diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -34,1525 +34,1103 @@ try: ) from agentic_reliability_framework.arf_core.engine.simple_mcp_client import OSSMCPClient OSS_AVAILABLE = True -except ImportError: +except ImportError as e: + logging.warning(f"OSS components not available: {e}") OSS_AVAILABLE = False - logger = logging.getLogger(__name__) - logger.warning("OSS package not available") -# ============================================================================ -# BUSINESS IMPACT CALCULATIONS (Based on business.py) -# ============================================================================ +# Enhanced logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) -class BusinessImpactCalculator: - """Enterprise-scale business impact calculation""" +# =========================================== +# ENHANCED VISUALIZATION ENGINE v3.4.0 +# =========================================== + +class VisualizationEngine: + """Enhanced visualization engine with all visualizations working""" def __init__(self): - # Enterprise-scale constants - self.BASE_REVENUE_PER_MINUTE = 5000.0 # $5K/min for enterprise - self.BASE_USERS = 10000 # 10K active users - - def calculate_impact(self, scenario: Dict[str, Any]) -> Dict[str, Any]: - """Calculate business impact for demo scenarios""" - revenue_at_risk = scenario.get("revenue_at_risk", 0) - users_impacted = scenario.get("users_impacted", 0) + self.performance_data = deque(maxlen=100) + self.incident_history = [] + self.color_palette = px.colors.qualitative.Set3 - if revenue_at_risk > 1000000: - severity = "🚨 CRITICAL" - impact_color = "#ff4444" - elif revenue_at_risk > 500000: - severity = "⚠️ HIGH" - impact_color = "#ffaa00" - elif revenue_at_risk > 100000: - severity = "📈 MEDIUM" - impact_color = "#ffdd00" - else: - severity = "✅ LOW" - impact_color = "#44ff44" + def create_performance_radar(self, metrics: Dict[str, float]) -> go.Figure: + """Create performance radar chart""" + categories = list(metrics.keys()) + values = list(metrics.values()) - return { - "revenue_at_risk": f"${revenue_at_risk:,.0f}", - "users_impacted": f"{users_impacted:,}", - "severity": severity, - "impact_color": impact_color, - "time_to_resolution": f"{scenario.get('time_to_resolve', 2.3):.1f} min", - "auto_heal_possible": scenario.get("auto_heal_possible", True), - } - -# ============================================================================ -# RAG GRAPH VISUALIZATION (Based on v3_reliability.py) -# ============================================================================ - -class RAGGraphVisualizer: - """Visualize RAG graph memory growth""" - - def __init__(self): - self.incidents = [] - self.outcomes = [] - self.edges = [] + fig = go.Figure(data=go.Scatterpolar( + r=values + [values[0]], + theta=categories + [categories[0]], + fill='toself', + fillcolor='rgba(34, 163, 192, 0.3)', + line=dict(color='rgba(34, 163, 192, 0.8)'), + name="Performance" + )) - def add_incident(self, component: str, severity: str): - """Add an incident to the graph""" - incident_id = f"inc_{len(self.incidents)}" - self.incidents.append({ - "id": incident_id, - "component": component, - "severity": severity, - "timestamp": time.time(), - }) - return incident_id - - def add_outcome(self, incident_id: str, success: bool, action: str): - """Add an outcome to the graph""" - outcome_id = f"out_{len(self.outcomes)}" - self.outcomes.append({ - "id": outcome_id, - "incident_id": incident_id, - "success": success, - "action": action, - "timestamp": time.time(), - }) + fig.update_layout( + polar=dict( + radialaxis=dict( + visible=True, + range=[0, 100], + gridcolor='rgba(200, 200, 200, 0.3)' + )), + showlegend=True, + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', + height=400 + ) - # Add edge - self.edges.append({ - "source": incident_id, - "target": outcome_id, - "type": "resolved" if success else "failed", - }) - return outcome_id + return fig - def get_graph_figure(self): - """Create Plotly figure of RAG graph""" - if not self.incidents: - # Return empty figure with message + def create_heatmap_timeline(self, incidents: List[Dict]) -> go.Figure: + """Create incident severity heatmap timeline - FIXED VERSION""" + if not incidents: + # Create empty figure with proper message fig = go.Figure() fig.update_layout( - title="🧠 RAG Graph Memory - Learning from Incidents", + title="No Incident Data Available", + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', + height=300, xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), - plot_bgcolor="white", - height=500, - annotations=[dict( - text="No incidents recorded yet. Try a scenario!", - xref="paper", yref="paper", - x=0.5, y=0.5, showarrow=False, - font=dict(size=16, color="gray") - )] + annotations=[ + dict( + text="No incidents to display", + xref="paper", yref="paper", + x=0.5, y=0.5, + showarrow=False, + font=dict(size=14, color="gray") + ) + ] ) return fig - # Prepare node data - nodes = [] - node_colors = [] - node_sizes = [] - - # Add incident nodes - for inc in self.incidents: - nodes.append({ - "x": random.random(), - "y": random.random(), - "label": f"{inc['component']}\n{inc['severity']}", - "id": inc["id"], - "type": "incident", - }) - node_colors.append("#ff6b6b" if inc["severity"] == "critical" else "#ffa726") - node_sizes.append(30) + # Prepare data for heatmap + hours = list(range(24)) + services = sorted(list(set(inc['service'] for inc in incidents if 'service' in inc))) - # Add outcome nodes - for out in self.outcomes: - nodes.append({ - "x": random.random() + 0.5, # Shift right - "y": random.random(), - "label": f"{out['action']}\n{'✅' if out['success'] else '❌'}", - "id": out["id"], - "type": "outcome", - }) - node_colors.append("#4caf50" if out["success"] else "#f44336") - node_sizes.append(20) + if not services: + services = ["Service A", "Service B", "Service C", "Service D", "Service E"] - # Create figure - fig = go.Figure() + # Create severity matrix + severity_matrix = np.zeros((len(services), len(hours))) - # Add edges - for edge in self.edges: - source = next((n for n in nodes if n["id"] == edge["source"]), None) - target = next((n for n in nodes if n["id"] == edge["target"]), None) - - if source and target: - fig.add_trace(go.Scatter( - x=[source["x"], target["x"]], - y=[source["y"], target["y"]], - mode="lines", - line=dict( - color="#888888", - width=2, - dash="dash" if edge["type"] == "failed" else "solid" - ), - hoverinfo="none", - showlegend=False, - )) + for inc in incidents: + if 'service' in inc and 'hour' in inc: + try: + service_idx = services.index(inc['service']) + hour_idx = int(inc['hour']) % 24 + severity = inc.get('severity', 1) + severity_matrix[service_idx, hour_idx] = max( + severity_matrix[service_idx, hour_idx], severity + ) + except (ValueError, IndexError): + continue - # Add nodes - fig.add_trace(go.Scatter( - x=[n["x"] for n in nodes], - y=[n["y"] for n in nodes], - mode="markers+text", - marker=dict( - size=node_sizes, - color=node_colors, - line=dict(color="white", width=2) + # Create heatmap with corrected colorbar configuration + fig = go.Figure(data=go.Heatmap( + z=severity_matrix, + x=hours, + y=services, + colorscale='RdYlGn_r', # Red for high severity, green for low + showscale=True, + hoverongaps=False, + colorbar=dict( + title=dict( + text="Severity Level", + side="right" + ), + titleside="right", # This is deprecated but kept for compatibility + tickvals=[0, 1, 2, 3], + ticktext=["None", "Low", "Medium", "High"], + len=0.8, + thickness=15 ), - text=[n["label"] for n in nodes], - textposition="top center", - hovertext=[f"Type: {n['type']}" for n in nodes], - hoverinfo="text", - showlegend=False, + hovertemplate=( + "Service: %{y}
" + "Hour: %{x}:00
" + "Severity: %{z}
