"""
🚀 ARF Ultimate Investor Demo v3.8.0 - ENTERPRISE EDITION
With Audit Trail, Incident History, Memory Graph, and Enterprise Features
"""
import logging
import datetime
import random
import uuid
import json
import tempfile
from typing import Dict, List, Optional, Any, Tuple
from collections import deque
import gradio as gr
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from plotly.subplots import make_subplots
# Import ARF OSS if available
try:
from agentic_reliability_framework.arf_core.models.healing_intent import (
HealingIntent,
create_scale_out_intent
)
from agentic_reliability_framework.arf_core.engine.simple_mcp_client import OSSMCPClient
ARF_OSS_AVAILABLE = True
except ImportError:
ARF_OSS_AVAILABLE = False
# Mock classes for demo
class HealingIntent:
def __init__(self, **kwargs):
self.intent_type = kwargs.get("intent_type", "scale_out")
self.parameters = kwargs.get("parameters", {})
def to_dict(self) -> Dict[str, Any]:
return {
"intent_type": self.intent_type,
"parameters": self.parameters,
"created_at": datetime.datetime.now().isoformat()
}
def create_scale_out_intent(resource_type: str, scale_factor: float = 2.0) -> HealingIntent:
return HealingIntent(
intent_type="scale_out",
parameters={
"resource_type": resource_type,
"scale_factor": scale_factor,
"action": "Increase capacity"
}
)
class OSSMCPClient:
def analyze_incident(self, metrics: Dict, pattern: str = "") -> Dict[str, Any]:
return {
"status": "analysis_complete",
"recommendations": [
"Increase resource allocation",
"Implement monitoring",
"Add circuit breakers",
"Optimize configuration"
],
"confidence": 0.92
}
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# ===========================================
# COMPREHENSIVE DATA
# ===========================================
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": "✅ 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": [
"✅ Auto-scaled Redis cluster: 4GB → 8GB",
"✅ Deployed intelligent cache warming service",
"✅ Optimized 12 key patterns with ML recommendations",
"✅ Implemented circuit breaker with 95% success rate",
"✅ Validated recovery with automated testing"
],
"metrics_improvement": {
"Cache Hit Rate": "18.5% → 72%",
"Response Time": "1850ms → 450ms",
"Database Load": "92% → 45%",
"Throughput": "1250 → 2450 req/sec"
},
"business_impact": {
"Recovery Time": "60 min → 12 min",
"Cost Saved": "$7,200",
"Users Impacted": "45,000 → 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%"
}
},
"API Rate Limit Exceeded": {
"description": "Global API rate limit exceeded causing 429 errors for external clients",
"severity": "MEDIUM",
"metrics": {
"429 Error Rate": "42.5%",
"Successful Requests": "58.3%",
"API Latency": "120ms",
"Queue Depth": "1250",
"Client Satisfaction": "65/100"
},
"impact": {
"Revenue Loss": "$1,800/hour",
"Affected Partners": "8",
"Partner SLA Violations": "3",
"Business Impact": "Medium"
}
},
"Microservice Cascading Failure": {
"description": "Order service failure causing cascading failures in dependent services",
"severity": "CRITICAL",
"metrics": {
"Order Failure Rate": "68.2%",
"Circuit Breakers Open": "4",
"Retry Storm Intensity": "425",
"Error Propagation": "85%",
"System Stability": "15/100"
},
"impact": {
"Revenue Loss": "$25,000/hour",
"Abandoned Carts": "12,500",
"Affected Users": "75,000",
"Brand Damage": "High"
}
}
}
# ===========================================
# AUDIT TRAIL & HISTORY MANAGEMENT
# ===========================================
class AuditTrailManager:
"""Manage audit trail and execution history"""
def __init__(self) -> None:
self.execution_history = deque(maxlen=50)
self.incident_history = deque(maxlen=100)
self._initialize_sample_data()
def _initialize_sample_data(self) -> None:
"""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, "✅ Executed", "Auto-scaled cache"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=75),
"Memory Leak", 3, 5200, "✅ Executed", "Fixed memory leak"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=60),
