""" šŸš€ ARF Ultimate Investor Demo v3.8.0 - ENTERPRISE EDITION Main entry point with comprehensive 5-tab interface Uses actual ARF OSS v3.3.6 framework """ import logging import sys import traceback import json import datetime from pathlib import Path from typing import Dict, List, Any, Optional, Tuple # Configure logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.StreamHandler(sys.stdout), logging.FileHandler('arf_demo.log') ] ) logger = logging.getLogger(__name__) # Add parent directory to path for module imports sys.path.insert(0, str(Path(__file__).parent)) try: # Import ARF OSS framework (actual package) try: from agentic_reliability_framework import __version__ as arf_version from agentic_reliability_framework.arf_core.models.healing_intent import ( HealingIntent, create_scale_out_intent, create_rollback_intent ) from agentic_reliability_framework.arf_core.engine.simple_mcp_client import OSSMCPClient from agentic_reliability_framework.engine.mcp_server import MCPServer, MCPMode ARF_OSS_AVAILABLE = True OSS_VERSION = arf_version logger.info(f"āœ… Successfully imported ARF OSS v{OSS_VERSION}") # Create OSS client instance oss_client = OSSMCPClient() except ImportError as e: logger.warning(f"Failed to import ARF OSS: {e}") ARF_OSS_AVAILABLE = False OSS_VERSION = "3.3.6 (Mock)" # Mock classes for demo class HealingIntent: def __init__(self, action: str, component: str, parameters: Dict, **kwargs): self.action = action self.component = component self.parameters = parameters self.justification = kwargs.get('justification', '') self.confidence = kwargs.get('confidence', 0.85) self.similar_incidents = kwargs.get('similar_incidents', []) self.rag_similarity_score = kwargs.get('rag_similarity_score') def to_enterprise_request(self) -> Dict: return { 'action': self.action, 'component': self.component, 'parameters': self.parameters, 'justification': self.justification, 'confidence': self.confidence, 'requires_enterprise': True, 'oss_metadata': { 'similar_incidents_count': len(self.similar_incidents), 'rag_used': self.rag_similarity_score is not None } } def mark_as_oss_advisory(self): return self class OSSMCPClient: def __init__(self): self.mode = "advisory" async def analyze_and_recommend(self, tool_name: str, component: str, parameters: Dict, context: Optional[Dict] = None) -> HealingIntent: # Simulate RAG similarity search similar_incidents = [ {"id": "inc_001", "similarity": 0.78, "resolution": "scaled_out", "component": "redis"}, {"id": "inc_045", "similarity": 0.65, "resolution": "restarted", "component": "database"}, {"id": "inc_089", "similarity": 0.59, "resolution": "circuit_breaker", "component": "api"} ] return HealingIntent( action=tool_name, component=component, parameters=parameters, justification=f"OSS Analysis: Based on {len(similar_incidents)} similar incidents, recommend {tool_name} for {component}", confidence=0.82 + (len(similar_incidents) * 0.01), similar_incidents=similar_incidents, rag_similarity_score=0.72 ) oss_client = OSSMCPClient() MCPMode = type('MCPMode', (), { 'ADVISORY': 'advisory', 'APPROVAL': 'approval', 'AUTONOMOUS': 'autonomous' }) # Import Gradio and visualization libraries import gradio as gr import plotly.graph_objects as go import plotly.express as px import pandas as pd import numpy as np from plotly.subplots import make_subplots # =========================================== # COMPREHENSIVE DATA MODELS # =========================================== class AuditTrailManager: """Manage audit trail and execution history""" def __init__(self): self.execution_history = [] self.incident_history = [] self._initialize_sample_data() def _initialize_sample_data(self): """Initialize with sample historical data""" base_time = datetime.datetime.now() - datetime.timedelta(hours=2) # Sample execution history sample_executions = [ self._create_execution_entry( base_time - datetime.timedelta(minutes=90), "Cache Miss Storm", 4, 7200, "āœ… Executed", "Auto-scaled cache" ), self._create_execution_entry( base_time - datetime.timedelta(minutes=75), "Memory Leak", 3, 5200, "āœ… Executed", "Fixed memory leak" ), self._create_execution_entry( base_time - datetime.timedelta(minutes=60), "API Rate Limit", 4, 2800, "āœ… Executed", "Increased rate limits" ), self._create_execution_entry( base_time - datetime.timedelta(minutes=45), "DB Connection Pool", 4, 3800, "āœ… Executed", "Scaled connection pool" ), ] self.execution_history = sample_executions # Sample incident history services = ["API Gateway", "Database", "Redis Cache", "Auth Service", "Payment Service"] for i in range(10): incident_time = base_time - datetime.timedelta(minutes=i * 15) self.incident_history.append({ "timestamp": incident_time, "time_str": incident_time.strftime("%H:%M"), "service": services[i % len(services)], "type": "Cache Miss Storm" if i % 3 == 0 else "Memory Leak", "severity": 3 if i % 3 == 0 else 2, "description": f"High latency on {services[i % len(services)]}", "id": f"inc_{i:03d}" }) def _create_execution_entry(self, timestamp, scenario, actions, savings, status, details): """Create an execution history entry""" return { "timestamp": timestamp, "time_str": timestamp.strftime("%H:%M"), "scenario": scenario, "actions": str(actions), "savings": f"${savings:,}", "status": status, "details": details, "id": f"exec_{len(self.execution_history):03d}" } def add_execution(self, scenario: str, actions: List[str], savings: int, approval_required: bool, details: str = ""): """Add new execution to history""" entry = self._create_execution_entry( datetime.datetime.now(), scenario, len(actions), savings, "āœ… Approved & Executed" if approval_required else "āœ… Auto-Executed", details ) self.execution_history.insert(0, entry) return entry def add_incident(self, scenario_name: str, metrics: Dict): """Add incident to history""" entry = { "timestamp": datetime.datetime.now(), "time_str": datetime.datetime.now().strftime("%H:%M"), "service": "Demo System", "type": scenario_name, "severity": 3, "description": f"Demo incident: {scenario_name}", "id": f"inc_{len(self.incident_history):03d}" } self.incident_history.insert(0, entry) return entry def get_execution_history_table(self, limit: int = 10) -> List[List]: """Get execution history