned Patterns", variant="secondary" ) export_status = gr.HTML( "
" "✅ Ready to export
", visible=True ) with gr.Column(scale=2): # Enhanced RAG Graph visualization with gr.Tabs(): with gr.TabItem("🔗 2D Knowledge Graph"): learning_graph_2d = gr.Plot( label="", ) with gr.TabItem("🌐 3D Knowledge Graph"): learning_graph_3d = gr.Plot( label="", ) with gr.TabItem("📊 Learning Progress"): learning_progress = gr.Plot( label="", ) # Update learning graphs def update_learning_graphs(): return { learning_graph_2d: rag_visualizer.get_graph_figure(), learning_graph_3d: viz_engine.create_3d_rag_graph( rag_visualizer.incidents, rag_visualizer.outcomes, rag_visualizer.edges ), learning_stats: rag_visualizer.get_stats(), learning_progress: viz_engine.create_real_time_metrics_stream(), } # Simulate enhanced learning def simulate_enhanced_learning(cycles, success_prob, session_state): components = ["payment-service", "database", "api-service", "cache", "auth-service", "cdn-service", "analytics-service", "queue-service"] actions = ["scale_out", "restart_container", "rollback", "circuit_breaker", "failover", "load_balance", "cache_clear", "connection_pool"] for _ in range(cycles): component = random.choice(components) incident_id = rag_visualizer.add_incident( component=component, severity=random.choice(["low", "medium", "high", "critical"]) ) rag_visualizer.add_outcome( incident_id=incident_id, success=random.random() < success_prob, action=random.choice(actions) ) # Record in session session_manager.record_action( session_state["session_id"], "simulate_learning", {"cycles": cycles, "success_probability": success_prob} ) return update_learning_graphs() # Connect events simulate_learning_btn.click( fn=simulate_enhanced_learning, inputs=[learning_rate, success_probability, session_state], outputs=[learning_graph_2d, learning_graph_3d, learning_stats, learning_progress] ) refresh_graph.click( fn=update_learning_graphs, outputs=[learning_graph_2d, learning_graph_3d, learning_stats, learning_progress] ) # ================================================================ # ENHANCED COMPLIANCE AUDITOR TAB # ================================================================ with gr.TabItem("📝 Compliance Auditor", elem_id="compliance-tab"): gr.Markdown(""" ## 📝 Automated Compliance & Audit Trails **Enterprise-only: Generate SOC2/GDPR/HIPAA compliance reports in seconds** """) with gr.Row(): with gr.Column(scale=1): # Compliance configuration compliance_standard = gr.Dropdown( choices=["SOC2", "GDPR", "HIPAA", "ISO27001", "PCI-DSS"], value="SOC2", label="📋 Compliance Standard", info="Select compliance standard" ) compliance_license = gr.Textbox( label="🔑 Enterprise License Required", value="ARF-ENT-COMPLIANCE", interactive=True, placeholder="Enter compliance license key..." ) # Export options with gr.Accordion("📤 Export Options", open=False): report_format = gr.Radio( choices=["HTML Report", "JSON", "PDF Summary"], value="HTML Report", label="Report Format" ) include_audit_trail = gr.Checkbox( label="Include Audit Trail", value=True ) generate_report_btn = gr.Button( "⚡ Generate & Export Report", variant="primary", elem_id="generate-report" ) # Audit trail viewer gr.Markdown("### 📜 Live Audit Trail") audit_trail = gr.Dataframe( label="", headers=["Time", "Action", "Component", "User", "Status", "Details"], value=[], ) with gr.Column(scale=2): # Report display with tabs with gr.Tabs(): with gr.TabItem("📄 Compliance Report"): compliance_report = gr.JSON( label="", value={}, ) with gr.TabItem("📊 Compliance Dashboard"): compliance_dashboard = gr.Plot( label="Compliance Metrics Dashboard", ) with gr.TabItem("🔍 Detailed Findings"): findings_display = gr.HTML( label="", value="
Select a standard and generate report
" ) # Report actions with gr.Row(): preview_report = gr.Button( "👁️ Preview Report", variant="secondary", size="sm" ) download_report = gr.Button( "📥 Download Report", variant="secondary", size="sm" ) share_report = gr.Button( "🔗 Share Report", variant="secondary", size="sm" ) # ================================================================ # ENHANCED ROI CALCULATOR TAB # ================================================================ with gr.TabItem("💰 ROI Calculator", elem_id="roi-tab"): gr.Markdown(""" ## 💰 Enterprise ROI Calculator **Calculate your potential savings with ARF Enterprise** """) with gr.Row(): with gr.Column(scale=1): # Inputs with tooltips gr.Markdown("### 📝 Input Your Business Metrics") monthly_revenue = gr.Number( value=1000000, label="Monthly Revenue ($)", info="Your company's monthly revenue", minimum=10000, maximum=1000000000, step=10000 ) monthly_incidents = gr.Slider( minimum=1, maximum=100, value=20, label="Monthly Incidents", info="Reliability incidents per month", step=1 ) team_size = gr.Slider( minimum=1, maximum=20, value=3, label="SRE/DevOps Team Size", info="Engineers handling incidents", step=1 ) avg_incident_cost = gr.Slider( minimum=100, maximum=10000, value=1500, label="Average Incident Cost ($)", info="Revenue loss + engineer time per incident", step=100 ) with gr.Accordion("⚙️ Advanced Settings", open=False): engineer_hourly_rate = gr.Number( value=100, label="Engineer Hourly Rate ($)", info="Average hourly rate of engineers" ) implementation_timeline = gr.Slider( minimum=1, maximum=12, value=3, label="Implementation Timeline (months)", info="Time to fully implement ARF" ) calculate_roi_btn = gr.Button( "📈 Calculate ROI", variant="primary", size="lg" ) with gr.Column(scale=2): # Enhanced results display with gr.Tabs(): with gr.TabItem("📊 ROI Results"): roi_results = gr.JSON( label="", value={}, ) with gr.TabItem("📈 Visualization"): roi_chart = gr.Plot( label="", ) with gr.TabItem("📋 Detailed Breakdown"): roi_breakdown = gr.Dataframe( label="Cost-Benefit Analysis", headers=["Category", "Without ARF", "With ARF", "Savings", "ROI Impact"], value=[], ) # Export section gr.Markdown("### 📤 Export ROI Analysis") with gr.Row(): export_roi_html = gr.Button( "🌐 Export as HTML", variant="secondary" ) export_roi_csv = gr.Button( "📊 Export as CSV", variant="secondary" ) export_roi_pdf = gr.Button( "📄 Export as PDF", variant="secondary" ) export_status = gr.HTML( "
" "📝 Ready for export
", visible=True ) # ================================================================ # ENHANCED ANALYTICS & EXPORT TAB # ================================================================ with gr.TabItem("📈 Analytics & Export", elem_id="analytics-section"): gr.Markdown(""" ## 📈 Advanced Analytics & Export Hub **Deep dive into performance metrics and export professional reports** """) with gr.Row(): with gr.Column(scale=1): # Analytics controls gr.Markdown("### 📊 Analytics Controls") analytics_timeframe = gr.Dropdown( choices=["Last Hour", "Today", "Last 7 Days", "Last 30 Days", "All Time"], value="Today", label="Timeframe" ) analytics_metric = gr.Dropdown( choices=["Revenue Protected", "Incidents Handled", "Auto-Heal Rate", "MTTR Improvement", "ROI", "Compliance Score"], value="Revenue Protected", label="Primary Metric" ) refresh_analytics = gr.Button( "🔄 Refresh Analytics", variant="primary" ) # Export all data gr.Markdown("### 📤 Bulk Export") with gr.Accordion("Export All Session Data", open=False): export_all_format = gr.Radio( choices=["JSON", "CSV", "HTML Report"], value="JSON", label="Export Format" ) export_all_btn = gr.Button( "💾 Export All Data", variant="secondary" ) with gr.Column(scale=2): # Historical trends gr.Markdown("### 📈 Historical Performance Trends") historical_trends = gr.Plot( label="", ) # Session analytics gr.Markdown("### 👤 Session Analytics") session_analytics = gr.JSON( label="", value={}, ) # Export hub gr.Markdown("### 🚀 Export Hub", elem_id="export-section") with gr.Row(): with gr.Column(scale=1): export_type = gr.Dropdown( choices=["ROI Report", "Compliance Report", "Incident Analysis", "Performance Dashboard", "Executive Summary"], value="ROI Report", label="Report Type" ) export_customize = gr.CheckboxGroup( choices=["Include Charts", "Include Raw Data", "Add Watermark", "Password Protect", "Brand Customization"], value=["Include Charts"], label="Customization Options" ) with gr.Column(scale=2): export_preview = gr.HTML( "
" "