"API Rate Limit", 4, 2800, "✅ Executed", "Increased rate limits"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=45),
"DB Connection Pool", 4, 3800, "✅ Executed", "Scaled connection pool"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=30),
"Cascading Failure", 5, 12500, "✅ Executed", "Isolated services"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=15),
"Cache Miss Storm", 4, 7200, "✅ Executed", "Optimized cache"
)
]
for execution in sample_executions:
self.execution_history.append(execution)
# Sample incident history
services = ["API Gateway", "Database", "Cache", "Auth Service", "Payment Service",
"Order Service", "User Service", "Session Service"]
for _ in range(25):
incident_time = base_time - datetime.timedelta(minutes=random.randint(5, 120))
self.incident_history.append({
"timestamp": incident_time,
"time_str": incident_time.strftime("%H:%M"),
"service": random.choice(services),
"type": random.choice(list(INCIDENT_SCENARIOS.keys())),
"severity": random.randint(1, 3),
"description": f"{random.choice(['High latency', 'Connection failed', 'Memory spike', 'Timeout'])} on {random.choice(services)}",
"id": str(uuid.uuid4())[:8]
})
def _create_execution_entry(self, timestamp: datetime.datetime, scenario: str,
actions: int, savings: int, status: str, details: str) -> Dict[str, Any]:
"""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": str(uuid.uuid4())[:8]
}
def add_execution(self, scenario: str, actions: List[str],
savings: int, approval_required: bool, details: str = "") -> Dict[str, Any]:
"""Add new execution to history"""
entry = self._create_execution_entry(
datetime.datetime.now(),
scenario,
len(actions),
savings,
"✅ Approved & Executed" if approval_required else "✅ Auto-Executed",
details
)
self.execution_history.appendleft(entry) # Newest first
return entry
def add_incident(self, scenario_name: str, metrics: Dict) -> Dict[str, Any]:
"""Add incident to history"""
severity = 2 if "MEDIUM" in INCIDENT_SCENARIOS.get(scenario_name, {}).get("severity", "") else 3
entry = {
"timestamp": datetime.datetime.now(),
"time_str": datetime.datetime.now().strftime("%H:%M"),
"service": "Demo System",
"type": scenario_name,
"severity": severity,
"description": f"Demo incident: {scenario_name}",
"id": str(uuid.uuid4())[:8]
}
self.incident_history.appendleft(entry)
return entry
def get_execution_history_table(self, limit: int = 10) -> List[List[str]]:
"""Get execution history for table display"""
return [
[entry["time_str"], entry["scenario"], entry["actions"],
entry["status"], entry["savings"], entry["details"]]
for entry in list(self.execution_history)[:limit]
]
def get_incident_history_table(self, limit: int = 15) -> List[List[str]]:
"""Get incident history for table display"""
return [
[entry["time_str"], entry["service"], entry["type"],
f"{entry['severity']}/3", entry["description"]]
for entry in list(self.incident_history)[:limit]
]
def clear_history(self) -> Tuple[List[List[str]], List[List[str]]]:
"""Clear all history"""
self.execution_history.clear()
self.incident_history.clear()
self._initialize_sample_data() # Restore sample data
return self.get_execution_history_table(), self.get_incident_history_table()
def export_audit_trail(self) -> str:
"""Export audit trail as JSON"""
total_savings = 0
for e in self.execution_history:
if "$" in e["savings"]:
try:
total_savings += int(e["savings"].replace("$", "").replace(",", ""))
except ValueError:
continue
return json.dumps({
"executions": list(self.execution_history),
"incidents": list(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
}, indent=2, default=str)
# ===========================================
# ENHANCED VISUALIZATION ENGINE
# ===========================================
class EnhancedVisualizationEngine:
"""Enhanced visualization engine with memory graph support"""
@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": "📉 Cache hit rate drops to 18.5%", "type": "problem"},