for table display""" return [ [entry["time_str"], entry["scenario"], entry["actions"], entry["status"], entry["savings"], entry["details"]] for entry in self.execution_history[:limit] ] def get_incident_history_table(self, limit: int = 15) -> List[List]: """Get incident history for table display""" return [ [entry["time_str"], entry["service"], entry["type"], f"{entry['severity']}/3", entry["description"]] for entry in self.incident_history[:limit] ] def export_audit_trail(self) -> str: """Export audit trail as JSON""" total_savings = sum( int(e["savings"].replace("$", "").replace(",", "")) for e in self.execution_history if "$" in e["savings"] ) return json.dumps({ "executions": self.execution_history, "incidents": self.incident_history, "exported_at": datetime.datetime.now().isoformat(), "total_executions": len(self.execution_history), "total_incidents": len(self.incident_history), "total_savings": total_savings, "arf_version": OSS_VERSION, "oss_available": ARF_OSS_AVAILABLE }, indent=2, default=str) # =========================================== # INCIDENT SCENARIOS # =========================================== INCIDENT_SCENARIOS = { "Cache Miss Storm": { "description": "Redis cluster experiencing 80% cache miss rate causing database overload", "severity": "CRITICAL", "metrics": { "Cache Hit Rate": "18.5% (Critical)", "Database Load": "92% (Overloaded)", "Response Time": "1850ms (Slow)", "Affected Users": "45,000", "Eviction Rate": "125/sec" }, "impact": { "Revenue Loss": "$8,500/hour", "Page Load Time": "+300%", "Users Impacted": "45,000", "SLA Violation": "Yes", "Customer Sat": "-40%" }, "oss_analysis": { "status": "āœ… ARF OSS Analysis Complete", "recommendations": [ "Increase Redis cache memory allocation", "Implement cache warming strategy", "Optimize key patterns (TTL adjustments)", "Add circuit breaker for database fallback", "Deploy monitoring for cache hit rate trends" ], "estimated_time": "60+ minutes", "engineers_needed": "2-3 SREs + 1 DBA", "manual_effort": "High", "total_cost": "$8,500", "healing_intent": "scale_out_cache" }, "enterprise_results": { "actions_completed": [ "āœ… Auto-scaled Redis cluster: 4GB ā†’ 8GB", "āœ… Deployed intelligent cache warming service", "āœ… Optimized 12 key patterns with ML recommendations", "āœ… Implemented circuit breaker with 95% success rate", "āœ… Validated recovery with automated testing" ], "metrics_improvement": { "Cache Hit Rate": "18.5% ā†’ 72%", "Response Time": "1850ms ā†’ 450ms", "Database Load": "92% ā†’ 45%", "Throughput": "1250 ā†’ 2450 req/sec" }, "business_impact": { "Recovery Time": "60 min ā†’ 12 min", "Cost Saved": "$7,200", "Users Impacted": "45,000 ā†’ 0", "Revenue Protected": "$1,700", "MTTR Improvement": "80% reduction" } } }, "Database Connection Pool Exhaustion": { "description": "Database connection pool exhausted causing API timeouts and user failures", "severity": "HIGH", "metrics": { "Active Connections": "98/100 (Critical)", "API Latency": "2450ms", "Error Rate": "15.2%", "Queue Depth": "1250", "Connection Wait": "45s" }, "impact": { "Revenue Loss": "$4,200/hour", "Affected Services": "API Gateway, User Service, Payment", "SLA Violation": "Yes", "Partner Impact": "3 external APIs" } }, "Memory Leak in Production": { "description": "Java service memory leak causing gradual performance degradation", "severity": "HIGH", "metrics": { "Memory Usage": "96% (Critical)", "GC Pause Time": "4500ms", "Error Rate": "28.5%", "Restart Frequency": "12/hour", "Heap Fragmentation": "42%" }, "impact": { "Revenue Loss": "$5,500/hour", "Session Loss": "8,500 users", "Customer Impact": "High", "Support Tickets": "+300%" } } } # =========================================== # BUSINESS LOGIC # =========================================== class BusinessLogic: """Business logic for the demo""" def __init__(self, audit_manager: AuditTrailManager, oss_client): self.audit_manager = audit_manager self.oss_client = oss_client self.license_info = { "valid": True, "customer_name": "Demo Enterprise Corp", "customer_email": "demo@enterprise.com", "tier": "ENTERPRISE", "expires_at": "2024-12-31T23:59:59", "features": ["autonomous_healing", "compliance", "audit_trail", "multi_cloud"], "max_services": 100, "max_incidents_per_month": 1000, "status": "āœ… Active" } async def run_oss_analysis(self, scenario_name: str) -> Dict: """Run OSS analysis using actual ARF framework""" scenario = INCIDENT_SCENARIOS.get(scenario_name, {}) # Create HealingIntent using OSS client healing_intent = await self.oss_client.analyze_and_recommend( tool_name="scale_out", component="redis_cache", parameters={"scale_factor": 2.0, "resource_type": "memory"}, context={ "incident_type": scenario_name, "metrics": scenario.get("metrics", {}), "severity": scenario.get("severity", "HIGH") } ) # Build analysis response analysis = scenario.get("oss_analysis", {}).copy() analysis["healing_intent"] = healing_intent.to_enterprise_request() analysis["arf_oss_version"] = OSS_VERSION analysis["analysis_timestamp"] = datetime.datetime.now().isoformat() # Add to incident history self.audit_manager.add_incident(scenario_name, scenario.get("metrics", {})) return analysis def execute_enterprise_healing(self, scenario_name: str, approval_required: bool) -> Tuple[Dict, Dict]: """Execute enterprise healing""" scenario = INCIDENT_SCENARIOS.get(scenario_name, {}) results = scenario.get("enterprise_results", {}).copy() # Add enterprise context results["enterprise_context"] = { "approval_required": approval_required, "execution_mode": "autonomous" if not approval_required else "approval", "timestamp": datetime.datetime.now().isoformat(), "license_tier": self.license_info["tier"] } # Calculate savings savings = 7200 if scenario_name == "Cache Miss Storm" else 4200 # Add to audit trail self.audit_manager.add_execution( scenario_name, results.get("actions_completed", []), savings, approval_required, f"Healed {scenario_name} incident" ) # Create approval HTML approval_html = self._create_approval_html(scenario_name, approval_required) return approval_html, results def _create_approval_html(self, scenario_name: str, approval_required: bool) -> str: """Create approval workflow HTML""" if approval_required: return f"""
šŸ›”ļø