" + "" + ) )) - # Update layout fig.update_layout( - title="🧠 RAG Graph Memory - Learning from Incidents", - showlegend=False, - xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), - yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), - plot_bgcolor="white", - height=500, + title="Incident Severity Heatmap (24h)", + xaxis_title="Hour of Day", + yaxis_title="Service", + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', + height=400, + xaxis=dict( + tickmode='array', + tickvals=list(range(0, 24, 3)), + ticktext=[f"{h:02d}:00" for h in range(0, 24, 3)] + ), + yaxis=dict( + autorange="reversed" # Reverse so Service A is at top + ) ) return fig - def get_stats(self): - """Get graph statistics""" - successful_outcomes = sum(1 for o in self.outcomes if o["success"]) + def create_stream_graph(self, metrics_history: List[Dict]) -> go.Figure: + """Create streaming metrics visualization""" + if not metrics_history: + return self._create_empty_figure("No metrics history available") - return { - "incident_nodes": len(self.incidents), - "outcome_nodes": len(self.outcomes), - "edges": len(self.edges), - "success_rate": f"{(successful_outcomes / len(self.outcomes) * 100):.1f}%" if self.outcomes else "0%", - "patterns_learned": len(self.outcomes) // 3, # Rough estimate - } - -# ============================================================================ -# PREDICTIVE ANALYTICS (Based on predictive.py) -# ============================================================================ - -class PredictiveVisualizer: - """Visualize predictive analytics""" - - def __init__(self): - self.predictions = [] + df = pd.DataFrame(metrics_history[-50:]) # Show last 50 data points - def add_prediction(self, metric: str, current_value: float, predicted_value: float, - time_to_threshold: Optional[float] = None): - """Add a prediction""" - self.predictions.append({ - "metric": metric, - "current": current_value, - "predicted": predicted_value, - "time_to_threshold": time_to_threshold, - "timestamp": time.time(), - "predicted_at": datetime.datetime.now().strftime("%H:%M:%S"), - }) - - def get_predictive_timeline(self): - """Create predictive timeline visualization""" - if not self.predictions: - # Return empty figure with message - fig = go.Figure() - fig.update_layout( - title="🔮 Predictive Analytics Timeline", - xaxis_title="Time", - yaxis_title="Metric Value", - height=400, - plot_bgcolor="white", - annotations=[dict( - text="No predictions yet. Try a scenario!", - xref="paper", yref="paper", - x=0.5, y=0.5, showarrow=False, - font=dict(size=14, color="gray") - )] - ) - return fig + fig = go.Figure() - # Create timeline data - ensure we have valid data - valid_predictions = [] - for p in self.predictions[-10:]: # Last 10 predictions - if isinstance(p.get("current"), (int, float)) and isinstance(p.get("predicted"), (int, float)): - valid_predictions.append(p) + # Add each metric as a separate trace + colors = px.colors.qualitative.Set3 + for idx, column in enumerate(df.columns): + if column != 'timestamp': + fig.add_trace(go.Scatter( + x=df['timestamp'], + y=df[column], + mode='lines+markers', + name=column, + line=dict(color=colors[idx % len(colors)], width=2), + marker=dict(size=4) + )) - if not valid_predictions: - # Return empty figure - fig = go.Figure() - fig.update_layout( - title="🔮 Predictive Analytics Timeline", - height=400, - annotations=[dict( - text="Waiting for prediction data...", - xref="paper", yref="paper", - x=0.5, y=0.5, showarrow=False - )] + fig.update_layout( + title="Real-time Metrics Stream", + xaxis_title="Time", + yaxis_title="Value", + hovermode='x unified', + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', + height=400, + legend=dict( + yanchor="top", + y=0.99, + xanchor="left", + x=0.01 ) - return fig + ) - df = pd.DataFrame(valid_predictions) + return fig + + def create_predictive_timeline(self, incidents: List[Dict]) -> go.Figure: + """Create predictive analytics timeline""" + if not incidents: + return self._create_empty_figure("No incident data for prediction") + + # Prepare timeline data + timeline_data = [] + for inc in incidents: + timeline_data.append({ + 'timestamp': inc.get('timestamp', datetime.datetime.now()), + 'severity': inc.get('severity', 1), + 'service': inc.get('service', 'Unknown'), + 'type': 'Actual' + }) + + # Add predicted incidents + now = datetime.datetime.now() + for i in range(1, 6): + timeline_data.append({ + 'timestamp': now + datetime.timedelta(hours=i), + 'severity': random.randint(1, 3), + 'service': random.choice(['API Gateway', 'Database', 'Cache', 'Auth Service']), + 'type': 'Predicted' + }) + + df = pd.DataFrame(timeline_data) + df['timestamp'] = pd.to_datetime(df['timestamp']) + df = df.sort_values('timestamp') fig = go.Figure() - # Add current values + # Add actual incidents + actual_df = df[df['type'] == 'Actual'] fig.add_trace(go.Scatter( - x=df["predicted_at"], - y=df["current"], - mode="lines+markers", - name="Current", - line=dict(color="#4caf50", width=3), - marker=dict(size=10), + x=actual_df['timestamp'], + y=actual_df['severity'], + mode='markers', + name='Actual', + marker=dict( + color='red', + size=15, + symbol='circle', + line=dict(width=2, color='darkred') + ), + text=actual_df['service'], + hovertemplate="%{text}
Time: %{x}
Severity: %{y}" + )) + + # Add predicted incidents + pred_df = df[df['type'] == 'Predicted'] + fig.add_trace(go.Scatter( + x=pred_df['timestamp'], + y=pred_df['severity'], + mode='markers', + name='Predicted', + marker=dict( + color='orange', + size=15, + symbol='diamond', + line=dict(width=2, color='darkorange') + ), + text=pred_df['service'], + hovertemplate="%{text}
Time: %{x}
Severity: %{y}" )) - # Add predicted values + # Add trend line fig.add_trace(go.Scatter( - x=df["predicted_at"], - y=df["predicted"], - mode="lines+markers", - name="Predicted", - line=dict(color="#ff9800", width=2, dash="dash"), - marker=dict(size=8), + x=df['timestamp'], + y=np.convolve(df['severity'], np.ones(3)/3, mode='same'), + mode='lines', + name='Trend', + line=dict(color='blue', width=2, dash='dash'), + opacity=0.6 )) - # Update layout fig.update_layout( - title="🔮 Predictive Analytics Timeline", + title="Predictive Analytics Timeline", xaxis_title="Time", - yaxis_title="Metric Value", - hovermode="x unified", - plot_bgcolor="white", + yaxis_title="Incident Severity", + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', height=400, + hovermode='closest' ) return fig - -# ============================================================================ -# ENTERPRISE MOCK SERVER (Based on enterprise code structure) -# ============================================================================ - -class MockEnterpriseServer: - """Mock enterprise server showing full capabilities""" - def __init__(self, license_key: str): - self.license_key = license_key - self.license_tier = self._get_license_tier(license_key) - self.audit_trail = [] - self.learning_engine_active = True - self.execution_stats = { - "total_executions": 0, - "successful_executions": 0, - "autonomous_executions": 0, - "approval_workflows": 0, - "revenue_protected": 0.0, - } + def create_rag_memory_viz(self, memory_graph: Dict) -> go.Figure: + """Create RAG graph memory visualization""" + if not memory_graph.get('nodes'): + return self._create_empty_figure("No memory data available") - def _get_license_tier(self, license_key: str) -> str: - """Determine license tier from key""" - if "ENTERPRISE" in license_key: - return "Enterprise" - elif "PROFESSIONAL" in license_key: - return "Professional" - elif "TRIAL" in license_key: - return "Trial" - return "Starter" - - async def execute_healing(self, healing_intent: Dict[str, Any], mode: str = "autonomous") -> Dict[str, Any]: - """Mock enterprise execution""" - execution_id = f"exec_{uuid.uuid4().hex[:16]}" - start_time = time.time() + # Create network graph + nodes = memory_graph['nodes'] + edges = memory_graph.get('edges', []) - # Simulate execution time - await asyncio.sleep(random.uniform(0.5, 2.0)) + node_x = [] + node_y = [] + node_text = [] + node_size = [] + node_color = [] - # Determine success based on confidence - confidence = healing_intent.get("confidence", 0.85) - success = random.random() < confidence + # Position nodes in a circular layout + n_nodes = len(nodes) + for i, node in enumerate(nodes): + angle = 2 * np.pi * i / n_nodes + radius = 1.0 + node_x.append(radius * np.cos(angle)) + node_y.append(radius * np.sin(angle)) + node_text.append(f"{node['type']}: {node['id'][:8]}") + node_size.append(15 + (node.get('importance', 1) * 10)) + node_color.append(node.get('color_idx', i % 12)) - # Calculate simulated impact - revenue_protected = random.randint(50000, 500000) + # Create edge traces + edge_x = [] + edge_y = [] - # Update stats - self.execution_stats["total_executions"] += 1 - if success: - self.execution_stats["successful_executions"] += 1 - self.execution_stats["revenue_protected"] += revenue_protected + for edge in edges: + if edge['source'] < n_nodes and edge['target'] < n_nodes: + edge_x.extend([node_x[edge['source']], node_x[edge['target']], None]) + edge_y.extend([node_y[edge['source']], node_y[edge['target']], None]) - if mode == "autonomous": - self.execution_stats["autonomous_executions"] += 1 - elif mode == "approval": - self.execution_stats["approval_workflows"] += 1 + fig = go.Figure() - # Record audit - audit_entry = { - "audit_id": f"audit_{uuid.uuid4().hex[:8]}", - "timestamp": datetime.datetime.now().isoformat(), - "action": healing_intent["action"], - "component": healing_intent["component"], - "mode": mode, - "success": success, - "revenue_protected": revenue_protected, - "execution_time": time.time() - start_time, - "license_tier": self.license_tier, - } - self.audit_trail.append(audit_entry) + # Add edges + if edge_x: + fig.add_trace(go.Scatter( + x=edge_x, y=edge_y, + mode='lines', + line=dict(color='rgba(100, 100, 100, 0.3)', width=1), + hoverinfo='none', + showlegend=False + )) - return { - "execution_id": execution_id, - "success": success, - "message": f"✅ Successfully executed {healing_intent['action']} on {healing_intent['component']}" if success - else f"⚠️ Execution partially failed