diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -2,6 +2,7 @@ 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 +FULLY CORRECTED VERSION - Integrated with all components """ import logging @@ -9,8 +10,10 @@ import sys import traceback import json import datetime +import asyncio +import time from pathlib import Path -from typing import Dict, List, Any, Optional, Tuple +from typing import Dict, List, Any, Optional, Tuple, Union # Configure logging logging.basicConfig( @@ -23,1658 +26,797 @@ logging.basicConfig( ) logger = logging.getLogger(__name__) -# Add parent directory to path for module imports +# Add parent directory to path sys.path.insert(0, str(Path(__file__).parent)) +# =========================================== +# ARF FRAMEWORK IMPORT - CORRECTED BASED ON PACKAGE STRUCTURE +# =========================================== +ARF_OSS_AVAILABLE = False +OSS_VERSION = "3.3.6" +HealingIntent = None +IntentSource = None +IntentStatus = None +OSSMCPClient = None + try: - # Import ARF OSS framework (actual package) + # ATTEMPT 1: Import from public package (correct based on pyproject.toml) 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 + from agentic_reliability_framework.models import HealingIntent, IntentSource, IntentStatus + from agentic_reliability_framework.engine import OSSMCPClient 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() + logger.info(f"āœ… Successfully imported ARF OSS v{OSS_VERSION} from public package") - except ImportError as e: - logger.warning(f"Failed to import ARF OSS: {e}") - ARF_OSS_AVAILABLE = False - OSS_VERSION = "3.3.6 (Mock)" + except ImportError as e1: + logger.debug(f"Attempt 1 failed: {e1}") - # 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') + # ATTEMPT 2: Import from modules (development structure) + try: + # For Hugging Face demo, use enhanced mock classes + logger.info("Using enhanced mock classes for demo environment") + + # Define enums matching the actual structure + class IntentSource: + OSS_ANALYSIS = "oss_analysis" + RAG_SIMILARITY = "rag_similarity" + HUMAN_OVERRIDE = "human_override" + AUTOMATED_LEARNING = "automated_learning" - 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 - } - } + class IntentStatus: + CREATED = "created" + OSS_ADVISORY_ONLY = "oss_advisory_only" + PENDING_EXECUTION = "pending_execution" + EXECUTING = "executing" + COMPLETED = "completed" + FAILED = "failed" - def mark_as_oss_advisory(self): - return self - - class OSSMCPClient: - def __init__(self): - self.mode = "advisory" + # Enhanced HealingIntent based on actual structure + 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') + self.source = kwargs.get('source', IntentSource.OSS_ANALYSIS) + self.status = kwargs.get('status', IntentStatus.CREATED) + self.intent_id = kwargs.get('intent_id', f"intent_{int(time.time())}") + self.oss_edition = "oss" + self.requires_enterprise = True + self.execution_allowed = False + self.created_at = time.time() + + def to_enterprise_request(self) -> Dict: + return { + 'action': self.action, + 'component': self.component, + 'parameters': self.parameters, + 'justification': self.justification, + 'confidence': self.confidence, + 'requires_enterprise': self.requires_enterprise, + 'oss_metadata': { + 'similar_incidents_count': len(self.similar_incidents), + 'rag_used': self.rag_similarity_score is not None, + 'source': self.source, + 'oss_edition': self.oss_edition, + 'execution_allowed': self.execution_allowed + }, + 'intent_id': self.intent_id, + 'created_at': self.created_at + } - 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"} - ] + def mark_as_oss_advisory(self): + self.status = IntentStatus.OSS_ADVISORY_ONLY + self.execution_allowed = False + return self - 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' - }) + @classmethod + def from_analysis(cls, action: str, component: str, parameters: Dict, + justification: str, confidence: float, **kwargs): + return cls( + action=action, + component=component, + parameters=parameters, + justification=justification, + confidence=confidence, + similar_incidents=kwargs.get('similar_incidents'), + rag_similarity_score=kwargs.get('rag_similarity_score'), + source=kwargs.get('source', IntentSource.OSS_ANALYSIS) + ) + + 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: + similar_incidents = [ + {"id": "inc_001", "similarity": 0.78, "resolution": "scaled_out", + "component": "redis", "success": True, "timestamp": "2024-01-15T10:30:00"}, + {"id": "inc_045", "similarity": 0.65, "resolution": "restarted", + "component": "database", "success": True, "timestamp": "2024-01-10T14:20:00"}, + {"id": "inc_089", "similarity": 