Approval Required

Action: Scale resources for {scenario_name}
Risk Level: Low
Blast Radius: Limited to affected service
Auto-rollback: Available
Status
āœ… Approved & Executed
{datetime.datetime.now().strftime("%H:%M:%S")}
""" else: return f"""
āš”

Auto-Executed

Action: Autonomous healing for {scenario_name}
Mode: Fully autonomous
Guardrails: Safety limits active
Rollback: Ready if needed
Status
āœ… Successfully completed
{datetime.datetime.now().strftime("%H:%M:%S")}
""" def calculate_roi(self, monthly_incidents: int, avg_impact: int, team_size: int) -> Dict: """Calculate ROI""" try: annual_impact = monthly_incidents * 12 * avg_impact team_cost = team_size * 150000 # $150k per engineer savings = annual_impact * 0.82 # 82% savings with ARF roi_multiplier = savings / team_cost if team_cost > 0 else 0 if roi_multiplier >= 5.0: recommendation = "šŸš€ Excellent fit for ARF Enterprise" icon = "šŸš€" color = "#28a745" elif roi_multiplier >= 2.0: recommendation = "āœ… Good ROI with ARF Enterprise" icon = "āœ…" color = "#20c997" elif roi_multiplier >= 1.0: recommendation = "āš ļø Consider ARF OSS edition first" icon = "ā„¹ļø" color = "#ffc107" else: recommendation = "šŸ†“ Start with ARF OSS (free)" icon = "šŸ†“" color = "#6c757d" payback = (team_cost / (savings / 12)) if savings > 0 else 0 return { "analysis": { "your_annual_impact": f"${annual_impact:,.0f}", "your_team_cost": f"${team_cost:,.0f}", "potential_savings": f"${savings:,.0f}", "your_roi_multiplier": f"{roi_multiplier:.1f}Ɨ", "vs_industry_average": "5.2Ɨ average ROI", "recommendation": f"{icon} {recommendation}", "recommendation_color": color, "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)}"} # =========================================== # VISUALIZATION ENGINE # =========================================== class VisualizationEngine: """Enhanced visualization engine""" @staticmethod def create_incident_timeline() -> go.Figure: """Create interactive incident timeline""" fig = go.Figure() # Create timeline events now = datetime.datetime.now() events = [ {"time": now - datetime.timedelta(minutes=25), "event": "šŸ“‰ Cache hit rate drops to 18.5%", "type": "problem"}, {"time": now - datetime.timedelta(minutes=22), "event": "āš ļø Alert: Database load hits 92%", "type": "alert"}, {"time": now - datetime.timedelta(minutes=20), "event": "šŸ¤– ARF detects pattern", "type": "detection"}, {"time": now - datetime.timedelta(minutes=18), "event": "šŸ§  Analysis: Cache Miss Storm identified", "type": "analysis"}, {"time": now - datetime.timedelta(minutes=15), "event": "āš” Healing actions executed", "type": "action"}, {"time": now - datetime.timedelta(minutes=12), "event": "āœ… Cache hit rate recovers to 72%", "type": "recovery"}, {"time": now - datetime.timedelta(minutes=10), "event": "šŸ“Š System stabilized", "type": "stable"} ] color_map = { "problem": "#FF6B6B", "alert": "#FFA726", "detection": "#42A5F5", "analysis": "#AB47BC", "action": "#66BB6A", "recovery": "#26A69A", "stable": "#2E7D32" } # Add events for event in events: fig.add_trace(go.Scatter( x=[event["time"]], y=[1], mode='markers+text', marker=dict( size=20, color=color_map[event["type"]], symbol='circle' if event["type"] in ['problem', 'alert'] else 'diamond', line=dict(width=2, color='white') ), text=[event["event"]], textposition="top center", hoverinfo="text", hovertemplate="%{text}
%{x|%H:%M:%S}", showlegend=False )) # Add connecting lines times = [event["time"] for event in events] fig.add_trace(go.Scatter( x=times, y=[1] * len(times), mode='lines', line=dict(color='rgba(100, 100, 100, 0.3)', width=2, dash='dash'), hoverinfo='none', showlegend=False )) fig.update_layout( title="Incident Timeline - Cache Miss Storm Resolution", xaxis_title="Time ā†’", yaxis=dict( showticklabels=False, range=[0.5, 1.5] ), height=450, showlegend=False, paper_bgcolor='white', plot_bgcolor='white', hovermode='closest', xaxis=dict( tickformat='%H:%M', gridcolor='rgba(200,200,200,0.2)', showgrid=True ), margin=dict(l=50, r=50, t=80, b=50) ) return fig @staticmethod def create_business_dashboard() -> go.Figure: """Create executive business dashboard""" fig = make_subplots( rows=2, cols=2, subplot_titles=('Annual Cost Impact', 'Team Capacity Shift', 'MTTR Comparison', 'ROI Analysis'), vertical_spacing=0.15, horizontal_spacing=0.15 ) # 1. Cost Impact categories = ['Without ARF', 'With ARF Enterprise', 'Net Savings'] values = [2960000, 1000000, 1960000] fig.add_trace( go.Bar( x=categories, y=values, marker_color=['#FF6B6B', '#4ECDC4', '#45B7D1'], text=[f'${v/1000000:.1f}M' for v in values], textposition='auto', name='Cost Impact' ), row=1, col=1 ) # 2. Team Capacity Shift labels = ['Firefighting', 'Innovation', 'Strategic Work'] before = [60, 20, 20] after = [10, 60, 30] fig.add_trace( go.Bar( x=labels, y=before, name='Before ARF', marker_color='#FF6B6B' ), row=1, col=2 ) fig.add_trace( go.Bar( x=labels, y=after, name='After ARF Enterprise', marker_color='#4ECDC4' ), row=1, col=2 ) # 3. MTTR Comparison mttr_categories = ['Manual', 'Traditional', 'ARF OSS', 'ARF Enterprise'] mttr_values = [120, 45, 25, 8] fig.add_trace( go.Bar( x=mttr_categories, y=mttr_values, marker_color=['#FF6B6B', '#FFE66D', '#45B7D1', '#4ECDC4'], text=[f'{v} min' for v in mttr_values], textposition='auto', name='MTTR' ), row=2, col=1 ) # 4. ROI Gauge fig.add_trace( go.Indicator( mode="gauge+number+delta", value=5.2, title={'text': "ROI Multiplier"}, delta={'reference': 1.0, 'increasing': {'color': "green"}}, gauge={ 'axis': {'range': [0, 10], 'tickwidth': 1}, 'bar': {'color': "#4ECDC4"}, 'steps': [ {'range': [0, 2], 'color': "lightgray"}, {'range': [2, 4], 'color': "gray"}, {'range': [4, 6], 'color': "lightgreen"}, {'range': [6, 10], 'color': "green"} ], 'threshold': { 'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 5.2 } } ), row=2, col=2 ) fig.update_layout( height=700, showlegend=True, paper_bgcolor='white', plot_bgcolor='white', title_text="Executive Business Dashboard", barmode='group', margin=dict(l=50, r=50, t=100, b=50) ) return fig @staticmethod def create_execution_history_chart(audit_manager: AuditTrailManager) -> go.Figure: """Create execution history visualization""" executions = audit_manager.execution_history[:10] if not executions: fig = go.Figure() fig.update_layout( title="No execution history yet", height=400, paper_bgcolor='white', plot_bgcolor='white', xaxis_showgrid=True, yaxis_showgrid=True ) return fig # Extract data scenarios = [e["scenario"] for e in executions] savings = [] for e in executions: try: savings.append(int(e["savings"].replace("$", "").replace(",", ""))) except: savings.append(0) fig = go.Figure(data=[ go.Bar( x=scenarios, y=savings, marker_color='#4ECDC4', text=[f'${s:,.0f}' for s in savings], textposition='outside', name='Cost Saved', hovertemplate="%{x}
Savings: %{text}" ) ]) fig.update_layout( title="Execution History - Cost Savings", xaxis_title="Scenario", yaxis_title="Cost Saved ($)", height=500, paper_bgcolor='white', plot_bgcolor='white', showlegend=False, xaxis_showgrid=True, yaxis_showgrid=True, margin=dict(l=50, r=50, t=80, b=100) ) return fig @staticmethod def create_memory_graph(audit_manager: AuditTrailManager) -> go.Figure: """Create incident memory graph""" incidents = audit_manager.incident_history[:15] if not incidents: # Create sample graph fig = go.Figure() # Create nodes in a circle angles = np.linspace(0, 2*np.pi, 5, endpoint=False) radius = 1 x = radius * np.cos(angles) y = radius * np.sin(angles) fig.add_trace(go.Scatter( x=x, y=y, mode='markers+text', marker=dict(size=30, color=['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4', '#FFE66D']), text=['Cache', 'DB', 'API', 'Auth', 'Payment'], textposition="top center" )) fig.update_layout( title="Incident Memory Graph (Sample)", showlegend=False, height=600, paper_bgcolor='white', plot_bgcolor='white', xaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]), yaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]), margin=dict(l=20, r=20, t=60, b=20) ) return fig # Create actual graph from incidents nodes = [] for i, incident in enumerate(incidents): nodes.append({ "x": np.cos(2 * np.pi * i / len(incidents)), "y": np.sin(2 * np.pi * i / len(incidents)), "size": 15 + incident["severity"] * 5, "color": "#FF6B6B" if incident["severity"] == 3 else "#FFA726" if incident["severity"] == 2 else "#42A5F5", "label": incident["type"][:15], "service": incident["service"] }) fig = go.Figure() # Add nodes fig.add_trace(go.Scatter( x=[node["x"] for node in nodes], y=[node["y"] for node in nodes], mode='markers+text', marker=dict( size=[node["size"] for node in nodes], color=[node["color"] for node in nodes], line=dict(width=2, color='white') ), text=[node["label"] for node in nodes], textposition="top center", hovertext=[f"Service: {node['service']}" for node in nodes], hoverinfo="text", name="Incidents" )) # Add edges (connect similar types) for i in range(len(nodes)): for j in range(i + 1, len(nodes)): if incidents[i]["type"] == incidents[j]["type"]: fig.add_trace(go.Scatter( x=[nodes[i]["x"], nodes[j]["x"], None], y=[nodes[i]["y"], nodes[j]["y"], None], mode='lines', line=dict(width=1, color='rgba(100, 100, 100, 0.2)'), hoverinfo='none', showlegend=False )) fig.update_layout( title=f"Incident Memory Graph ({len(incidents)} incidents)", showlegend=False, height=600, paper_bgcolor='white', plot_bgcolor='white', xaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]), yaxis=dict(showgrid=False, zeroline=False, showticklabels=False, range=[-1.5, 1.5]), margin=dict(l=50, r=50, t=80, b=50) ) return fig # =========================================== # CREATE DEMO INTERFACE # =========================================== def create_demo_interface(): """Create the 5-tab demo interface""" # Initialize components audit_manager = AuditTrailManager() business_logic = BusinessLogic(audit_manager, oss_client) viz_engine = VisualizationEngine() # Custom CSS custom_css = """ .gradio-container { max-width: 1800px !important; margin: auto !important; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, sans-serif !important; } h1 { background: linear-gradient(90deg, #1a365d 0%, #2d3748 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text; font-weight: 800 !important; font-size: 2.5rem !important; margin-bottom: 0.5rem !important; } .critical { color: #FF6B6B !important; font-weight: 900 !important; } .success { color: #4ECDC4 !important; font-weight: 900 !important; } .tab-nav { background: linear-gradient(90deg, #f8fafc 0%, #ffffff 100%) !important; border-radius: 10px !important; padding: 5px !important; margin-bottom: 20px !important; } .metric-card { background: white !important; border-radius: 10px !important; padding: 20px !important; box-shadow: 0 2px 8px rgba(0,0,0,0.06) !important; border-left: 4px solid #4ECDC4 !important; margin-bottom: 15px !important; } .enterprise-badge { background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important; color: white !important; padding: 8px 16px !important; border-radius: 20px !important; font-weight: 700 !important; font-size: 0.85rem !important; display: inline-block !important; margin: 5px 0 !important; } .oss-badge { background: linear-gradient(135deg, #4299e1 0%, #38b2ac 100%) !important; color: white !important; padding: 8px 16px !important; border-radius: 20px !important; font-weight: 700 !important; font-size: 0.85rem !important; display: inline-block !important; margin: 5px 0 !important; } """ with gr.Blocks( title=f"šŸš€ ARF Investor Demo v3.8.0", theme=gr.themes.Soft( primary_hue="blue", secondary_hue="teal", font=[gr.themes.GoogleFont("Inter"), "Arial", "sans-serif"] ), css=custom_css ) as demo: # ============ HEADER ============ gr.Markdown(f"""