for {healing_intent['action']}", - "revenue_protected": revenue_protected, - "execution_time": time.time() - start_time, - "mode": mode, - "license_tier": self.license_tier, - "audit_id": audit_entry["audit_id"], - "learning_recorded": self.learning_engine_active and success, - } + # Add nodes + fig.add_trace(go.Scatter( + x=node_x, y=node_y, + mode='markers+text', + marker=dict( + size=node_size, + color=node_color, + colorscale='Viridis', + line=dict(color='white', width=2) + ), + text=node_text, + textposition="top center", + hoverinfo='text', + name='Memory Nodes' + )) + + fig.update_layout( + title="RAG Graph Memory Visualization", + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', + height=400, + showlegend=False, + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + margin=dict(l=20, r=20, t=40, b=20) + ) + + return fig - def generate_compliance_report(self, standard: str = "SOC2") -> Dict[str, Any]: - """Generate mock compliance report""" - return { - "report_id": f"compliance_{uuid.uuid4().hex[:8]}", - "standard": standard, - "generated_at": datetime.datetime.now().isoformat(), - "period": "last_30_days", - "findings": { - "audit_trail_complete": True, - "access_controls_enforced": True, - "data_encrypted": True, - "incident_response_documented": True, - "sla_compliance": "99.95%", + def _create_empty_figure(self, message: str) -> go.Figure: + """Create an empty figure with a message""" + fig = go.Figure() + fig.update_layout( + paper_bgcolor='rgba(0,0,0,0)', + plot_bgcolor='rgba(0,0,0,0)', + height=300, + xaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + yaxis=dict(showgrid=False, zeroline=False, showticklabels=False), + annotations=[ + dict( + text=message, + xref="paper", yref="paper", + x=0.5, y=0.5, + showarrow=False, + font=dict(size=14, color="gray") + ) + ] + ) + return fig + +# =========================================== +# INCIDENT SCENARIOS DATABASE +# =========================================== + +class IncidentScenarios: + """Enhanced incident scenarios with business impact""" + + SCENARIOS = { + "database_connection_pool_exhaustion": { + "name": "Database Connection Pool Exhaustion", + "description": "Database connection pool exhausted due to connection leaks, causing API timeouts and user failures.", + "severity": "HIGH", + "services_affected": ["API Gateway", "User Service", "Payment Service"], + "current_metrics": { + "Database Connections": 98, + "API Latency (p95)": 2450, + "Error Rate": 15.2, + "Throughput": 1250, + "CPU Utilization": 85 }, - "summary": f"✅ {standard} compliance requirements fully met", - "estimated_audit_cost_savings": "$150,000", + "business_impact": { + "affected_users": "15,000", + "revenue_loss_per_hour": "$4,200", + "customer_satisfaction": "-25%", + "recovery_time": "45 minutes", + "total_impact": "$3,150" + }, + "oss_recommendation": "Increase connection pool size from 100 to 200, implement connection timeout of 30s, and add connection leak detection.", + "enterprise_actions": [ + "Auto-scale database connection pool from 100 to 200", + "Implement connection timeout (30s)", + "Deploy connection leak detection", + "Rollback if no improvement in 5 minutes" + ], + "execution_results": { + "connection_pool_increased": True, + "timeout_implemented": True, + "leak_detection_deployed": True, + "recovery_time": "8 minutes", + "cost_saved": "$2,800" + } + }, + "api_rate_limit_exceeded": { + "name": "API Rate Limit Exceeded", + "description": "Global API rate limit exceeded causing 429 errors for all external clients.", + "severity": "MEDIUM", + "services_affected": ["API Gateway", "External API"], + "current_metrics": { + "429 Error Rate": 42.5, + "Successful Requests": 58.3, + "API Latency": 120, + "Queue Depth": 1250, + "Client Satisfaction": 65 + }, + "business_impact": { + "affected_partners": "8", + "revenue_loss_per_hour": "$1,800", + "partner_sla_violations": "3", + "recovery_time": "30 minutes", + "total_impact": "$900" + }, + "oss_recommendation": "Increase global rate limit by 50%, implement per-client quotas, and add automatic throttling.", + "enterprise_actions": [ + "Increase global rate limit from 10k to 15k RPM", + "Implement per-client quotas", + "Deploy intelligent throttling", + "Notify affected partners" + ] + }, + "cache_miss_storm": { + "name": "Cache Miss Storm", + "description": "Redis cluster experiencing 80% cache miss rate due to key eviction and invalid patterns.", + "severity": "HIGH", + "services_affected": ["Product Catalog", "Recommendation Engine", "Search Service"], + "current_metrics": { + "Cache Hit Rate": 18.5, + "Database Load": 92, + "Response Time": 1850, + "Cache Memory Usage": 95, + "Eviction Rate": 125 + }, + "business_impact": { + "affected_users": "45,000", + "revenue_loss_per_hour": "$8,500", + "page_load_time": "+300%", + "recovery_time": "60 minutes", + "total_impact": "$8,500" + }, + "oss_recommendation": "Increase cache memory, implement cache warming, optimize key patterns, and add circuit breaker.", + "enterprise_actions": [ + "Scale Redis cluster memory by 2x", + "Deploy cache warming service", + "Optimize key patterns", + "Implement circuit breaker" + ] + }, + "microservice_cascading_failure": { + "name": "Microservice Cascading Failure", + "description": "Order service failure causing cascading failures in payment, inventory, and notification services.", + "severity": "CRITICAL", + "services_affected": ["Order Service", "Payment Service", "Inventory Service", "Notification Service"], + "current_metrics": { + "Order Failure Rate": 68.2, + "Circuit Breakers Open": 4, + "Retry Storm Intensity": 425, + "Error Propagation": 85, + "System Stability": 15 + }, + "business_impact": { + "affected_users": "75,000", + "revenue_loss_per_hour": "$25,000", + "abandoned_carts": "12,500", + "recovery_time": "90 minutes", + "total_impact": "$37,500" + }, + "oss_recommendation": "Implement bulkheads, circuit breakers, retry with exponential backoff, and graceful degradation.", + "enterprise_actions": [ + "Isolate order service with bulkheads", + "Implement circuit breakers", + "Deploy retry with exponential backoff", + "Enable graceful degradation mode" + ] + }, + "memory_leak_in_production": { + "name": "Memory Leak in Production", + "description": "Java service memory leak causing gradual performance degradation and eventual OOM crashes.", + "severity": "HIGH", + "services_affected": ["User Profile Service", "Session Service"], + "current_metrics": { + "Memory Usage": 96, + "GC Pause Time": 4500, + "Request Latency": 3200, + "Error Rate": 28.5, + "Restart Frequency": 12 + }, + "business_impact": { + "affected_users": "25,000", + "revenue_loss_per_hour": "$5,500", + "session_loss": "8,500", + "recovery_time": "75 minutes", + "total_impact": "$6,875" + }, + "oss_recommendation": "Increase heap size, implement memory leak detection, add health checks, and schedule rolling restart.", + "enterprise_actions": [ + "Increase JVM heap from 4GB to 8GB", + "Deploy memory leak detection", + "Implement proactive health checks", + "Execute rolling restart" + ] } + } + + @classmethod + def get_scenario(cls, scenario_id: str) -> Dict[str, Any]: + """Get scenario by ID""" + return cls.SCENARIOS.get(scenario_id, { + "name": "Unknown Scenario", + "description": "No scenario selected", + "severity": "UNKNOWN", + "services_affected": [], + "current_metrics": {}, + "business_impact": {}, + "oss_recommendation": "Please select a scenario", + "enterprise_actions": [] + }) + + @classmethod + def get_all_scenarios(cls) -> List[Dict[str, str]]: + """Get all available scenarios""" + return [ + {"id": key, "name": value["name"], "severity": value["severity"]} + for key, value in cls.SCENARIOS.items() + ] -# ============================================================================ -# LIVE DASHBOARD -# ============================================================================ +# =========================================== +# OSS & ENTERPRISE MODELS +# =========================================== -class LiveDashboard: - """Live executive dashboard""" +class OSSModel: + """OSS Edition Model (Advisory Only)""" def __init__(self): - self.total_revenue_protected = 0.0 - self.total_incidents = 0 - self.auto_healed = 0 - self.engineer_hours_saved = 0 - self.start_time = time.time() - - def add_execution_result(self, revenue_protected: float, auto_healed: bool = True): - """Add execution result to dashboard""" - self.total_revenue_protected += revenue_protected - self.total_incidents += 1 - if auto_healed: - self.auto_healed += 1 - self.engineer_hours_saved += 2.5 # 2.5 hours saved per auto-healed incident + self.healing_intent = HealingIntent() if OSS_AVAILABLE else None - def get_dashboard_data(self): - """Get current dashboard data""" - uptime_hours = (time.time() - self.start_time) / 3600 - - return { - "revenue_protected": f"${self.total_revenue_protected:,.0f}", - "total_incidents": self.total_incidents, - "auto_healed": self.auto_healed, - "auto_heal_rate": f"{(self.auto_healed / self.total_incidents * 100):.1f}%" if self.total_incidents > 0 else "0%", - "engineer_hours_saved": f"{self.engineer_hours_saved:.0f} hours", - "avg_mttr": "2.3 minutes", - "industry_mttr": "45 minutes", - "improvement": "94% faster", - "uptime": f"{uptime_hours:.1f} hours", - "roi": "5.2×", - } - -# ============================================================================ -# ENHANCED VISUALIZATION ENGINE - FINAL FIXED VERSION -# ============================================================================ + def analyze_and_recommend(self, scenario: Dict) -> Dict[str, Any]: + """Analyze incident and provide recommendations""" + try: + if self.healing_intent: + intent = self.healing_intent.create_intent( + issue_type=scenario.get("name", "Unknown"), + symptoms=scenario.get("description", ""), + urgency="HIGH" if scenario.get("severity") in ["HIGH", "CRITICAL"] else "MEDIUM" + ) + return { + "analysis": "✅ Analysis complete", + "recommendations": scenario.get("oss_recommendation", "No specific recommendations"), + "healing_intent": intent, + "estimated_impact": "30-60 minute resolution with manual intervention" + } + else: + return { + "analysis": "⚠️ OSS Model Simulated", + "recommendations": scenario.get("oss_recommendation", "No specific recommendations"), + "healing_intent": "create_scale_out_intent" if "connection" in scenario.get("name", "").lower() else "create_restart_intent", + "estimated_impact": "Simulated: 45 minute resolution" + } + except Exception as e: + logger.error(f"OSS analysis failed: {e}") + return { + "analysis": "❌ Analysis failed", + "recommendations": "Please check system configuration", + "healing_intent": "create_rollback_intent", + "estimated_impact": "Unknown" + } -class EnhancedVisualizationEngine: - """Enhanced visualization engine - FINAL FIXED VERSION""" +class EnterpriseModel: + """Enterprise Edition Model (Autonomous Execution)""" - @staticmethod - def create_animated_radar_chart(metrics: Dict[str, float], title: str = "Performance Radar"): - """Create animated radar chart - GUARANTEED WORKING""" + def __init__(self): + self.execution_history = [] + self.learning_engine = LearningEngine() + + def execute_healing(self, scenario: Dict, approval_required: bool = True) -> Dict[str, Any]: + """Execute healing actions with optional approval""" try: - # Always create a valid radar chart - if not metrics: - metrics = { - "Latency": 85.0, - "Error Rate": 22.0, - "CPU Usage": 95.0, - "Memory": 88.0, - "Throughput": 65.0, - "Availability": 92.0 - } + execution_id = str(uuid.uuid4())[:8] + timestamp = datetime.datetime.now() - # Convert to list format - categories = list(metrics.keys())[:6] - values = [] - for cat in categories: - val = metrics.get(cat, 50) - if isinstance(val, (int, float)): - values.append(float(val)) - else: - values.append(50.0) # Default + actions = scenario.get("enterprise_actions", []) + execution_results = scenario.get("execution_results", {}) - # Create radar chart - fig = go.Figure() + if approval_required: + status = "✅ Approved and Executed" + else: + status = "✅ Auto-Executed" - fig.add_trace(go.Scatterpolar( - r=values, - theta=categories, - fill='toself', - name='Current', - line_color='#4CAF50', - opacity=0.8, - marker=dict(size=8) - )) - - # Add target line - target_values = [v * 1.2 for v in values] - fig.add_trace(go.Scatterpolar( - r=target_values, - theta=categories, - fill='toself', - name='Target', - line_color='#2196F3', - opacity=0.3 - )) + execution_record = { + "id": execution_id, + "timestamp": timestamp, + "scenario": scenario.get("name"), + "actions": actions, + "results": execution_results, + "status": status, + "business_impact": scenario.get("business_impact", {}) + } - fig.update_layout( - polar=dict( - radialaxis=dict( - visible=True, - range=[0, 100] - ) - ), - showlegend=True, - title=dict( - text=title, - x=0.5, - font=dict(size=16) - ), - height=400, - margin=dict(l=80, r=80, t=60, b=60), - ) + self.execution_history.append(execution_record) + self.learning_engine.record_execution(execution_record) - return fig + return { + "execution_id": execution_id, + "timestamp": timestamp.isoformat(), + "actions_executed": len(actions), + "results": execution_results, + "status": status, + "learning_applied": True, + "compliance_logged": True + } - except: - # Ultimate fallback - fig = go.Figure() - fig.add_trace(go.Bar( - x=['Latency', 'Errors', 'CPU', 'Memory', 'Throughput'], - y=[85, 22, 95, 88, 65], - marker_color='#4CAF50' - )) - fig.update_layout( - title=title, - height=400, - showlegend=False - ) - return fig + except Exception as e: + logger.error(f"Enterprise execution failed: {e}") + return { + "execution_id": "ERROR", + "timestamp": datetime.datetime.now().isoformat(), + "actions_executed": 0, + "results": {}, + "status": "❌ Execution Failed", + "learning_applied": False, + "compliance_logged": False + } + +class LearningEngine: + """Continuous learning engine for Enterprise edition""" - @staticmethod - def create_heatmap_timeline(scenarios: List[Dict[str, Any]]): - """Create heatmap timeline - SIMPLIFIED GUARANTEED WORKING""" - # Always return a working heatmap with predefined data - scenario_names = [ - "Payment Crisis", - "DB Exhaustion", - "Memory Leak", - "API Errors", - "CDN Outage" - ] - - # Data matrix (Revenue in $M, Users in K, Severity 1-3, Time in min) - data = [ - [2.5, 45.0, 3, 2.3], # Payment Crisis - [1.2, 12.0, 2, 8.5], # DB Exhaustion - [0.25, 65.0, 1, 0.8], # Memory Leak - [0.15, 8.0, 1, 45.0], # API Errors - [3.5, 200.0, 3, 15.5] # CDN Outage - ] - - # Labels for Y-axis - y_labels = [ - "Revenue ($M)", - "Users (K)", - "Severity", - "Time (min)" - ] - - # Create heatmap - fig = go.Figure(data=go.Heatmap( - z=data, - x=scenario_names, - y=y_labels, - colorscale=[ - [0.0, '#4CAF50'], # Green - [0.5, '#FFEB3B'], # Yellow - [1.0, '#F44336'] # Red - ], - colorbar=dict( - title="Impact Level", - titleside="right" - ), - hoverongaps=False, - hovertemplate='%{x}
%{y}: %{z}' - )) - - fig.update_layout( - title=dict( - text="🔥 Incident Severity Heatmap", - x=0.5, - font=dict(size=16) - ), - xaxis_title="Incident Scenarios", - yaxis_title="Impact Metrics", - height=450, - xaxis={'tickangle': 45}, - margin=dict(l=80, r=20, t=60, b=80) - ) - - return fig + def __init__(self): + self.patterns_learned = [] + self.successful_resolutions = [] - @staticmethod - def create_real_time_metrics_stream(): - """Create real-time streaming metrics - GUARANTEED WORKING""" - # Generate time series data - times = pd.date_range(start='now', periods=50, freq='1min') - values = 90 + np.random.randn(50) * 5 - - fig = go.Figure() - - fig.add_trace(go.Scatter( - x=times, - y=values, - mode='lines', - name='System Health', - line=dict( - color='#2196F3', - width=3, - ), - fill='tozeroy', - fillcolor='rgba(33, 150, 243, 0.1)', - hovertemplate='Time: %{x|%H:%M}
Health: %{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=85, line_dash="dash", line_color="orange", - annotation_text="Warning", annotation_position="right") - fig.add_hline(y=75, line_dash="dash", line_color="red", - annotation_text="Critical", annotation_position="right") - - fig.update_layout( - title=dict( - text="📊 Real-time System Health Monitor", - x=0.5, - font=dict(size=16) - ), - xaxis=dict( - title="Time", - rangeslider=dict(visible=True), - type="date", - tickformat="%H:%M" - ), - yaxis=dict( - title="Health Score (%)", - range=[70, 100] - ), - height=420, - showlegend=True, - hovermode="x unified", - margin=dict(l=60, r=20, t=60, b=60), - ) - - return fig + def record_execution(self, execution: Dict): + """Record execution for learning""" + if execution.get("status", "").startswith("✅"): + self.successful_resolutions.append(execution) + + # Extract patterns + pattern = { + "scenario": execution["scenario"], + "actions": execution["actions"], + "effectiveness": random.uniform(0.7, 0.95), + "learned_at": datetime.datetime.now() + } + self.patterns_learned.append(pattern) + + def get_insights(self) -> List[Dict]: + """Get learned insights""" + return self.patterns_learned[-5:] if self.patterns_learned else [] -# ============================================================================ -# EXPORT ENGINE -# ============================================================================ +# =========================================== +# ROI CALCULATOR +# =========================================== -class ExportEngine: - """Handle export of reports, charts, and data""" +class ROICalculator: + """Enhanced ROI calculator with business metrics""" @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'), - ] + def calculate_roi(incident_scenarios: List[Dict]) -> Dict[str, Any]: + """Calculate ROI based on incident scenarios""" + total_impact = 0 + enterprise_savings = 0 + incidents_resolved = 0 - for key, label in metrics_to_show: - if key in roi_data: - html += f""" -
-
{label}
-
{roi_data[key]}
-
- """ - - html += """ -
- -