0.59, "resolution": "circuit_breaker", + "component": "api", "success": False, "timestamp": "2024-01-05T09:15:00"} + ] + + rag_score = sum(inc["similarity"] for inc in similar_incidents[:3]) / 3 if similar_incidents else 0.67 + + return HealingIntent.from_analysis( + action=tool_name, + component=component, + parameters=parameters, + justification=f"OSS Analysis: Based on {len(similar_incidents)} similar historical incidents with {rag_score:.0%} average similarity", + confidence=0.82 + (len(similar_incidents) * 0.01), + similar_incidents=similar_incidents, + rag_similarity_score=rag_score, + source=IntentSource.RAG_SIMILARITY + ).mark_as_oss_advisory() + + # Set module-level variables + IntentSource = IntentSource + IntentStatus = IntentStatus + OSSMCPClient = OSSMCPClient + logger.info("āœ… Using enhanced mock classes for demo") + + except Exception as e2: + logger.warning(f"Enhanced mock creation failed: {e2}") + + # Minimal fallback + class HealingIntent: + def __init__(self, **kwargs): + pass + def to_enterprise_request(self): + return {"error": "ARF not available"} + @classmethod + def from_analysis(cls, **kwargs): + return cls() + def mark_as_oss_advisory(self): + return self + + class OSSMCPClient: + def __init__(self): + self.mode = "advisory" + async def analyze_and_recommend(self, **kwargs): + return HealingIntent() + +except Exception as e: + logger.error(f"āŒ CRITICAL: Failed to initialize ARF framework: {e}") + logger.error(traceback.format_exc()) + # Provide absolute minimal fallbacks + class HealingIntent: + def __init__(self, **kwargs): + pass + def to_enterprise_request(self): + return {"error": "ARF not available"} + @classmethod + def from_analysis(cls, **kwargs): + return cls() + class OSSMCPClient: + async def analyze_and_recommend(self, **kwargs): + return HealingIntent() + +# =========================================== +# IMPORT SUPPORTING MODULES +# =========================================== +try: + # Import UI components + from ui.components import ( + create_header, create_status_bar, create_tab1_incident_demo, + create_tab2_business_roi, create_tab3_audit_trail, + create_tab4_enterprise_features, create_tab5_learning_engine, + create_footer + ) - # 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 + # Import demo orchestrator + from demo.orchestrator import DemoOrchestrator - # =========================================== - # COMPREHENSIVE DATA MODELS - # =========================================== + # Import visualizations + from core.visualizations import EnhancedVisualizationEngine - 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}" + logger.info("āœ… Successfully imported all supporting modules") + +except ImportError as e: + logger.warning(f"Could not import some modules: {e}") + # We'll define minimal versions inline if needed + +# =========================================== +# INCIDENT SCENARIOS (from original app.py) +# =========================================== +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%" + }, + "component": "redis_cache", + "oss_analysis": { + "status": "āœ… ARF OSS Analysis Complete", + "recommendations": [ + "Increase Redis cache memory allocation", + "Implement cache warming strategy", + "Optimize key patterns", + "Add circuit breaker for database fallback" + ], + "estimated_time": "60+ minutes", + "engineers_needed": "2-3 SREs + 1 DBA", + "manual_effort": "High", + "total_cost": "$8,500" + }, + "enterprise_results": { + "actions_completed": [ + "āœ… Auto-scaled Redis cluster: 4GB ā†’ 8GB", + "āœ… Deployed intelligent cache warming", + "āœ… Optimized 12 key patterns with ML", + "āœ… Implemented circuit breaker" + ], + "metrics_improvement": { + "Cache Hit Rate": "18.5% ā†’ 72%", + "Response Time": "1850ms ā†’ 450ms", + "Database Load": "92% ā†’ 45%" + }, + "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" + }, + "component": "database" + }, + "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%" + }, + "component": "java_service" + } +} + +# =========================================== +# AUDIT TRAIL MANAGER (from original app.py) +# =========================================== +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) - 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 + 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" + ), + ] - 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] - ] + self.execution_history = sample_executions - 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] - ] + services = ["API Gateway", "Database", "Redis Cache", "Auth Service"] - 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 "$" in e["savings"] - ) - - return json.dumps({ - "executions": self.execution_history, - "incidents": self.incident_history, - "exported_at": datetime.datetime.now().isoformat(), - "total_executions": len(self.execution_history), - "total_incidents": len(self.incident_history), - "total_savings": total_savings, - "arf_version": OSS_VERSION, - "oss_available": ARF_OSS_AVAILABLE - }, indent=2, default=str) + for i in range(8): + incident_time = base_time - datetime.timedelta(minutes=i * 15) + self.incident_history.append({ + "timestamp": incident_time, + "time_str": incident_time.strftime("%H:%M"), + "service": services[i % len(services)], + "type": "Cache Miss Storm" if i % 3 == 0 else "Memory Leak", + "severity": 3 if i % 3 == 0 else 2, + "description": f"High latency on {services[i % len(services)]}", + "id": f"inc_{i:03d}" + }) - # =========================================== - # INCIDENT SCENARIOS - # =========================================== + def _create_execution_entry(self, timestamp, scenario, actions, savings, status, details): + return { + "timestamp": timestamp, + "time_str": timestamp.strftime("%H:%M"), + "scenario": scenario, + "actions": str(actions), + "savings": f"${savings:,}", + "status": status, + "details": details, + "id": f"exec_{len(self.execution_history):03d}" + } - 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": "$5,500/hour", - "Session Loss": "8,500 users", - "Customer Impact": "High", - "Support Tickets": "+300%" - } + def add_execution(self, scenario: str, actions: List[str], + savings: int, approval_required: bool, details: str = ""): + entry = self._create_execution_entry( + datetime.datetime.now(), + scenario, + len(actions), + savings, + "āœ… Approved & Executed" if approval_required else "āœ… Auto-Executed", + details + ) + self.execution_history.insert(0, entry) + return entry + + def add_incident(self, scenario_name: str, metrics: Dict): + entry = { + "timestamp": datetime.datetime.now(), + "time_str": datetime.datetime.now().strftime("%H:%M"), + "service": "Demo System", + "type": scenario_name, + "severity": 3, + "description": f"Demo incident: {scenario_name}", + "id": f"inc_{len(self.incident_history):03d}" } - } + self.incident_history.insert(0, entry) + return entry + + def get_execution_history_table(self, limit: int = 10) -> List[List]: + return [ + [entry["time_str"], entry["scenario"], entry["actions"], + entry["status"], entry["savings"], entry["details"]] + for entry in self.execution_history[:limit] + ] - # =========================================== - # BUSINESS LOGIC - # =========================================== + def get_incident_history_table(self, limit: int = 15) -> List[List]: + return [ + [entry["time_str"], entry["service"], entry["type"], + f"{entry['severity']}/3", entry["description"]] + for entry in self.incident_history[:limit] + ] - class BusinessLogic: - """Business logic for the demo""" + def export_audit_trail(self) -> str: + total_savings = sum( + int(e["savings"].replace("$", "").replace(",", "")) + for e in self.execution_history + if "$" in e["savings"] + ) - 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" - } + 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) + +# =========================================== +# CREATE DEMO INTERFACE - CORRECTED +# =========================================== +def create_demo_interface(): + """Create the 5-tab demo interface - CORRECTED VERSION""" + + # Import gradio here to avoid issues + import gradio as gr + + # Initialize components + audit_manager = AuditTrailManager() + + # Create OSS client + oss_client = OSSMCPClient() if OSSMCPClient else None + + # Create orchestrator + orchestrator = DemoOrchestrator(arf_client=oss_client) + + # Create visualization engine + try: + viz_engine = EnhancedVisualizationEngine() + except: + # Fallback to simple visualizations + class SimpleVizEngine: + def create_interactive_timeline(self, scenario): + import plotly.graph_objects as go + fig = go.Figure() + fig.add_trace(go.Scatter(x=[1,2,3], y=[1,2,1])) + fig.update_layout(title="Timeline", height=400) + return fig + def create_executive_dashboard(self, roi=None): + import plotly.graph_objects as go + fig = go.Figure() + fig.add_trace(go.Bar(x=['A','B','C'], y=[1,2,3])) + fig.update_layout(title="Dashboard", height=400) + return fig + viz_engine = SimpleVizEngine() + + # 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; } + .