šŸš€ Agentic Reliability Framework

Investor Demo v3.8.0 - Enterprise Edition

šŸ¢ Enterprise Features
šŸ†“ OSS v{OSS_VERSION}
šŸ“ˆ 5.2Ɨ Average ROI
āš” 85% MTTR Reduction
Experience the full journey from OSS Advisory to Enterprise Autonomous Healing. See how ARF transforms reliability operations.
""") # ============ SYSTEM STATUS ============ with gr.Row(): with gr.Column(scale=1): status_html = f"""

System Status

Operational
ARF OSS Integration
{"āœ… Connected v" + OSS_VERSION if ARF_OSS_AVAILABLE else "āš ļø Mock Mode"}
""" gr.HTML(status_html) with gr.Column(scale=2): performance_html = """

Performance Metrics

Auto-Heal Rate
81.7%
Avg Resolution
8.2 min
Cost Savings
$6.2M/yr
""" gr.HTML(performance_html) with gr.Column(scale=1): license_html = """

License Status

ENTERPRISE
Active ā€¢ Expires 2024-12-31
āœ… Valid
""" gr.HTML(license_html) # ============ 5 TABS ============ with gr.Tabs(): # TAB 1: LIVE INCIDENT DEMO with gr.TabItem("šŸ”„ Live Incident Demo"): with gr.Row(): # Left Panel with gr.Column(scale=1): gr.Markdown("### šŸŽ¬ Incident Scenario") scenario_dropdown = gr.Dropdown( choices=list(INCIDENT_SCENARIOS.keys()), value="Cache Miss Storm", label="Select critical incident:", interactive=True ) scenario_description = gr.Markdown( value=INCIDENT_SCENARIOS["Cache Miss Storm"]["description"] ) gr.Markdown("### šŸ“Š Current Crisis Metrics") metrics_display = gr.JSON( value=INCIDENT_SCENARIOS["Cache Miss Storm"]["metrics"], label="Live Metrics", show_label=True ) gr.Markdown("### šŸ’° Business Impact") impact_display = gr.JSON( value=INCIDENT_SCENARIOS["Cache Miss Storm"]["impact"], label="Impact Analysis", show_label=True ) # Right Panel with gr.Column(scale=2): gr.Markdown("### šŸ“ˆ Incident Timeline") timeline_output = gr.Plot( value=viz_engine.create_incident_timeline(), label="", show_label=False ) gr.Markdown("### āš” Take Action") with gr.Row(): oss_btn = gr.Button( "šŸ†“ Run OSS Analysis", variant="secondary", size="lg", elem_id="oss_btn" ) enterprise_btn = gr.Button( "šŸš€ Execute Enterprise Healing", variant="primary", size="lg", elem_id="enterprise_btn" ) with gr.Row(): approval_toggle = gr.Checkbox( label="šŸ” Require Manual Approval", value=True, info="Toggle to show approval workflow vs auto-execution", interactive=True ) demo_mode_btn = gr.Button( "āš” Quick Demo", variant="secondary", size="sm", elem_id="demo_btn" ) approval_display = gr.HTML( value="
Approval status will appear here after execution
" ) config_display = gr.JSON( label="āš™ļø Enterprise Configuration", value={"approval_required": True, "compliance_mode": "strict"}, show_label=True ) results_display = gr.JSON( label="šŸŽÆ Execution Results", value={"status": "Ready for execution..."}, show_label=True ) # TAB 2: BUSINESS IMPACT & ROI with gr.TabItem("šŸ’° Business Impact & ROI"): with gr.Column(): gr.Markdown("### šŸ“Š Executive Business Dashboard") dashboard_output = gr.Plot( value=viz_engine.create_business_dashboard(), label="", show_label=False ) gr.Markdown("### šŸ§® Interactive ROI Calculator") with gr.Row(): with gr.Column(scale=1): monthly_slider = gr.Slider( 1, 100, value=15, step=1, label="Monthly incidents", interactive=True ) impact_slider = gr.Slider( 1000, 50000, value=8500, step=500, label="Average incident impact ($)", interactive=True ) team_slider = gr.Slider( 1, 20, value=5, step=1, label="Reliability team size", interactive=True ) calculate_btn = gr.Button( "Calculate My ROI", variant="primary", size="lg" ) with gr.Column(scale=2): roi_output = gr.JSON( label="Your ROI Analysis", value={"analysis": "Adjust sliders and click Calculate"}, show_label=True ) with gr.Row(): with gr.Column(): gr.Markdown(""" **šŸ“ˆ ARF Enterprise ROI Metrics** - **Average ROI:** 5.2Ɨ 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(""" **šŸŽÆ 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("šŸ“œ Audit Trail & History"): with gr.Row(): # Left Column - Execution History with gr.Column(scale=1): gr.Markdown("### šŸ“‹ Execution History (Audit Trail)") with gr.Row(): refresh_btn = gr.Button("šŸ”„ Refresh", variant="secondary", size="sm") clear_btn = gr.Button("šŸ—‘ļø Clear", variant="stop", size="sm") export_btn = gr.Button("šŸ“„ Export", variant="secondary", size="sm") execution_table = gr.Dataframe( headers=["Time", "Scenario", "Actions", "Status", "Savings", "Details"], value=audit_manager.get_execution_history_table(), label="", interactive=False, wrap=True ) gr.Markdown("### šŸ“ˆ Visual History") execution_chart = gr.Plot( value=viz_engine.create_execution_history_chart(audit_manager), label="", show_label=False ) # Right Column - Incident History with gr.Column(scale=1): gr.Markdown("### šŸ“Š Incident History") incident_table = gr.Dataframe( headers=["Time", "Service", "Type", "Severity", "Description"], value=audit_manager.get_incident_history_table(), label="", interactive=False, wrap=True ) gr.Markdown("### šŸ§  Memory Graph") memory_graph = gr.Plot( value=viz_engine.create_memory_graph(audit_manager), label="", show_label=False ) gr.Markdown("### šŸ“¤ Export & Analytics") export_text = gr.Textbox( label="Full Audit Trail (JSON)", value=audit_manager.export_audit_trail(), lines=8, interactive=False ) # TAB 4: ENTERPRISE FEATURES with gr.TabItem("šŸ¢ Enterprise Features"): with gr.Row(): # Left Column with gr.Column(scale=1): gr.Markdown("### šŸ” License Management") license_display = gr.JSON( value=business_logic.license_info, label="License Information", show_label=True ) with gr.Row(): validate_btn = gr.Button("šŸ” Validate", variant="secondary") trial_btn = gr.Button("šŸ†“ Start Trial", variant="primary") upgrade_btn = gr.Button("šŸš€ Upgrade", variant="secondary") gr.Markdown("### āš” Feature Matrix") features_data = [ ["šŸ¤– Autonomous Healing", "āŒ", "āœ… Auto", "āœ… AI-Driven"], ["šŸ“Š Executive Dashboards", "Basic", "Advanced", "āœ… Comprehensive"], ["šŸ” Compliance Automation", "āŒ", "āœ…", "āœ… SOC2/GDPR"], ["šŸ“ˆ Predictive Analytics", "āŒ", "Basic", "āœ… ML-Powered"], ["šŸ”„ Auto-Remediation", "Manual", "āœ… Auto", "āœ… Continuous"], ["šŸŽÆ SLA Guarantees", "āŒ", "āŒ", "āœ… 99.9%"], ["šŸ“Š Cost Optimization", "Basic", "Advanced", "āœ… AI-Optimized"], ["šŸ”’ Role-Based Access", "āŒ", "āœ…", "āœ… Granular"], ["šŸ“ Audit Trail", "Basic", "āœ…", "āœ… Comprehensive"], ["šŸ”„ Multi-Cloud", "āŒ", "āŒ", "āœ… Native"], ] features_table = gr.Dataframe( value=features_data, headers=["Feature", "OSS", "Starter", "Enterprise"], label="", interactive=False, wrap=True ) # Right Column with gr.Column(scale=1): gr.Markdown("### šŸ“‹ Compliance Status") compliance_status = gr.JSON( value={ "SOC2": {"status": "āœ… Certified", "expires": "2025-06-30"}, "GDPR": {"status": "āœ… Compliant", "last_audit": "2024-10-15"}, "HIPAA": {"status": "šŸŸ” In Progress", "eta": "2024-12-31"}, "ISO27001": {"status": "āœ… Certified", "cert_id": "ISO-2024-001"}, "CCPA": {"status": "āœ… Compliant", "verified": True} }, label="Compliance Certifications", show_label=True ) gr.Markdown("### šŸ”— Integration Hub") integrations_data = [ ["AWS", "CloudWatch, S3, Lambda", "āœ… Connected"], ["Azure", "Monitor, Log Analytics", "āœ… Connected"], ["GCP", "Operations, BigQuery", "āœ… Connected"], ["Datadog", "Metrics, Logs, APM", "āœ… Connected"], ["New Relic", "Full-stack", "āœ… Connected"], ["PagerDuty", "Incident Response", "āœ… Connected"], ["ServiceNow", "ITSM & CMDB", "āœ… Connected"], ["Slack", "Notifications", "āœ… Connected"], ] integrations_table = gr.Dataframe( value=integrations_data, headers=["Platform", "Services", "Status"], label="", interactive=False, wrap=True ) # TAB 5: LEARNING ENGINE with gr.TabItem("šŸ§  Learning Engine"): with gr.Row(): # Left Column with gr.Column(scale=2): gr.Markdown("### šŸ§  Incident Memory Graph") memory_graph_plot = gr.Plot( value=viz_engine.create_memory_graph(audit_manager), label="", show_label=False ) with gr.Row(): graph_type = gr.Radio( choices=["Force Directed", "Hierarchical", "Timeline"], value="Force Directed", label="Graph Type", interactive=True ) show_weights = gr.Checkbox(label="Show Edge Weights", value=True, interactive=True) gr.Markdown("### šŸ” Similarity Search") search_query = gr.Textbox( label="Search for similar incidents", placeholder="Describe incident or paste metrics...", lines=2, interactive=True ) with gr.Row(): search_btn = gr.Button("šŸ” Search", variant="primary") clear_search_btn = gr.Button("Clear", variant="secondary") search_results = gr.Dataframe( headers=["Incident", "Similarity", "Resolution", "Actions"], value=[], label="", interactive=False, wrap=True ) # Right Column with gr.Column(scale=1): gr.Markdown("### šŸ“Š Learning Statistics") learning_stats = gr.JSON( value={ "total_incidents": len(audit_manager.incident_history), "resolved_automatically": len([e for e in audit_manager.execution_history if "Executed" in e["status"]]), "patterns_detected": 5, "confidence_threshold": 0.85, "memory_size": f"{len(audit_manager.incident_history) * 0.5:.1f} KB", "similar_incidents_found": 12 }, label="Learning Engine Statistics", show_label=True ) gr.Markdown("### šŸŽÆ Pattern Detection") pattern_analysis = gr.JSON( value={ "most_common": "Cache Miss Storm", "frequency": "45% of incidents", "avg_resolution_time": "8.2 minutes", "success_rate": "92%", "recommendations": [ "Implement proactive cache monitoring", "Add circuit breaker for database fallback", "Optimize cache TTL settings" ] }, label="Pattern Analysis", show_label=True ) # ============ EVENT HANDLERS ============ # Scenario dropdown change def update_scenario(scenario_name): 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() ) scenario_dropdown.change( fn=update_scenario, inputs=[scenario_dropdown], outputs=[scenario_description, metrics_display, impact_display, timeline_output] ) # OSS Analysis button async def run_oss_analysis(scenario_name): analysis = await business_logic.run_oss_analysis(scenario_name) incident_table_data = audit_manager.get_incident_history_table() memory_plot = viz_engine.create_memory_graph(audit_manager) return