📈 Detailed Breakdown

- - - """ + for scenario in incident_scenarios: + if isinstance(scenario, dict) and scenario.get("business_impact"): + impact_str = scenario["business_impact"].get("total_impact", "$0") + try: + impact_value = float(impact_str.replace("$", "").replace(",", "")) + total_impact += impact_value + + # Enterprise saves 70-90% of impact + savings_rate = random.uniform(0.7, 0.9) + enterprise_savings += impact_value * savings_rate + incidents_resolved += 1 + except (ValueError, AttributeError): + continue - # 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'), - ] + if total_impact == 0: + total_impact = 25000 # Default for demo + enterprise_savings = total_impact * 0.82 + incidents_resolved = 3 - for comp in comparisons: - html += f""" - - - - - - - """ + # Calculate ROI + enterprise_cost = 1200000 # Annual enterprise cost + annual_savings = enterprise_savings * 52 # Weekly incidents * 52 weeks - html += f""" -
MetricWithout ARFWith ARFImprovement
{comp[0]}{comp[1]}{comp[2]}{comp[3]}
- -
-

ARF Ultimate Investor Demo v3.4.0 | Generated automatically

-

Confidential - For investor review only

-

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

-
- - - """ + if enterprise_cost > 0: + roi_percentage = ((annual_savings - enterprise_cost) / enterprise_cost) * 100 + else: + roi_percentage = 520 # 5.2x ROI default - return html - -# ============================================================================ -# DEMO SCENARIOS - ENHANCED -# ============================================================================ - -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, - }, - "oss_action": "scale_out", - "enterprise_action": "autonomous_scale", - "prediction": "Database crash predicted in 8.5 minutes", - }, - - "⚡ 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, - }, - "oss_action": "restart_container", - "enterprise_action": "approval_workflow", - "prediction": "Cascading failure in 3.2 minutes", - }, - - "🔮 Predictive Memory Leak": { - "description": "Memory leak detected. $250K at risk in 18 minutes.", - "component": "cache-service", - "metrics": { - "latency_ms": 320, - "error_rate": 0.05, - "cpu_util": 0.45, - "memory_util": 0.94, - "cache_hit_rate": 0.12, - "garbage_collection": 45, - }, - "business_impact": { - "revenue_at_risk": 250000, - "users_impacted": 65000, - "time_to_resolve": 0.8, - "auto_heal_possible": True, - }, - "oss_action": "restart_container", - "enterprise_action": "predictive_prevention", - "prediction": "Outage prevented 17 minutes before crash", - }, - - "📈 API Error Rate Spike": { - "description": "API errors increasing. Requires investigation.", - "component": "api-service", - "metrics": { - "latency_ms": 120, - "error_rate": 0.25, - "cpu_util": 0.35, - "memory_util": 0.42, - "requests_per_second": 4500, - "timeout_rate": 0.15, - }, - "business_impact": { - "revenue_at_risk": 150000, - "users_impacted": 8000, - "time_to_resolve": 45.0, - "auto_heal_possible": False, - }, - "oss_action": "rollback", - "enterprise_action": "root_cause_analysis", - "prediction": "Error rate will reach 35% in 22 minutes", - }, - - "🌐 Global CDN Outage": { - "description": "CDN failing across 3 regions affecting 200K users", - "component": "cdn-service", - "metrics": { - "latency_ms": 1200, - "error_rate": 0.65, - "cpu_util": 0.25, - "memory_util": 0.35, - "bandwidth_util": 0.98, - "regional_availability": 0.33, - }, - "business_impact": { - "revenue_at_risk": 3500000, - "users_impacted": 200000, - "time_to_resolve": 15.5, - "auto_heal_possible": True, - }, - "oss_action": "failover_regions", - "enterprise_action": "geo_load_balancing", - "prediction": "Global outage spreading to 5 regions in 12 minutes", - }, -} + return { + "total_annual_impact": f"${total_impact * 52:,.0f}", + "enterprise_annual_savings": f"${annual_savings:,.0f}", + "enterprise_annual_cost": f"${enterprise_cost:,.0f}", + "roi_percentage": f"{roi_percentage:.1f}%", + "roi_multiplier": f"{(annual_savings / enterprise_cost):.1f}×", + "incidents_resolved_annually": incidents_resolved * 52, + "avg_resolution_time_oss": "45 minutes", + "avg_resolution_time_enterprise": "8 minutes", + "savings_per_incident": f"${enterprise_savings/incidents_resolved if incidents_resolved > 0 else 0:,.0f}" + } -# ============================================================================ -# MAIN DEMO UI - FINAL FIXED VERSION v3.4.0 -# ============================================================================ +# =========================================== +# MAIN APPLICATION +# =========================================== -def create_enhanced_demo(): - """Create enhanced ultimate investor demo UI - FINAL FIXED VERSION v3.4.0""" +class ARFUltimateInvestorDemo: + """Main application class for ARF Ultimate Investor Demo v3.4.0""" - # Initialize enhanced components - business_calc = BusinessImpactCalculator() - rag_visualizer = RAGGraphVisualizer() - predictive_viz = PredictiveVisualizer() - live_dashboard = LiveDashboard() - viz_engine = EnhancedVisualizationEngine() - export_engine = ExportEngine() - enterprise_servers = {} + def __init__(self): + self.viz_engine = VisualizationEngine() + self.incident_scenarios = IncidentScenarios() + self.oss_model = OSSModel() + self.enterprise_model = EnterpriseModel() + self.roi_calculator = ROICalculator() + + # Initialize incident history for visualizations + self._init_incident_history() - with gr.Blocks(title="🚀 ARF Ultimate Investor Demo v3.4.0") as demo: - gr.Markdown(""" - # 🚀 Agentic Reliability Framework - Ultimate Investor Demo v3.4.0 - ### **From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability** + def _init_incident_history(self): + """Initialize sample incident history for visualizations""" + services = ["API Gateway", "Database", "Cache", "Auth Service", "Payment Service"] -
-
-
-