{"time": now - datetime.timedelta(minutes=22), "event": "⚠️ Alert: Database load hits 92%", "type": "alert"},
{"time": now - datetime.timedelta(minutes=20), "event": "🤖 ARF detects pattern", "type": "detection"},
{"time": now - datetime.timedelta(minutes=18), "event": "🧠 Analysis: Cache Miss Storm identified", "type": "analysis"},
{"time": now - datetime.timedelta(minutes=15), "event": "⚡ Healing actions executed", "type": "action"},
{"time": now - datetime.timedelta(minutes=12), "event": "✅ Cache hit rate recovers to 72%", "type": "recovery"},
{"time": now - datetime.timedelta(minutes=10), "event": "📊 System stabilized", "type": "stable"}
]
color_map = {
"problem": "red", "alert": "orange", "detection": "blue",
"analysis": "purple", "action": "green", "recovery": "lightgreen",
"stable": "darkgreen"
}
for event in events:
fig.add_trace(go.Scatter(
x=[event["time"]],
y=[1],
mode='markers+text',
marker=dict(
size=15,
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",
name=event["type"].capitalize(),
hovertemplate="%{text}
%{x|%H:%M:%S}"
))
fig.update_layout(
title="Incident Timeline - Cache Miss Storm Resolution",
xaxis_title="Time →",
yaxis_title="Event Type",
height=450,
showlegend=True,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
hovermode='closest',
xaxis=dict(
tickformat='%H:%M',
gridcolor='rgba(200,200,200,0.2)'
),
yaxis=dict(
showticklabels=False,
gridcolor='rgba(200,200,200,0.1)'
)
)
return fig
@staticmethod
def create_business_dashboard() -> go.Figure:
"""Create executive business dashboard"""
fig = make_subplots(
rows=2, cols=2,
subplot_titles=('Annual Cost Impact', 'Team Capacity Shift',
'MTTR Comparison', 'ROI Analysis'),
vertical_spacing=0.15,
horizontal_spacing=0.15
)
# 1. Cost Impact
categories = ['Without ARF', 'With ARF Enterprise', 'Net Savings']
values = [2960000, 1000000, 1960000]
fig.add_trace(
go.Bar(
x=categories,
y=values,
marker_color=['#FF6B6B', '#4ECDC4', '#45B7D1'],
text=[f'${v/1000000:.1f}M' for v in values],
textposition='auto',
name='Cost Impact'
),
row=1, col=1
)
# 2. Team Capacity Shift
labels = ['Firefighting', 'Innovation', 'Strategic Work']
before = [60, 20, 20]
after = [10, 60, 30]
fig.add_trace(
go.Bar(
x=labels,
y=before,
name='Before ARF',
marker_color='#FF6B6B'
),
row=1, col=2
)
fig.add_trace(
go.Bar(
x=labels,
y=after,
name='After ARF Enterprise',
marker_color='#4ECDC4'
),
row=1, col=2
)
# 3. MTTR Comparison
mttr_categories = ['Manual', 'Traditional', 'ARF OSS', 'ARF Enterprise']
mttr_values = [120, 45, 25, 8]
fig.add_trace(
go.Bar(
x=mttr_categories,
y=mttr_values,
marker_color=['#FF6B6B', '#FFE66D', '#45B7D1', '#4ECDC4'],
text=[f'{v} min' for v in mttr_values],
textposition='auto',
name='MTTR'
),
row=2, col=1
)
# 4. ROI Gauge
fig.add_trace(
go.Indicator(
mode="gauge+number+delta",
value=5.2,
title={'text': "ROI Multiplier"},
delta={'reference': 1.0, 'increasing': {'color': "green"}},
gauge={
'axis': {'range': [0, 10], 'tickwidth': 1},
'bar': {'color': "#4ECDC4"},
'steps': [
{'range': [0, 2], 'color': "lightgray"},
{'range': [2, 4], 'color': "gray"},
{'range': [4, 6], 'color': "lightgreen"},
{'range': [6, 10], 'color': "green"}
],
'threshold': {
'line': {'color': "red", 'width': 4},
'thickness': 0.75,
'value': 5.2
}
}
),
row=2, col=2
)
fig.update_layout(
height=700,
showlegend=True,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
title_text="Executive Business Dashboard",
barmode='group'
)
return fig
@staticmethod
def create_execution_history_chart(audit_manager: AuditTrailManager) -> go.Figure:
"""Create execution history visualization"""
executions = list(audit_manager.execution_history)[:10] # Last 10 executions
if not executions:
fig = go.Figure()
fig.update_layout(
title="No execution history yet",
height=400,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'
)
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 ValueError:
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='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
showlegend=False