analysis, incident_table_data, memory_plot oss_btn.click( fn=run_oss_analysis, inputs=[scenario_dropdown], outputs=[results_display, incident_table, memory_graph] ) # Enterprise Healing button def execute_healing(scenario_name, approval_required): approval_html, results = business_logic.execute_enterprise_healing(scenario_name, approval_required) execution_table_data = audit_manager.get_execution_history_table() execution_chart_plot = viz_engine.create_execution_history_chart(audit_manager) return approval_html, results, execution_table_data, execution_chart_plot enterprise_btn.click( fn=execute_healing, inputs=[scenario_dropdown, approval_toggle], outputs=[approval_display, results_display, execution_table, execution_chart] ) # Quick Demo button async def run_quick_demo(): # Run OSS analysis analysis = await business_logic.run_oss_analysis("Cache Miss Storm") # Execute enterprise healing approval_html, results = business_logic.execute_enterprise_healing("Cache Miss Storm", False) # Update all displays execution_table_data = audit_manager.get_execution_history_table() incident_table_data = audit_manager.get_incident_history_table() execution_chart_plot = viz_engine.create_execution_history_chart(audit_manager) memory_plot = viz_engine.create_memory_graph(audit_manager) return ( analysis, approval_html, results, execution_table_data, incident_table_data, execution_chart_plot, memory_plot, gr.Checkbox.update(value=False) ) demo_mode_btn.click( fn=run_quick_demo, outputs=[ results_display, approval_display, results_display, execution_table, incident_table, execution_chart, memory_graph, approval_toggle ] ) # ROI Calculator def calculate_roi(monthly, impact, team): return business_logic.calculate_roi(monthly, impact, team) calculate_btn.click( fn=calculate_roi, inputs=[monthly_slider, impact_slider, team_slider], outputs=[roi_output] ) # Audit Trail Refresh def refresh_audit_trail(): execution_table_data = audit_manager.get_execution_history_table() incident_table_data = audit_manager.get_incident_history_table() execution_chart_plot = viz_engine.create_execution_history_chart(audit_manager) memory_plot = viz_engine.create_memory_graph(audit_manager) export_data = audit_manager.export_audit_trail() return execution_table_data, incident_table_data, execution_chart_plot, memory_plot, export_data refresh_btn.click( fn=refresh_audit_trail, outputs=[execution_table, incident_table, execution_chart, memory_graph, export_text] ) # Clear History def clear_audit_trail(): audit_manager.execution_history = [] audit_manager.incident_history = [] audit_manager._initialize_sample_data() return refresh_audit_trail() clear_btn.click( fn=clear_audit_trail, outputs=[execution_table, incident_table, execution_chart, memory_graph, export_text] ) # Export Audit Trail def update_export(): return audit_manager.export_audit_trail() export_btn.click( fn=update_export, outputs=[export_text] ) # License Management def validate_license(): business_logic.license_info["last_validated"] = datetime.datetime.now().isoformat() business_logic.license_info["validation_code"] = "VAL-2024-001" return business_logic.license_info validate_btn.click( fn=validate_license, outputs=[license_display] ) def start_trial(): business_logic.license_info["tier"] = "TRIAL" business_logic.license_info["expires_at"] = (datetime.datetime.now() + datetime.timedelta(days=30)).isoformat() business_logic.license_info["status"] = "šŸ†“ Trial Active (30 days)" return business_logic.license_info trial_btn.click( fn=start_trial, outputs=[license_display] ) def upgrade_license(): business_logic.license_info["tier"] = "PLATFORM" business_logic.license_info["status"] = "šŸš€ Upgraded to Platform Edition" return business_logic.license_info upgrade_btn.click( fn=upgrade_license, outputs=[license_display] ) # Learning Engine Search def search_similar_incidents(query): if not query.strip(): return [] # Mock search results results = [ ["Cache Miss Storm", "92%", "āœ… Resolved", "Scale cache + circuit breaker"], ["Database Connection Pool", "78%", "āœ… Resolved", "Increase pool size"], ["Memory Leak", "65%", "āš ļø Pending", "Restart + monitoring"], ["API Rate Limit", "58%", "āœ… Resolved", "Increase limits + caching"], ] return results search_btn.click( fn=search_similar_incidents, inputs=[search_query], outputs=[search_results] ) clear_search_btn.click( fn=lambda: [], outputs=[search_results] ) # Graph type change def update_graph_view(graph_type, show_weights): return viz_engine.create_memory_graph(audit_manager) graph_type.change( fn=update_graph_view, inputs=[graph_type, show_weights], outputs=[memory_graph_plot] ) show_weights.change( fn=update_graph_view, inputs=[graph_type, show_weights], outputs=[memory_graph_plot] ) # Initialize with default plots 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), audit_manager.export_audit_trail() ), outputs=[dashboard_output, timeline_output, execution_chart, memory_graph, export_text] ) # ============ FOOTER ============ gr.Markdown("""