🎯 Enhanced Investor Demo v3.4.0

-

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

-
-
-

🚀 All visualizations working

-

📊 Professional analytics & export features

-
-
-
+ for i in range(20): + hour = random.randint(0, 23) + severity = random.choices([0, 1, 2, 3], weights=[0.3, 0.4, 0.2, 0.1])[0] + + if severity > 0: # Only record actual incidents + self.viz_engine.incident_history.append({ + "timestamp": datetime.datetime.now() - datetime.timedelta(hours=24-i), + "hour": hour, + "service": random.choice(services), + "severity": severity, + "type": random.choice(["latency", "error", "timeout", "crash"]) + }) + + def create_demo_interface(self): + """Create the main Gradio interface""" - *Watch as ARF transforms reliability from a $2M cost center to a $10M profit engine* - """) + # CSS for professional styling + css = """ + .gradio-container { + max-width: 1400px !important; + margin: 0 auto !important; + } + .dashboard-header { + background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); + padding: 2rem; + border-radius: 10px; + margin-bottom: 2rem; + color: white; + } + .metric-card { + background: white; + padding: 1.5rem; + border-radius: 10px; + box-shadow: 0 4px 6px rgba(0,0,0,0.1); + margin-bottom: 1rem; + border-left: 4px solid #667eea; + } + .enterprise-card { + border-left: 4px solid #10b981; + } + .oss-card { + border-left: 4px solid #f59e0b; + } + .capability-table { + width: 100%; + border-collapse: collapse; + margin: 1rem 0; + } + .capability-table th, .capability-table td { + padding: 12px; + text-align: left; + border-bottom: 1px solid #e5e7eb; + } + .capability-table th { + background-color: #f9fafb; + font-weight: 600; + } + .success { color: #10b981; } + .warning { color: #f59e0b; } + .error { color: #ef4444; } + .info { color: #3b82f6; } + """ - # ================================================================ - # ENHANCED EXECUTIVE DASHBOARD TAB - # ================================================================ - with gr.TabItem("🏢 Executive Dashboard"): - gr.Markdown(""" - ## 📊 Real-Time Business Impact Dashboard - **Live metrics showing ARF's financial impact in enterprise deployments** - """) - - with gr.Row(): - with gr.Column(scale=1): - revenue_protected = gr.Markdown("### 💰 Revenue Protected\n**$0**") - with gr.Column(scale=1): - auto_heal_rate = gr.Markdown("### ⚡ Auto-Heal Rate\n**0%**") - with gr.Column(scale=1): - mttr_improvement = gr.Markdown("### 🚀 MTTR Improvement\n**94% faster**") - with gr.Column(scale=1): - engineer_hours = gr.Markdown("### 👷 Engineer Hours Saved\n**0 hours**") + with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo: - # Real-time streaming metrics - gr.Markdown("### 📈 Real-time System Health Monitor") - real_time_metrics = gr.Plot( - label="", - ) - - # Enhanced incident feed - gr.Markdown("### 🔥 Live Incident Feed") - incident_feed = gr.Dataframe( - headers=["Time", "Service", "Impact", "Status", "Value Protected"], - value=[], - interactive=False, - ) - - # Top customers protected - gr.Markdown("### 🏆 Top Customers Protected") - 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, - ) - - # ================================================================ - # ENHANCED LIVE WAR ROOM TAB - # ================================================================ - with gr.TabItem("🔥 Live War Room"): - gr.Markdown(""" - ## 🔥 Multi-Incident War Room - **Watch ARF handle 5+ simultaneous incidents across different services** - """) + # ============ HEADER ============ + with gr.Column(elem_classes="dashboard-header"): + gr.Markdown(""" + # 🚀 Agentic Reliability Framework - Ultimate Investor Demo v3.4.0 + ### From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability + + **🎯 Enhanced Investor Demo v3.4.0** + Experience the full spectrum: OSS (Free) ↔ Enterprise (Paid) + + 🚀 **All visualizations working** + 📊 **Professional analytics & export features** + + *Watch as ARF transforms reliability from a $2M cost center to a $10M profit engine* + """) - with gr.Row(): - with gr.Column(scale=1): - # Enhanced scenario selector - 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, - ) - - # Visualization type selector - viz_type = gr.Radio( - choices=["Radar Chart", "Heatmap", "Stream"], - value="Radar Chart", - label="📊 Visualization Type", - info="Choose how to visualize the metrics" - ) - - # Metrics display - metrics_display = gr.JSON( - label="📊 Current Metrics", - value={}, - ) - - # Business impact - impact_display = gr.JSON( - label="💰 Business Impact Analysis", - value={}, - ) - - # Action buttons - with gr.Row(): - oss_action_btn = gr.Button("🤖 OSS: Analyze & Recommend", variant="secondary") - enterprise_action_btn = gr.Button("🚀 Enterprise: Execute Healing", variant="primary") - - # Enterprise configuration - 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" - ) - - execution_mode = gr.Radio( - choices=["autonomous", "approval"], - value="autonomous", - label="⚙️ Execution Mode", - info="How to execute the healing action" - ) + # ============ MAIN TABS ============ + with gr.Tabs(): - with gr.Column(scale=2): - # Enhanced results display with tabs - with gr.Tabs(): - with gr.TabItem("🎯 Execution Results"): - result_display = gr.JSON( - label="", - value={}, + # ============ TAB 1: MULTI-INCIDENT WAR ROOM ============ + with gr.TabItem("🔥 Multi-Incident War Room"): + with gr.Row(): + with gr.Column(scale=2): + gr.Markdown("### 🎬 Select Incident Scenario") + scenario_dropdown = gr.Dropdown( + choices=[ + ("Database Connection Pool Exhaustion", "database_connection_pool_exhaustion"), + ("API Rate Limit Exceeded", "api_rate_limit_exceeded"), + ("Cache Miss Storm", "cache_miss_storm"), + ("Microservice Cascading Failure", "microservice_cascading_failure"), + ("Memory Leak in Production", "memory_leak_in_production") + ], + label="Choose an enterprise incident scenario", + value="database_connection_pool_exhaustion" ) - - with gr.TabItem("📈 Performance Analysis"): - performance_chart = gr.Plot( - label="Performance Radar Chart", + + gr.Markdown("### 📊 Visualization Type") + viz_type = gr.Radio( + choices=["Radar Chart", "Heatmap", "Stream"], + label="Choose how to visualize the metrics", + value="Radar Chart" ) + + # Metrics display + gr.Markdown("### 📊 Current Metrics") + metrics_display = gr.JSON(label="Live Metrics", value={}) + + # Business Impact + gr.Markdown("### 💰 Business Impact Analysis") + business_impact = gr.JSON(label="Impact Analysis", value={}) - with gr.TabItem("🔥 Incident Heatmap"): - incident_heatmap = gr.Plot( - label="Incident Severity Heatmap", - ) + with gr.Column(scale=3): + # OSS Analysis + with gr.Group(elem_classes="oss-card"): + gr.Markdown("### 🤖 OSS: Analyze & Recommend") + oss_analyze_btn = gr.Button("🚀 Run OSS Analysis", variant="secondary") + oss_results = gr.JSON(label="OSS Analysis Results", value={}) + + # Enterprise Execution + with gr.Group(elem_classes="enterprise-card"): + gr.Markdown("### 🚀 Enterprise: Execute Healing") + + with gr.Row(): + approval_toggle = gr.Checkbox( + label="Require Manual Approval", + value=True, + info="Enterprise can auto-execute or wait for approval" + ) + execute_btn = gr.Button("⚡ Execute Autonomous Healing", variant="primary") + + enterprise_config = gr.JSON( + label="⚙️ Enterprise Configuration", + value={"approval_required": True, "compliance_mode": "strict"} + ) + + enterprise_results = gr.JSON(label="🎯 Execution Results", value={}) + + # Visualizations + visualization_output = gr.Plot(label="📈 Performance Analysis") + heatmap_output = gr.Plot(label="🔥 Incident Heatmap") + + # ============ TAB 2: EXECUTIVE DASHBOARD ============ + with gr.TabItem("🏢 Executive Dashboard"): + with gr.Row(): + with gr.Column(): + gr.Markdown("### 📊 Performance Overview") + performance_radar = gr.Plot() + + gr.Markdown("### 🔮 Predictive Analytics") + predictive_timeline = gr.Plot() + + with gr.Column(): + gr.Markdown("### 🧠 Learning Engine Insights") + rag_memory_viz = gr.Plot() + + gr.Markdown("### 💰 ROI Calculator") + roi_results = gr.JSON(value={}) + calculate_roi_btn = gr.Button("📊 Calculate ROI", variant="primary") + + # ============ TAB 3: CAPABILITY COMPARISON ============ + with gr.TabItem("📊 Capability Matrix"): + gr.Markdown(""" + ### 🚀 Ready to transform your reliability operations? - # RAG Graph visualization - rag_graph = gr.Plot( - label="🧠 RAG Graph Memory Visualization", - ) + **Capability Comparison:** - # Predictive Timeline - predictive_timeline = gr.Plot( - label="🔮 Predictive Analytics Timeline", - ) + | Capability | OSS Edition | Enterprise 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](https://arf.dev) + 📚 **Documentation:** [https://docs.arf.dev](https://docs.arf.dev) + 💻 **GitHub:** [petterjuan/agentic-reliability-framework](https://github.com/petterjuan/agentic-reliability-framework) + """) - # FIXED: Function to update scenario with enhanced visualization - def update_scenario_enhanced(scenario_name, viz_type): - scenario = ENTERPRISE_SCENARIOS.get(scenario_name, {}) - - # Add to RAG graph - if scenario: - rag_visualizer.add_incident( - component=scenario.get("component", "unknown"), - severity="critical" if scenario.get("business_impact", {}).get("revenue_at_risk", 0) > 1000000 else "high" - ) + # ============ EVENT HANDLERS ============ + + def update_scenario_enhanced(scenario_id: str, viz_type: str): + """Update all displays based on selected scenario""" + scenario = self.incident_scenarios.get_scenario(scenario_id) - # Get impact analysis - impact_analysis = {} - if scenario and "business_impact" in scenario: - impact_analysis = business_calc.calculate_impact(scenario["business_impact"]) + # Update metrics display + metrics = scenario.get("current_metrics", {}) + business_impact = scenario.get("business_impact", {}) - # Get metrics - scenario_metrics = scenario.get("metrics", {}) if scenario else {} + # Create visualization based on type + if viz_type == "Radar Chart": + viz = self.viz_engine.create_performance_radar(metrics) + elif viz_type == "Heatmap": + viz = self.viz_engine.create_heatmap_timeline(self.viz_engine.incident_history) + else: # Stream + viz = self.viz_engine.create_stream_graph([ + {"timestamp": f"{i}:00", **{k: v + random.randint(-10, 10) for k, v in metrics.items()}} + for i in range(24) + ]) - # Select visualization based on type - try: - if viz_type == "Radar Chart": - viz_fig = viz_engine.create_animated_radar_chart( - scenario_metrics, - f"Performance - {scenario_name[:20]}..." - ) - elif viz_type == "Heatmap": - viz_fig = viz_engine.create_heatmap_timeline([scenario] if scenario else []) - else: # Stream - viz_fig = viz_engine.create_real_time_metrics_stream() - except: - # Use default visualization if any error occurs - viz_fig = viz_engine.create_real_time_metrics_stream() + # Update heatmap + incident_heatmap = self.viz_engine.create_heatmap_timeline(self.viz_engine.incident_history) return { - metrics_display: scenario_metrics, - impact_display: impact_analysis, - 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] if scenario else []), - real_time_metrics: viz_engine.create_real_time_metrics_stream(), + metrics_display: metrics, + business_impact: business_impact, + visualization_output: viz, + heatmap_output: incident_heatmap } - # Function for OSS analysis - async def oss_analysis(scenario_name): - scenario = ENTERPRISE_SCENARIOS.get(scenario_name, {}) - - return { - result_display: { - "status": "OSS_ADVISORY_COMPLETE", - "action": scenario.get("oss_action", "unknown"), - "component": scenario.get("component", "unknown"), - "message": f"✅ OSS analysis recommends {scenario.get('oss_action')} for {scenario.get('component')}", - "requires_enterprise": True, - "confidence": 0.85, - "enterprise_features_required": [ - "autonomous_execution", - "learning_engine", - "audit_trails", - "compliance_reporting", - ], - "upgrade_url": "https://arf.dev/enterprise", - } - } + def run_oss_analysis(scenario_id: str): + """Run OSS analysis on selected scenario""" + scenario = self.incident_scenarios.get_scenario(scenario_id) + analysis = self.oss_model.analyze_and_recommend(scenario) + return {oss_results: analysis} - # Function for Enterprise execution - async def enterprise_execution(scenario_name, license_key, mode): - scenario = ENTERPRISE_SCENARIOS.get(scenario_name, {}) - - # Create or get enterprise server - if license_key not in enterprise_servers: - enterprise_servers[license_key] = MockEnterpriseServer(license_key) - - server = enterprise_servers[license_key] + def run_enterprise_execution(scenario_id: str, approval_required: bool): + """Execute enterprise healing actions""" + scenario = self.incident_scenarios.get_scenario(scenario_id) + results = self.enterprise_model.execute_healing(scenario, approval_required) - # Create healing intent - healing_intent = { - "action": scenario.get("enterprise_action", "unknown"), - "component": scenario.get("component", "unknown"), - "justification": f"Enterprise execution for {scenario_name}", - "confidence": 0.92, - "parameters": {"scale_factor": 3} if "scale" in scenario.get("enterprise_action", "") else {}, - } + # Update ROI + roi = self.roi_calculator.calculate_roi([scenario]) - # Execute - result = await server.execute_healing(healing_intent, mode) + # Update visualizations + rag_viz = self.viz_engine.create_rag_memory_viz({ + "nodes": [ + {"id": f"exec_{i}", "type": "Execution", "importance": i+1, "color_idx": i} + for i in range(5) + ], + "edges": [ + {"source": i, "target": (i+1)%5} + for i in range(5) + ] + }) - # Update dashboard - live_dashboard.add_execution_result(result["revenue_protected"]) + predictive_viz = self.viz_engine.create_predictive_timeline(self.viz_engine.incident_history) - # Add to RAG graph - if rag_visualizer.incidents: - rag_visualizer.add_outcome( - incident_id=rag_visualizer.incidents[-1]["id"], - success=result["success"], - action=healing_intent["action"] - ) + return { + enterprise_results: results, + roi_results: roi, + rag_memory_viz: rag_viz, + predictive_timeline: predictive_viz + } + + def calculate_comprehensive_roi(): + """Calculate comprehensive ROI""" + all_scenarios = [ + self.incident_scenarios.get_scenario(key) + for key in self.incident_scenarios.SCENARIOS.keys() + ] + roi = self.roi_calculator.calculate_roi(all_scenarios) - # Update dashboard displays - dashboard_data = live_dashboard.get_dashboard_data() + # Update performance radar with ROI metrics + roi_metrics = { + "ROI Multiplier": float(roi["roi_multiplier"].replace("×", "")), + "Annual Savings": float(roi["enterprise_annual_savings"].replace("$", "").replace(",", "")) / 1000000, + "Resolution Speed": 90, # Percentage improvement + "Incidents Prevented": 85, + "Cost Reduction": 72 + } + performance_viz = self.viz_engine.create_performance_radar(roi_metrics) return { - result_display: { - **result, - "rag_stats": rag_visualizer.get_stats(), - "dashboard_update": dashboard_data, - }, - rag_graph: rag_visualizer.get_graph_figure(), - revenue_protected: f"### 💰 Revenue Protected\n**{dashboard_data['revenue_protected']}**", - auto_heal_rate: f"### ⚡ Auto-Heal Rate\n**{dashboard_data['auto_heal_rate']}**", - engineer_hours: f"### 👷 Engineer Hours Saved\n**{dashboard_data['engineer_hours_saved']}**", - incident_feed: [[ - datetime.datetime.now().strftime("%H:%M:%S"), - scenario.get("component", "unknown"), - f"${result['revenue_protected']:,.0f}", - "✅ Resolved" if result["success"] else "⚠️ Partial", - f"${result['revenue_protected']:,.0f}" - ]], + roi_results: roi, + performance_radar: performance_viz } - # Connect events - scenario_selector.change( + # ============ EVENT BINDINGS ============ + + # Scenario updates + scenario_dropdown.change( fn=update_scenario_enhanced, - inputs=[scenario_selector, viz_type], - outputs=[metrics_display, impact_display, rag_graph, predictive_timeline, - performance_chart, incident_heatmap, real_time_metrics] + inputs=[scenario_dropdown, viz_type], + outputs=[metrics_display, business_impact, visualization_output, heatmap_output] ) viz_type.change( fn=lambda scenario, viz_type: update_scenario_enhanced(scenario, viz_type), - inputs=[scenario_selector, viz_type], - outputs=[performance_chart, incident_heatmap] + inputs=[scenario_dropdown, viz_type], + outputs=[metrics_display, business_impact, visualization_output, heatmap_output] ) - oss_action_btn.click( - fn=oss_analysis, - inputs=[scenario_selector], - outputs=[result_display] + # OSS Analysis + oss_analyze_btn.click( + fn=run_oss_analysis, + inputs=[scenario_dropdown], + outputs=[oss_results] ) - enterprise_action_btn.click( - fn=enterprise_execution, - inputs=[scenario_selector, license_input, execution_mode], - outputs=[result_display, rag_graph, revenue_protected, auto_heal_rate, engineer_hours, incident_feed] + # Enterprise Execution + execute_btn.click( + fn=run_enterprise_execution, + inputs=[scenario_dropdown, approval_toggle], + outputs=[enterprise_results, roi_results, rag_memory_viz, predictive_timeline] ) - - # ================================================================ - # ENHANCED LEARNING ENGINE TAB - # ================================================================ - with gr.TabItem("🧠 Learning Engine"): - gr.Markdown(""" - ## 🧠 RAG Graph Learning Engine - **Watch ARF learn from every incident and outcome** - """) - - with gr.Row(): - with gr.Column(scale=1): - # Learning stats - learning_stats = gr.JSON( - label="📊 Learning Statistics", - value=rag_visualizer.get_stats(), - ) - - # Simulate learning button - simulate_learning_btn = gr.Button("🎓 Simulate Learning Cycle", variant="primary") - - # Export knowledge button - export_btn = gr.Button("📤 Export Learned Patterns", variant="secondary") - - with gr.Column(scale=2): - # RAG Graph visualization - learning_graph = gr.Plot( - label="🔗 Knowledge Graph Visualization", - ) - # Simulate learning - def simulate_learning(): - # Add random incidents and outcomes - components = ["payment-service", "database", "api-service", "cache", "auth-service"] - actions = ["scale_out", "restart_container", "rollback", "circuit_breaker"] - - for _ in range(3): - 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() > 0.2, # 80% success rate - action=random.choice(actions) - ) - - return { - learning_graph: rag_visualizer.get_graph_figure(), - learning_stats: rag_visualizer.get_stats(), - } - - # Connect events - simulate_learning_btn.click( - fn=simulate_learning, - outputs=[learning_graph, learning_stats] + # ROI Calculation + calculate_roi_btn.click( + fn=calculate_comprehensive_roi, + inputs=[], + outputs=[roi_results, performance_radar] ) - export_btn.click( - fn=lambda: {"message": "✅ Knowledge patterns exported to Neo4j for persistent learning"}, - outputs=[gr.JSON(value={"message": "✅ Knowledge patterns exported"})] + # Initial load + demo.load( + fn=lambda: update_scenario_enhanced("database_connection_pool_exhaustion", "Radar Chart"), + inputs=[], + outputs=[metrics_display, business_impact, visualization_output, heatmap_output] ) - - # ================================================================ - # ENHANCED COMPLIANCE AUDITOR TAB - # ================================================================ - with gr.TabItem("📝 Compliance Auditor"): - 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 standard selector - compliance_standard = gr.Dropdown( - choices=["SOC2", "GDPR", "HIPAA", "ISO27001", "PCI-DSS"], - value="SOC2", - label="📋 Compliance Standard", - ) - - # License input - compliance_license = gr.Textbox( - label="🔑 Enterprise License Required", - value="ARF-ENT-COMPLIANCE", - interactive=True, - ) - - # Generate report button - generate_report_btn = gr.Button("⚡ Generate Compliance Report", variant="primary") - - # Audit trail viewer - audit_trail = gr.Dataframe( - label="📜 Live Audit Trail", - headers=["Time", "Action", "Component", "User", "Status"], - value=[], - ) - - with gr.Column(scale=2): - # Report display - compliance_report = gr.JSON( - label="📄 Compliance Report", - value={}, - ) - - # Generate compliance report - def generate_compliance_report(standard, license_key): - if "ENT" not in license_key: - return { - compliance_report: { - "error": "Enterprise license required", - "message": "Compliance features require Enterprise license", - "upgrade_url": "https://arf.dev/enterprise", - } - } - - # Create mock enterprise server - if license_key not in enterprise_servers: - enterprise_servers[license_key] = MockEnterpriseServer(license_key) - - server = enterprise_servers[license_key] - report = server.generate_compliance_report(standard) - - # Update audit trail - audit_data = [] - for entry in server.audit_trail[-10:]: # Last 10 entries - audit_data.append([ - entry["timestamp"][11:19], # Just time - entry["action"], - entry["component"], - "ARF System", - "✅" if entry["success"] else "⚠️", - ]) - - return { - compliance_report: report, - audit_trail: audit_data, - } - - generate_report_btn.click( - fn=generate_compliance_report, - inputs=[compliance_standard, compliance_license], - outputs=[compliance_report, audit_trail] + demo.load( + fn=calculate_comprehensive_roi, + inputs=[], + outputs=[roi_results, performance_radar] ) - - # ================================================================ - # ENHANCED ROI CALCULATOR TAB - # ================================================================ - with gr.TabItem("💰 ROI Calculator"): + + # Footer gr.Markdown(""" - ## 💰 Enterprise ROI Calculator - **Calculate your potential savings with ARF Enterprise** + --- + 🚀 **ARF Ultimate Investor Demo v3.4.0** | Enhanced with Professional Analytics & Export Features + *Built with ❤️ using Gradio & Plotly | All visualizations guaranteed working* """) - - with gr.Row(): - with gr.Column(scale=1): - # Inputs - monthly_revenue = gr.Number( - value=1000000, - label="Monthly Revenue ($)", - info="Your company's monthly revenue" - ) - - monthly_incidents = gr.Slider( - minimum=1, - maximum=100, - value=20, - label="Monthly Incidents", - info="Reliability incidents per month" - ) - - team_size = gr.Slider( - minimum=1, - maximum=20, - value=3, - label="SRE/DevOps Team Size", - info="Engineers handling incidents" - ) - - avg_incident_cost = gr.Number( - value=1500, - label="Average Incident Cost ($)", - info="Revenue loss + engineer time per incident" - ) - - calculate_roi_btn = gr.Button("📈 Calculate ROI", variant="primary") - - with gr.Column(scale=2): - # Results - roi_results = gr.JSON( - label="📊 ROI Analysis Results", - value={}, - ) - - # Visualization - roi_chart = gr.Plot( - label="📈 ROI Visualization", - ) - - # Calculate ROI - def calculate_roi(revenue, incidents, team_size, incident_cost): - # ARF metrics (based on real deployments) - auto_heal_rate = 0.817 # 81.7% - mttr_reduction = 0.94 # 94% faster - engineer_time_savings = 0.85 # 85% less engineer time - - # Ensure numeric values - try: - incidents = float(incidents) if incidents else 0 - team_size = float(team_size) if team_size else 0 - incident_cost = float(incident_cost) if incident_cost else 0 - except: - incidents = team_size = incident_cost = 0 - - # Calculations - manual_incidents = incidents * (1 - auto_heal_rate) - auto_healed = incidents * auto_heal_rate - - # Costs without ARF - traditional_cost = incidents * incident_cost - engineer_cost = incidents * 2.5 * 100 * team_size # 2.5 hours at $100/hour - total_traditional_cost = traditional_cost + engineer_cost - - # Costs with ARF - arf_incident_cost = manual_incidents * incident_cost * (1 - mttr_reduction) - arf_engineer_cost = manual_incidents * 2.5 * 100 * team_size * engineer_time_savings - total_arf_cost = arf_incident_cost + arf_engineer_cost - - # Savings - monthly_savings = total_traditional_cost - total_arf_cost - annual_savings = monthly_savings * 12 - implementation_cost = 47500 # $47.5K implementation - - # ROI - payback_months = implementation_cost / monthly_savings if monthly_savings > 0 else 999 - first_year_roi = ((annual_savings - implementation_cost) / implementation_cost) * 100 if implementation_cost > 0 else 0 - - # Create chart - fig = go.Figure(data=[ - go.Bar(name='Without ARF', x=['Monthly Cost'], y=[total_traditional_cost], marker_color='#ff4444'), - go.Bar(name='With ARF', x=['Monthly Cost'], y=[total_arf_cost], marker_color='#44ff44'), - ]) - fig.update_layout( - title="Monthly Cost Comparison", - yaxis_title="Cost ($)", - barmode='group', - height=300, - ) - - return { - roi_results: { - "monthly_revenue": f"${revenue:,.0f}", - "monthly_incidents": incidents, - "auto_heal_rate": f"{auto_heal_rate*100:.1f}%", - "mttr_improvement": f"{mttr_reduction*100:.0f}%", - "monthly_savings": f"${monthly_savings:,.0f}", - "annual_savings": f"${annual_savings:,.0f}", - "implementation_cost": f"${implementation_cost:,.0f}", - "payback_period": f"{payback_months:.1f} months", - "first_year_roi": f"{first_year_roi:.1f}%", - "key_metrics": { - "incidents_auto_healed": f"{auto_healed:.0f}/month", - "engineer_hours_saved": f"{(incidents * 2.5 * engineer_time_savings):.0f} hours/month", - "revenue_protected": f"${(incidents * incident_cost * auto_heal_rate):,.0f}/month", - } - }, - roi_chart: fig, - } - - calculate_roi_btn.click( - fn=calculate_roi, - inputs=[monthly_revenue, monthly_incidents, team_size, avg_incident_cost], - outputs=[roi_results, roi_chart] - ) - - # Enhanced footer - gr.Markdown(""" - --- -
-
-
-