🚀 Export Preview

" "

Select report type and customization options

" "
" ) with gr.Row(): generate_export = gr.Button( "⚡ Generate Export", variant="primary" ) preview_export = gr.Button( "👁️ Preview", variant="secondary" ) clear_exports = gr.Button( "🗑️ Clear", variant="secondary" ) # ================================================================ # MOBILE RESPONSIVE ELEMENTS # ================================================================ gr.Markdown(""" """) # ================================================================ # ENHANCED FOOTER WITH EXPORT LINKS # ================================================================ gr.Markdown(""" ---

🚀 Ready to transform your reliability operations?

Capability Comparison:

CapabilityOSS EditionEnterprise Edition
Execution❌ Advisory only✅ Autonomous + Approval
Learning❌ No learning✅ Continuous learning engine
Compliance❌ No audit trails✅ SOC2/GDPR/HIPAA compliant
Storage⚠️ In-memory only✅ Persistent (Neo4j + PostgreSQL)
Support❌ Community✅ 24/7 Enterprise support
ROI❌ None✅ 5.2× average first year ROI

📞 Contact & Resources

📧 Email: enterprise@petterjuan.com

🌐 Website: https://arf.dev

📚 Documentation: https://docs.arf.dev

💻 GitHub: petterjuan/agentic-reliability-framework

📊 Demo Session ID: """ + session_id[-8:] + """

🚀 ARF Ultimate Investor Demo v3.3.7 | Enhanced with Professional Analytics & Export Features

Built with ❤️ using Gradio & Plotly | Session started at """ + datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + """

""") return demo # ============================================================================ # MAIN ENTRY POINT # ============================================================================ def main(): """Main entry point""" logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) logger.info("=" * 80) logger.info("🚀 Starting ARF Ultimate Investor Demo v3.3.7") logger.info("=" * 80) demo = create_enhanced_demo() demo.launch( server_name="0.0.0.0", server_port=7860, share=False, show_error=True, theme="soft", favicon_path=None, ) if __name__ == "__main__": main()