šŸš€ Experience the Journey

  • 1. Start with OSS - Get recommendations
  • 2. Calculate ROI - See your savings
  • 3. Execute Healing - Experience autonomy
  • 4. View Audit Trail - Track everything
  • 5. Explore Features - See enterprise power

šŸ“ž Get Started

  • šŸ“§ Contact: sales@arfinvestor.com
  • šŸ“š Docs: docs.arfinvestor.com
  • šŸ’¬ Slack: Join 2,500+ engineers
  • šŸ†“ Trial: 30-day enterprise trial
  • šŸš€ Demo: Live demo available

šŸ›”ļø Trust & Security

  • āœ… SOC 2 Type II Certified
  • āœ… GDPR & CCPA Compliant
  • āœ… ISO 27001 Certified
  • āœ… HIPAA Ready
  • āœ… Enterprise-grade Security

Ā© 2024 Agentic Reliability Framework. Demo v3.8.0 Enterprise Edition.

This demonstration uses ARF OSS v{OSS_VERSION}. Actual enterprise features require license activation.

""".format(OSS_VERSION=OSS_VERSION)) return demo return create_demo_interface() except Exception as e: logger.error(f"Failed to create demo: {e}") logger.error(traceback.format_exc()) # Minimal fallback import gradio as gr with gr.Blocks(title="šŸš€ ARF Demo - Error") as demo: gr.Markdown(f""" # āš ļø ARF Demo Initialization Error Failed to initialize the demo: ```python {str(e)} ``` Please check the logs for details. """) return demo def main(): """Main entry point""" print("šŸš€ Starting ARF Ultimate Investor Demo v3.8.0...") print("=" * 70) print("šŸ“Š Features:") print(" ā€¢ 5 Comprehensive Tabs with User-Focused Journey") print(" ā€¢ Live Incident Demo with OSS Analysis") print(" ā€¢ Business Impact & ROI Calculator") print(" ā€¢ Audit Trail & History with Memory Graph") print(" ā€¢ Enterprise Features & License Management") print(" ā€¢ Learning Engine with Pattern Detection") print("=" * 70) print(f"\nšŸ“¦ Using ARF OSS v{OSS_VERSION if 'OSS_VERSION' in locals() else '3.3.6'}") print("šŸŒ Opening web interface at http://localhost:7860...") demo = create_demo_interface() demo.launch( server_name="0.0.0.0", server_port=7860, share=False, debug=False, show_error=True ) if __name__ == "__main__": main()