🚀 Ready to transform your reliability operations?

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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
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- -
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📞 Contact & Resources

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📧 Email: enterprise@petterjuan.com

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🌐 Website: https://arf.dev

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📚 Documentation: https://docs.arf.dev

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💻 GitHub: petterjuan/agentic-reliability-framework

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🚀 ARF Ultimate Investor Demo v3.4.0 | Enhanced with Professional Analytics & Export Features

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Built with ❤️ using Gradio & Plotly | All visualizations guaranteed working

-
- """) - - return demo + return demo -# ============================================================================ -# MAIN ENTRY POINT -# ============================================================================ +# =========================================== +# APPLICATION ENTRY POINT +# =========================================== def main(): - """Main entry point""" - logging.basicConfig(level=logging.INFO) - logger = logging.getLogger(__name__) - + """Main application entry point""" logger.info("=" * 80) logger.info("🚀 Starting ARF Ultimate Investor Demo v3.4.0") logger.info("=" * 80) - demo = create_enhanced_demo() + if OSS_AVAILABLE: + logger.info("✅ Agentic Reliability Framework v3.3.6 (OSS Edition)") + logger.info("📦 HealingIntent & OSSMCPClient available (advisory-only)") + logger.info("✓ HealingIntent instantiation successful") + else: + logger.info("⚠️ OSS components not available - running in simulation mode") + + # Create and launch the application + app = ARFUltimateInvestorDemo() + demo = app.create_demo_interface() + demo.launch( server_name="0.0.0.0", server_port=7860, share=False, - show_error=True, - theme="soft", + debug=True ) if __name__ == "__main__":