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; + } + """ + + with gr.Blocks( + title=f"šŸš€ ARF Investor Demo v3.8.0", + theme=gr.themes.Soft( + primary_hue="blue", + secondary_hue="teal" + ), + css=custom_css + ) as demo: - async def run_oss_analysis(self, scenario_name: str) -> Dict: - """Run OSS analysis using actual ARF framework""" - scenario = INCIDENT_SCENARIOS.get(scenario_name, {}) + # ============ HEADER ============ + create_header(OSS_VERSION, ARF_OSS_AVAILABLE) + + # ============ STATUS BAR ============ + create_status_bar() + + # ============ 5 TABS ============ + with gr.Tabs(): + + # TAB 1: LIVE INCIDENT DEMO + with gr.TabItem("šŸ”„ Live Incident Demo"): + # Get components from UI module + (scenario_dropdown, scenario_description, metrics_display, impact_display, + timeline_output, oss_btn, enterprise_btn, approval_toggle, demo_btn, + approval_display, config_display, results_display) = create_tab1_incident_demo( + INCIDENT_SCENARIOS, "Cache Miss Storm" + ) - # 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") - } + # TAB 2: BUSINESS IMPACT & ROI + with gr.TabItem("šŸ’° Business Impact & ROI"): + (dashboard_output, monthly_slider, impact_slider, team_slider, + calculate_btn, roi_output) = create_tab2_business_roi() + + # TAB 3: AUDIT TRAIL & HISTORY + with gr.TabItem("šŸ“œ Audit Trail & History"): + (refresh_btn, clear_btn, export_btn, execution_table, savings_chart, + incident_table, memory_graph, export_text) = create_tab3_audit_trail() + + # TAB 4: ENTERPRISE FEATURES + with gr.TabItem("šŸ¢ Enterprise Features"): + (license_display, validate_btn, trial_btn, upgrade_btn, features_table, + compliance_display, integrations_table, mcp_mode) = create_tab4_enterprise_features() + + # TAB 5: LEARNING ENGINE + with gr.TabItem("šŸ§  Learning Engine"): + (learning_graph, graph_type, show_labels, search_query, search_btn, + clear_btn_search, search_results, stats_display, patterns_display, + performance_display) = create_tab5_learning_engine() + + # ============ FOOTER ============ + create_footer() + + # ============ EVENT HANDLERS ============ + + # Scenario dropdown change + def update_scenario(scenario_name): + scenario = INCIDENT_SCENARIOS.get(scenario_name, {}) + timeline = viz_engine.create_interactive_timeline(scenario) + return ( + f"### {scenario_name}\n{scenario.get('description', 'No description')}", + scenario.get("metrics", {}), + scenario.get("impact", {}), + 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): + scenario = INCIDENT_SCENARIOS.get(scenario_name, {}) + analysis = await orchestrator.analyze_incident(scenario_name, scenario) - # 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 audit trail + audit_manager.add_incident(scenario_name, scenario.get("metrics", {})) - # Add to incident history - self.audit_manager.add_incident(scenario_name, scenario.get("metrics", {})) + # Update tables + incident_table_data = audit_manager.get_incident_history_table() - return analysis + return analysis, incident_table_data - def execute_enterprise_healing(self, scenario_name: str, approval_required: bool) -> Tuple[Dict, Dict]: - """Execute enterprise healing""" + oss_btn.click( + fn=run_oss_analysis, + inputs=[scenario_dropdown], + outputs=[results_display, incident_table] + ) + + # Enterprise Healing button + def execute_healing(scenario_name, approval_required): 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"] + # Create mock healing intent + healing_intent = { + "action": "scale_out", + "component": scenario.get("component", "unknown"), + "parameters": {"scale_factor": 2}, + "justification": f"Healing {scenario_name}" } - # Calculate savings - savings = 7200 if scenario_name == "Cache Miss Storm" else 4200 + # Execute through orchestrator + execution = orchestrator.execute_healing( + scenario_name, + healing_intent, + mode="approval" if approval_required else "autonomous" + ) # Add to audit trail - self.audit_manager.add_execution( + audit_manager.add_execution( scenario_name, - results.get("actions_completed", []), - savings, + ["scale_out", "circuit_breaker", "monitoring"], + 7200, approval_required, - f"Healed {scenario_name} incident" + f"Healed {scenario_name}" ) # 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""" -
+ approval_html = """ +
-
- šŸ›”ļø -
-

Approval Required

-
-
-
-
Action: Scale resources for {scenario_name}
-
Risk Level: Low
-
Blast Radius: Limited to affected service
-
Auto-rollback: Available
+
+ āœ…
+

Approved & Executed

-
-
-
Status
-
āœ… Approved & Executed
-
-
- {datetime.datetime.now().strftime("%H:%M:%S")} -
+
+ Action approved by system administrator and executed successfully.
""" else: - return f""" -
+ approval_html = """ +
-
+
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")} -
+
+ Action executed autonomously by ARF Enterprise.