)
return fig
@staticmethod
def create_memory_graph(audit_manager: AuditTrailManager, graph_type: str = "Force Directed",
show_weights: bool = True, auto_layout: bool = True) -> go.Figure:
"""Create interactive memory graph visualization"""
fig = go.Figure()
# Get incidents from history
incidents = list(audit_manager.incident_history)[:20] # Last 20 incidents
if not incidents:
# Create sample graph
nodes = [
{"id": "Incident_1", "label": "Cache Miss", "type": "incident", "size": 20},
{"id": "Action_1", "label": "Scale Cache", "type": "action", "size": 15},
{"id": "Outcome_1", "label": "Resolved", "type": "outcome", "size": 15},
{"id": "Component_1", "label": "Redis", "type": "component", "size": 18},
]
edges = [
{"source": "Incident_1", "target": "Action_1", "weight": 0.9, "label": "resolved_by"},
{"source": "Action_1", "target": "Outcome_1", "weight": 1.0, "label": "leads_to"},
{"source": "Incident_1", "target": "Component_1", "weight": 0.8, "label": "affects"},
]
else:
# Create nodes from actual incidents
nodes = []
edges = []
for i, incident in enumerate(incidents):
node_id = f"Incident_{i}"
nodes.append({
"id": node_id,
"label": incident["type"][:20],
"type": "incident",
"size": 15 + (incident.get("severity", 2) * 5),
"severity": incident.get("severity", 2)
})
# Create edges to previous incidents
if i > 0:
prev_id = f"Incident_{i-1}"
edges.append({
"source": prev_id,
"target": node_id,
"weight": 0.7,
"label": "related_to"
})
# Color mapping
color_map = {
"incident": "#FF6B6B",
"action": "#4ECDC4",
"outcome": "#45B7D1",
"component": "#96CEB4"
}
# Add nodes
node_x = []
node_y = []
node_text = []
node_color = []
node_size = []
for i, node in enumerate(nodes):
# Simple layout - could be enhanced with networkx
angle = 2 * np.pi * i / len(nodes)
radius = 1.0
node_x.append(radius * np.cos(angle))
node_y.append(radius * np.sin(angle))
node_text.append(f"{node['label']}
Type: {node['type']}")
node_color.append(color_map.get(node["type"], "#999999"))
node_size.append(node.get("size", 15))
fig.add_trace(go.Scatter(
x=node_x,
y=node_y,
mode='markers+text',
marker=dict(
size=node_size,
color=node_color,
line=dict(width=2, color='white')
),
text=[node["label"] for node in nodes],
textposition="top center",
hovertext=node_text,
hoverinfo="text",
name="Nodes"
))
# Add edges
for edge in edges:
try:
source_idx = next(i for i, n in enumerate(nodes) if n["id"] == edge["source"])
target_idx = next(i for i, n in enumerate(nodes) if n["id"] == edge["target"])
fig.add_trace(go.Scatter(
x=[node_x[source_idx], node_x[target_idx], None],
y=[node_y[source_idx], node_y[target_idx], None],
mode='lines',
line=dict(
width=2 * edge.get("weight", 1.0),
color='rgba(100, 100, 100, 0.5)'
),
hoverinfo='none',
showlegend=False
))
except StopIteration:
continue
fig.update_layout(
title="Incident Memory Graph",
showlegend=True,
height=600,
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)',
hovermode='closest',
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
@staticmethod
def create_pattern_analysis_chart(analysis_data: Dict[str, Any]) -> go.Figure:
"""Create pattern analysis visualization"""
fig = make_subplots(
rows=2, cols=2,
subplot_titles=('Incident Frequency', 'Resolution Times',
'Success Rates', 'Pattern Correlation'),
vertical_spacing=0.15
)
# Sample data - in real app this would come from analysis
patterns = ['Cache Issues', 'DB Connections', 'Memory Leaks', 'API Limits', 'Cascading']
frequencies = [12, 8, 5, 7, 3]
resolution_times = [8.2, 15.5, 45.2, 5.1, 32.8]
success_rates = [92, 85, 78, 96, 65]
# Incident Frequency
fig.add_trace(
go.Bar(x=patterns, y=frequencies, name='Frequency'),
row=1, col=1
)
# Resolution Times
fig.add_trace(
go.Bar(x=patterns, y=resolution_times, name='Resolution Time (min)'),
row=1, col=2
)
# Success Rates
fig.add_trace(
go.Bar(x=patterns, y=success_rates, name='Success Rate %'),
row=2, col=1
)
# Correlation Matrix
corr_matrix = np.array([
[1.0, 0.3, 0.1, 0.2, 0.05],
[0.3, 1.0, 0.4, 0.1, 0.25],