""" - - 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 - )) + # Update execution table + execution_table_data = audit_manager.get_execution_history_table() - # 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 + return approval_html, execution, execution_table_data - @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 + enterprise_btn.click( + fn=execute_healing, + inputs=[scenario_dropdown, approval_toggle], + outputs=[approval_display, results_display, execution_table] + ) - @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) + # Quick Demo button + async def run_quick_demo(): + # Run OSS analysis + scenario = INCIDENT_SCENARIOS["Cache Miss Storm"] + analysis = await orchestrator.analyze_incident("Cache Miss Storm", scenario) + + # Execute healing + execution = orchestrator.execute_healing( + "Cache Miss Storm", + {"action": "scale_out", "component": "redis_cache"}, + mode="autonomous" ) - 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) + # Update audit trail + audit_manager.add_incident("Cache Miss Storm", scenario.get("metrics", {})) + audit_manager.add_execution( + "Cache Miss Storm", + ["scale_out", "circuit_breaker"], + 7200, + False, + "Quick demo execution" ) - 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: + # Update all tables + execution_table_data = audit_manager.get_execution_history_table() + incident_table_data = audit_manager.get_incident_history_table() - # ============ 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 + # Create approval HTML + approval_html = """ +
+
+
+ āš”
+

Quick Demo Completed

- -
- Experience the full journey from OSS Advisory - to Enterprise Autonomous Healing. - See how ARF transforms reliability operations. +
+ OSS analysis ā†’ Enterprise execution completed successfully.
- """) - - # ============ 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] + """ + + return ( + analysis, + approval_html, + execution, + execution_table_data, + incident_table_data, + gr.Checkbox.update(value=False) ) + + demo_btn.click( + fn=run_quick_demo, + outputs=[ + results_display, + approval_display, + results_display, + execution_table, + incident_table, + approval_toggle + ] + ) + + # ROI Calculator + def calculate_roi(monthly, impact, team): + company_data = { + "monthly_incidents": monthly, + "avg_cost_per_incident": impact, + "team_size": team + } + roi_result = orchestrator.calculate_roi(company_data) + + # Format for display + formatted_result = { + "annual_impact": f"${roi_result['annual_impact']:,.0f}", + "team_cost": f"${roi_result['team_cost']:,.0f}", + "potential_savings": f"${roi_result['potential_savings']:,.0f}", + "roi_multiplier": f"{roi_result['roi_multiplier']:.1f}Ɨ", + "payback_months": f"{roi_result['payback_months']:.1f} months", + "recommendation": roi_result['recommendation'] + } - # 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] - ) + # Update dashboard with user-specific ROI + dashboard = viz_engine.create_executive_dashboard(roi_result) - # ============ 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 formatted_result, dashboard - 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 + calculate_btn.click( + fn=calculate_roi, + inputs=[monthly_slider, impact_slider, team_slider], + outputs=[roi_output, dashboard_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() + export_data = audit_manager.export_audit_trail() + return execution_table_data, incident_table_data, export_data - Failed to initialize the demo: + refresh_btn.click( + fn=refresh_audit_trail, + outputs=[execution_table, incident_table, 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, export_text] + ) + + # Export Audit Trail + def update_export(): + return audit_manager.export_audit_trail() + + export_btn.click( + fn=update_export, + outputs=[export_text] + ) + + # Search similar incidents + def search_incidents(query): + if not query.strip(): + return [] + results = orchestrator.get_similar_incidents(query) + return [[r["id"], f"{r['similarity']:.0%}", r["scenario"], r["resolution"]] + for r in results] + + search_btn.click( + fn=search_incidents, + inputs=[search_query], + outputs=[search_results] + ) - ```python - {str(e)} - ``` + clear_btn_search.click( + fn=lambda: [], + outputs=[search_results] + ) - Please check the logs for details. - """) + # Initialize dashboard on load + demo.load( + fn=lambda: viz_engine.create_executive_dashboard(), + outputs=[dashboard_output] + ) return demo - +# =========================================== +# MAIN EXECUTION - CORRECTED +# =========================================== def main(): - """Main entry point""" + """Main entry point - CORRECTED""" print("šŸš€ Starting ARF Ultimate Investor Demo v3.8.0...") print("=" * 70) print("šŸ“Š Features:") @@ -1685,18 +827,26 @@ def main(): 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...") + print(f"\nšŸ“¦ Using ARF OSS v{OSS_VERSION}") + print(f"šŸ”§ ARF Available: {ARF_OSS_AVAILABLE}") + print("šŸŒ Opening web interface...") + # Create and launch the demo interface demo = create_demo_interface() - demo.launch( - server_name="0.0.0.0", - server_port=7860, - share=False, - debug=False, - show_error=True - ) - + + # Launch configuration + launch_config = { + "server_name": "0.0.0.0", + "server_port": 7860, + "share": False, + "debug": False, + "show_error": True + } + + demo.launch(**launch_config) +# =========================================== +# EXECUTION GUARD - CORRECTED +# =========================================== if __name__ == "__main__": main() \ No newline at end of file