[0.1, 0.4, 1.0, 0.05, 0.6],
[0.2, 0.1, 0.05, 1.0, 0.1],
[0.05, 0.25, 0.6, 0.1, 1.0]
])
fig.add_trace(
go.Heatmap(z=corr_matrix, x=patterns, y=patterns),
row=2, col=2
)
fig.update_layout(
height=700,
showlegend=False,
title_text="Pattern Analysis Dashboard"
)
return fig
# ===========================================
# ENHANCED BUSINESS LOGIC
# ===========================================
class EnhancedBusinessLogic:
"""Enhanced business logic with enterprise features"""
def __init__(self, audit_manager: AuditTrailManager):
self.audit_manager = audit_manager
self.viz_engine = EnhancedVisualizationEngine()
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": "✅ Active"
}
self.mcp_mode = "approval"
self.learning_stats = {
"total_incidents": 127,
"resolved_automatically": 89,
"average_resolution_time": "8.2 min",
"success_rate": "92.1%",
"patterns_detected": 24,
"confidence_threshold": 0.85,
"memory_size": "4.7 MB",
"embeddings": 127,
"graph_nodes": 89,
"graph_edges": 245
}
def run_oss_analysis(self, scenario_name: str) -> Dict[str, Any]:
"""Run OSS analysis"""
scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
analysis = scenario.get("oss_analysis", {})
if not analysis:
analysis = {
"status": "✅ Analysis Complete",
"recommendations": [
"Increase resource allocation",
"Implement monitoring",
"Add circuit breakers",
"Optimize configuration"
],
"estimated_time": "45-60 minutes",
"engineers_needed": "2-3",
"manual_effort": "Required",
"total_cost": "$3,000 - $8,000"
}
# Add ARF context
analysis["arf_context"] = {
"oss_available": ARF_OSS_AVAILABLE,
"version": "3.3.6",
"mode": "advisory_only",
"healing_intent": True
}
# Add to incident history
self.audit_manager.add_incident(scenario_name, scenario.get("metrics", {}))
return analysis
def execute_enterprise_healing(self, scenario_name: str, approval_required: bool) -> Tuple[Any, ...]:
"""Execute enterprise healing"""
scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
results = scenario.get("enterprise_results", {})
# Use default results if not available
if not results:
results = {
"actions_completed": [
"✅ Auto-scaled resources based on ARF healing intent",
"✅ Implemented optimization recommendations",
"✅ Deployed monitoring and alerting",
"✅ Validated recovery with automated testing"
],
"metrics_improvement": {
"Performance": "Dramatically improved",
"Stability": "Restored",
"Recovery": "Complete"
},
"business_impact": {
"Recovery Time": f"60 min → {random.randint(5, 15)} min",
"Cost Saved": f"${random.randint(2000, 10000):,}",
"Users Impacted": "45,000 → 0",
"Revenue Protected": f"${random.randint(1000, 5000):,}"
}
}
# Calculate savings
savings = 0
if "Cost Saved" in results["business_impact"]:
try:
savings_str = results["business_impact"]["Cost Saved"]
savings = int(''.join(filter(str.isdigit, savings_str)))
except (ValueError, TypeError):
savings = random.randint(2000, 10000)
# Update status
if approval_required:
results["status"] = "✅ Approved and Executed"
approval_html = self._create_approval_html(scenario_name, True)
else:
results["status"] = "✅ Auto-Executed"
approval_html = self._create_approval_html(scenario_name, False)
# Add to audit trail
details = f"{len(results['actions_completed'])} actions executed"
self.audit_manager.add_execution(
scenario_name,
results["actions_completed"],
savings,
approval_required,
details
)
# Add enterprise context
results["enterprise_context"] = {
"approval_required": approval_required,
"compliance_mode": "strict",
"audit_trail": "created",
"learning_applied": True,
"roi_measured": True
}
# Update visualizations
execution_chart = self.viz_engine.create_execution_history_chart(self.audit_manager)
return (
approval_html,
{"approval_required": approval_required, "compliance_mode": "strict"},
results,
execution_chart,
self.audit_manager.get_execution_history_table(),
self.audit_manager.get_incident_history_table()
)
def _create_approval_html(self, scenario_name: str, approval_required: bool) -> str: