"""
đ 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": "â
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": [
"â
Auto-scaled Redis cluster: 4GB â 8GB",
"â
Deployed intelligent cache warming service",
"â
Optimized 12 key patterns with ML recommendations",
"â
Implemented circuit breaker with 95% success rate",
"â
Validated recovery with automated testing"
],
"metrics_improvement": {
"Cache Hit Rate": "18.5% â 72%",
"Response Time": "1850ms â 450ms",
"Database Load": "92% â 45%",
"Throughput": "1250 â 2450 req/sec"
},
"business_impact": {
"Recovery Time": "60 min â 12 min",
"Cost Saved": "$7,200",
"Users Impacted": "45,000 â 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, "â
Executed", "Auto-scaled cache"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=75),
"Memory Leak", 3, 5200, "â
Executed", "Fixed memory leak"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=60),
"API Rate Limit", 4, 2800, "â
Executed", "Increased rate limits"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=45),
"DB Connection Pool", 4, 3800, "â
Executed", "Scaled connection pool"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=30),
"Cascading Failure", 5, 12500, "â
Executed", "Isolated services"
),
self._create_execution_entry(
base_time - datetime.timedelta(minutes=15),
"Cache Miss Storm", 4, 7200, "â
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,
"â
Approved & Executed" if approval_required else "â
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": "đ Cache hit rate drops to 18.5%", "type": "problem"},
{"time": now - datetime.timedelta(minutes=22), "event": "â ī¸ Alert: Database load hits 92%", "type": "alert"},
{"time": now - datetime.timedelta(minutes=20), "event": "đ¤ ARF detects pattern", "type": "detection"},
{"time": now - datetime.timedelta(minutes=18), "event": "đ§ 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": "â
Cache hit rate recovers to 72%", "type": "recovery"},
{"time": now - datetime.timedelta(minutes=10), "event": "đ 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 â",
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": "â
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": "â
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 - $8,000"
}
# Add ARF context
analysis["arf_context"] = {
"oss_available": ARF_OSS_AVAILABLE,
"version": "3.3.6",
"mode": "advisory_only",
"healing_intent": True
}
# Add to incident history
self.audit_manager.add_incident(scenario_name, scenario.get("metrics", {}))
return analysis
def execute_enterprise_healing(self, scenario_name: str, approval_required: bool) -> Tuple[Any, ...]:
"""Execute enterprise healing"""
scenario = INCIDENT_SCENARIOS.get(scenario_name, {})
results = scenario.get("enterprise_results", {})
# Use default results if not available
if not results:
results = {
"actions_completed": [
"â
Auto-scaled resources based on ARF healing intent",
"â
Implemented optimization recommendations",
"â
Deployed monitoring and alerting",
"â
Validated recovery with automated testing"
],
"metrics_improvement": {
"Performance": "Dramatically improved",
"Stability": "Restored",
"Recovery": "Complete"
},
"business_impact": {
"Recovery Time": f"60 min â {random.randint(5, 15)} min",
"Cost Saved": f"${random.randint(2000, 10000):,}",
"Users Impacted": "45,000 â 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"] = "â
Approved and Executed"
approval_html = self._create_approval_html(scenario_name, True)
else:
results["status"] = "â
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:
"""Create approval workflow HTML"""
if approval_required:
return f"""
đĄī¸ Approval Required
Action: Scale resources for {scenario_name}
Risk Level: Low (auto-rollback available)
Blast Radius: Limited to affected service
Status: â
Approved & Executed
"""
else:
return f"""
⥠Auto-Executed
Action: Autonomous healing for {scenario_name}
Mode: Fully autonomous (safety guardrails active)
Guardrails: Blast radius limits, rollback ready, compliance logging
Status: â
Successfully completed
"""
def calculate_roi(self, monthly_incidents: int, avg_impact: int, team_size: int) -> Dict[str, Any]:
"""Calculate ROI"""
try:
annual_impact = monthly_incidents * 12 * avg_impact
team_cost = team_size * 150000 # $150k per engineer
savings = annual_impact * 0.82 # 82% savings with ARF
roi_multiplier = savings / team_cost if team_cost > 0 else 0
if roi_multiplier >= 5.0:
recommendation = "đ Excellent fit for ARF Enterprise"
icon = "đ"
elif roi_multiplier >= 2.0:
recommendation = "â
Good ROI with ARF Enterprise"
icon = "â
"
elif roi_multiplier >= 1.0:
recommendation = "â ī¸ Consider ARF OSS edition first"
icon = "âšī¸"
else:
recommendation = "đ Start with ARF OSS (free)"
icon = "đ"
payback = (team_cost / (savings / 12)) if savings > 0 else 0
return {
"analysis": {
"your_annual_impact": f"${annual_impact:,.0f}",
"your_team_cost": f"${team_cost:,.0f}",
"potential_savings": f"${savings:,.0f}",
"your_roi_multiplier": f"{roi_multiplier:.1f}Ã",
"vs_industry_average": "5.2Ã average ROI",
"recommendation": f"{icon} {recommendation}",
"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)}"}
def validate_license(self) -> Dict[str, Any]:
"""Validate current license"""
# Simulate license validation
self.license_info["last_validated"] = datetime.datetime.now().isoformat()
self.license_info["validation_code"] = f"VAL-{random.randint(1000, 9999)}"
return self.license_info
def start_trial(self) -> Dict[str, Any]:
"""Start 30-day trial"""
expires = datetime.datetime.now() + datetime.timedelta(days=30)
self.license_info.update({
"tier": "TRIAL",
"expires_at": expires.isoformat(),
"features": ["autonomous_healing", "compliance", "audit_trail"],
"max_services": 10,
"max_incidents_per_month": 100,
"status": "đ Trial Active",
"trial_started": datetime.datetime.now().isoformat(),
"days_remaining": 30
})
return self.license_info
def upgrade_license(self) -> Dict[str, Any]:
"""Upgrade license tier"""
tiers = ["STARTER", "PROFESSIONAL", "ENTERPRISE", "PLATFORM"]
current_tier = self.license_info.get("tier", "STARTER")
current_idx = tiers.index(current_tier) if current_tier in tiers else 0
if current_idx < len(tiers) - 1:
new_tier = tiers[current_idx + 1]
self.license_info["tier"] = new_tier
self.license_info["status"] = f"â
Upgraded to {new_tier}"
return self.license_info
def update_mcp_mode(self, mode: str) -> Dict[str, Any]:
"""Update MCP execution mode"""
self.mcp_mode = mode
# Update configuration
config = {
"mcp_mode": mode,
"approval_required": mode == "approval",
"autonomous": mode == "autonomous",
"safety_guardrails": True,
"rollback_enabled": True,
"compliance_mode": "strict"
}
return config
def get_learning_stats(self) -> Dict[str, Any]:
"""Get current learning engine statistics"""
# Update stats based on history
total_incidents = len(self.audit_manager.incident_history)
resolved = len([e for e in self.audit_manager.execution_history
if "Executed" in e.get("status", "")])
if total_incidents > 0:
success_rate = (resolved / total_incidents) * 100
self.learning_stats.update({
"total_incidents": total_incidents,
"resolved_automatically": resolved,
"success_rate": f"{success_rate:.1f}%",
"graph_nodes": total_incidents,
"graph_edges": total_incidents * 2 # Rough estimate
})
return self.learning_stats
def update_learning_stats(self) -> Dict[str, Any]:
"""Update and return learning stats"""
return self.get_learning_stats()
def analyze_patterns(self) -> Dict[str, Any]:
"""Analyze patterns in incident history"""
incidents = list(self.audit_manager.incident_history)
if not incidents:
return {"status": "No incidents to analyze"}
# Analyze incident types
type_counts = {}
severity_counts = {1: 0, 2: 0, 3: 0}
for incident in incidents:
inc_type = incident.get("type", "Unknown")
type_counts[inc_type] = type_counts.get(inc_type, 0) + 1
severity_counts[incident.get("severity", 2)] += 1
# Find most common incidents
most_common = sorted(type_counts.items(), key=lambda x: x[1], reverse=True)[:5]
# Calculate metrics
total_incidents = len(incidents)
avg_severity = sum(severity_counts.values()) / total_incidents if total_incidents > 0 else 0
return {
"total_incidents_analyzed": total_incidents,
"most_common_incidents": dict(most_common),
"severity_distribution": severity_counts,
"average_severity": f"{avg_severity:.2f}",
"time_period_covered": "Last 24 hours",
"recommendations": [
f"Focus on resolving {most_common[0][0]} incidents (most frequent)" if most_common else "No patterns found",
"Implement proactive monitoring for high-severity patterns",
"Review incident resolution times for optimization",
"Update runbooks based on frequent patterns"
]
}
def search_similar_incidents(self, query: str) -> List[List[str]]:
"""Search for similar incidents"""
if not query.strip():
return []
# Mock search - in real app this would use embeddings
incidents = list(self.audit_manager.incident_history)[:10]
results = []
for i, incident in enumerate(incidents[:5]): # Return top 5
similarity = 0.7 + (i * 0.05) # Mock similarity score
results.append([
incident.get("type", "Unknown"),
f"{similarity:.0%}",
"â
Resolved" if i % 2 == 0 else "â ī¸ Pending",
f"{random.randint(1, 5)} actions"
])
return results
def export_graph_data(self) -> str:
"""Export graph data"""
# Mock export
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.json')
temp_file.write(json.dumps({
"nodes": len(self.audit_manager.incident_history),
"edges": len(self.audit_manager.incident_history) * 2,
"exported_at": datetime.datetime.now().isoformat(),
"incidents": list(self.audit_manager.incident_history)[:10]
}, default=str).encode())
temp_file.close()
return temp_file.name
def export_embeddings(self) -> str:
"""Export embeddings"""
# Mock export
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.bin')
temp_file.write(b"Mock embeddings data")
temp_file.close()
return temp_file.name
# ===========================================
# MAIN INTERFACE
# ===========================================
def create_interface() -> gr.Blocks:
"""Create the comprehensive Gradio interface"""
# Initialize managers
audit_manager = AuditTrailManager()
business_logic = EnhancedBusinessLogic(audit_manager)
viz_engine = EnhancedVisualizationEngine()
custom_css = """
.gradio-container { max-width: 1400px !important; margin: auto !important; }
h1, h2, h3 { color: #1a365d !important; }
.critical { color: #FF6B6B !important; font-weight: bold; }
.success { color: #4ECDC4 !important; font-weight: bold; }
"""
with gr.Blocks(
title="đ ARF Investor Demo v3.8.0",
theme=gr.themes.Soft(),
css=custom_css
) as demo:
# ============ HEADER ============
arf_status = "â
ARF OSS v3.3.6" if ARF_OSS_AVAILABLE else "â ī¸ Simulation Mode"
gr.Markdown(f"""
# đ Agentic Reliability Framework - Investor Demo v3.8.0
## From Cost Center to Profit Engine: 5.2Ã ROI with Autonomous Reliability
{arf_status} | Experience: OSS (Advisory) â Enterprise (Autonomous)
""")
# ============ MAIN TABS ============
with gr.Tabs():
# TAB 1: LIVE INCIDENT DEMO
with gr.TabItem("đĨ Live Incident Demo"):
with gr.Row():
# Left Panel
with gr.Column(scale=1):
gr.Markdown("### đŦ Incident Scenario")
scenario_dropdown = gr.Dropdown(
choices=list(INCIDENT_SCENARIOS.keys()),
value="Cache Miss Storm",
label="Select critical incident:"
)
# Scenario description
scenario_description = gr.Markdown(
value=INCIDENT_SCENARIOS["Cache Miss Storm"]["description"]
)
gr.Markdown("### đ Current Crisis Metrics")
metrics_display = gr.JSON(
value=INCIDENT_SCENARIOS["Cache Miss Storm"]["metrics"],
label="Live Metrics"
)
gr.Markdown("### đ° Business Impact")
impact_display = gr.JSON(
value=INCIDENT_SCENARIOS["Cache Miss Storm"]["impact"],
label="Impact Analysis"
)
# Right Panel
with gr.Column(scale=2):
# Visualization
gr.Markdown("### đ Incident Timeline")
timeline_output = gr.Plot()
# Action Section
gr.Markdown("### ⥠Take Action")
with gr.Row():
oss_btn = gr.Button("đ Run OSS Analysis", variant="secondary")
enterprise_btn = gr.Button("đ Execute Enterprise Healing", variant="primary")
# Approval Controls
with gr.Row():
approval_toggle = gr.Checkbox(
label="đ Require Manual Approval",
value=True,
info="Toggle to show approval workflow vs auto-execution"
)
demo_mode_btn = gr.Button("⥠Quick Demo", variant="secondary", size="sm")
# Approval Display
approval_display = gr.HTML(
value="Approval status will appear here
"
)
# Configuration
config_display = gr.JSON(
label="âī¸ Enterprise Configuration",
value={"approval_required": True, "compliance_mode": "strict"}
)
# Results
results_display = gr.JSON(
label="đ¯ Execution Results",
value={"status": "Ready for execution..."}
)
# TAB 2: BUSINESS IMPACT & ROI
with gr.TabItem("đ° Business Impact & ROI"):
with gr.Column():
# Business Dashboard
gr.Markdown("### đ Executive Business Dashboard")
dashboard_output = gr.Plot()
# ROI Calculator
gr.Markdown("### 𧎠Interactive ROI Calculator")
with gr.Row():
with gr.Column(scale=1):
monthly_slider = gr.Slider(
1, 100, value=15, step=1,
label="Monthly incidents"
)
impact_slider = gr.Slider(
1000, 50000, value=8500, step=500,
label="Average incident impact ($)"
)
team_slider = gr.Slider(
1, 20, value=5, step=1,
label="Reliability team size"
)
calculate_btn = gr.Button("Calculate My ROI", variant="primary")
with gr.Column(scale=2):
roi_output = gr.JSON(
label="Your ROI Analysis",
value={"analysis": "Adjust sliders and click Calculate"}
)
# ROI Metrics
with gr.Row():
with gr.Column():
gr.Markdown("""
**đ ARF Enterprise ROI Metrics**
- **Average ROI:** 5.2Ã 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("""
**đ¯ 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("đ Audit Trail & History"):
with gr.Row():
# Left Column - Execution History
with gr.Column(scale=1):
gr.Markdown("### đ Execution History (Audit Trail)")
# Controls
with gr.Row():
refresh_btn = gr.Button("đ Refresh", variant="secondary", size="sm")
clear_btn = gr.Button("đī¸ Clear History", variant="stop", size="sm")
export_btn = gr.Button("đĨ Export JSON", variant="secondary", size="sm")
analyze_btn = gr.Button("đ§ AI Analysis", variant="primary", size="sm")
# Execution Table
execution_table = gr.Dataframe(
headers=["Time", "Scenario", "Actions", "Status", "Savings", "Details"],
value=audit_manager.get_execution_history_table(),
label="đ Execution History",
interactive=False,
wrap=True,
datatype=["str", "str", "str", "str", "str", "str"]
)
gr.Markdown("### đ Visual History")
execution_chart = gr.Plot()
# AI Analysis Results
gr.Markdown("### đ§ AI Pattern Analysis")
ai_analysis = gr.JSON(
value={"status": "Click 'AI Analysis' to run pattern detection"},
label="Pattern Recognition Results"
)
# Right Column - Incident History
with gr.Column(scale=1):
gr.Markdown("### đ Incident History")
incident_table = gr.Dataframe(
headers=["Time", "Service", "Type", "Severity", "Description"],
value=audit_manager.get_incident_history_table(),
label="đ Incident History",
interactive=False,
wrap=True
)
# Memory Graph Visualization
gr.Markdown("### đ§ Memory Graph")
memory_graph = gr.Plot()
# Export Area
gr.Markdown("### đ¤ Export & Analytics")
export_text = gr.Textbox(
label="Full Audit Trail (JSON)",
value=audit_manager.export_audit_trail(),
lines=10,
max_lines=15
)
with gr.Row():
download_btn = gr.Button("đž Download JSON", variant="secondary")
compliance_btn = gr.Button("đ Generate Compliance Report", variant="secondary")
# TAB 4: ENTERPRISE FEATURES & LICENSE
with gr.TabItem("đĸ Enterprise Features"):
with gr.Row():
# Left Column - License Management
with gr.Column(scale=1):
gr.Markdown("### đ License Management")
# License Info Display
license_display = gr.JSON(
value=business_logic.license_info,
label="License Information"
)
# License Controls
with gr.Row():
validate_btn = gr.Button("đ Validate License", variant="secondary")
trial_btn = gr.Button("đ Start 30-Day Trial", variant="primary")
upgrade_btn = gr.Button("đ Upgrade Tier", variant="secondary")
# Feature Comparison
gr.Markdown("### ⥠Feature Matrix")
features_data = [
["đ¤ Autonomous Healing", "â", "â
Auto", "Enterprise Only"],
["đ Executive Dashboards", "Basic", "Advanced", "â
Comprehensive"],
["đ Compliance Automation", "â", "â
", "â
SOC2/GDPR"],
["đ Predictive Analytics", "â", "Basic", "â
ML-Powered"],
["đ Auto-Remediation", "Manual", "â
Auto", "â
AI-Driven"],
["đ¯ SLA Guarantees", "â", "â", "â
99.9%"],
["đ Cost Optimization", "Basic", "Advanced", "â
AI-Optimized"],
["đ Role-Based Access", "â", "â
", "â
Granular"],
["đ Audit Trail", "Basic", "â
", "â
Comprehensive"],
["đ Multi-Cloud", "â", "â", "â
Native"],
["đ§ Learning Engine", "â", "Basic", "â
Continuous"],
["đ Compliance Reports", "â", "â", "â
Automated"],
]
features_table = gr.Dataframe(
value=features_data,
headers=["Feature", "OSS", "Starter", "Enterprise"],
label="Feature Comparison Matrix",
interactive=False,
wrap=True
)
# Right Column - Compliance & Integration
with gr.Column(scale=1):
# Compliance Status
gr.Markdown("### đ Compliance Status")
compliance_status = gr.JSON(
value={
"SOC2": {"status": "â
Certified", "expires": "2025-06-30"},
"GDPR": {"status": "â
Compliant", "last_audit": "2024-10-15"},
"HIPAA": {"status": "đĄ In Progress", "eta": "2024-12-31"},
"ISO27001": {"status": "â
Certified", "cert_id": "ISO-2024-001"},
"FedRAMP": {"status": "đ Moderate Pending", "phase": "Assessment"},
"CCPA": {"status": "â
Compliant", "verified": True}
},
label="Compliance Certifications"
)
# Integration Status
gr.Markdown("### đ Integration Hub")
integrations_data = [
["AWS", "CloudWatch, S3, Lambda", "â
Connected", "Last sync: 5min ago"],
["Azure", "Monitor, Log Analytics", "â
Connected", "Last sync: 8min ago"],
["GCP", "Operations, BigQuery", "â
Connected", "Last sync: 3min ago"],
["Datadog", "Metrics, Logs, APM", "â
Connected", "Active"],
["New Relic", "Full-stack", "â
Connected", "Active"],
["PagerDuty", "Incident Response", "â
Connected", "On-call active"],
["ServiceNow", "ITSM & CMDB", "â
Connected", "Last sync: 15min ago"],
["Slack", "Notifications", "â
Connected", "#arf-alerts"],
["Teams", "Notifications", "â
Connected", "General channel"],
["Jira", "Issue Tracking", "â
Connected", "ARF project"],
["GitHub", "CI/CD", "â
Connected", "Webhooks active"],
["GitLab", "CI/CD", "â
Connected", "Pipelines active"],
]
integrations_table = gr.Dataframe(
value=integrations_data,
headers=["Platform", "Services", "Status", "Details"],
label="Active Integrations",
interactive=False,
wrap=True
)
# MCP Mode Selector
gr.Markdown("### âī¸ MCP Execution Mode")
mcp_mode = gr.Radio(
choices=["advisory", "approval", "autonomous"],
value="approval",
label="Select MCP Mode:",
info="Controls how healing actions are executed"
)
update_mode_btn = gr.Button("đ Update Mode", variant="secondary")
# TAB 5: MEMORY & LEARNING ENGINE
with gr.TabItem("đ§ Memory & Learning"):
with gr.Row():
# Left Column - Memory Graph
with gr.Column(scale=2):
gr.Markdown("### đ§ Incident Memory Graph")
# Graph Controls
with gr.Row():
graph_type = gr.Radio(
choices=["Force Directed", "Hierarchical", "Timeline"],
value="Force Directed",
label="Graph Type"
)
show_weights = gr.Checkbox(label="Show Edge Weights", value=True)
auto_layout = gr.Checkbox(label="Auto-Layout", value=True)
# Graph Visualization
memory_graph_plot = gr.Plot()
# Node Details
gr.Markdown("### đ Selected Node Details")
node_details = gr.JSON(
value={"select": "a node in the graph to see details"},
label="Node Information"
)
# Right Column - Learning Engine
with gr.Column(scale=1):
# Similarity Search
gr.Markdown("### đ Similarity Search")
search_query = gr.Textbox(
label="Search for similar incidents",
placeholder="Describe incident or paste metrics...",
lines=3
)
with gr.Row():
search_btn = gr.Button("đ Search Memory", variant="primary")
clear_search_btn = gr.Button("Clear", variant="secondary")
search_results = gr.Dataframe(
headers=["Incident", "Similarity", "Resolution", "Actions"],
value=[],
label="Search Results",
interactive=False
)
# Learning Statistics
gr.Markdown("### đ Learning Engine Stats")
learning_stats = gr.JSON(
value=business_logic.learning_stats,
label="Learning Engine Statistics"
)
# Export Learning Data
gr.Markdown("### đ¤ Export Learning Data")
with gr.Row():
export_graph_btn = gr.Button("đž Export Graph", variant="secondary")
export_embeddings_btn = gr.Button("đž Export Embeddings", variant="secondary")
export_status = gr.Textbox(
label="Export Status",
value="Ready for export...",
interactive=False
)
# ===========================================
# EVENT HANDLERS
# ===========================================
def update_scenario(scenario_name: str) -> Tuple[str, Dict, Dict, go.Figure, Dict]:
"""Update scenario display"""
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(),
{"status": "Ready to analyze..."}
)
scenario_dropdown.change(
fn=update_scenario,
inputs=[scenario_dropdown],
outputs=[scenario_description, metrics_display, impact_display, timeline_output, results_display]
)
def run_oss_analysis(scenario_name: str) -> Tuple[Dict, List[List[str]], go.Figure]:
"""Run OSS analysis"""
analysis = business_logic.run_oss_analysis(scenario_name)
incident_table_data = audit_manager.get_incident_history_table()
graph_plot = viz_engine.create_memory_graph(audit_manager)
return analysis, incident_table_data, graph_plot
oss_btn.click(
fn=run_oss_analysis,
inputs=[scenario_dropdown],
outputs=[results_display, incident_table, memory_graph]
)
def execute_healing(scenario_name: str, approval_required: bool) -> Tuple[str, Dict, Dict, go.Figure, List[List[str]], List[List[str]], Dict]:
"""Execute enterprise healing"""
results = business_logic.execute_enterprise_healing(scenario_name, approval_required)
new_stats = business_logic.update_learning_stats()
return results + (new_stats,)
enterprise_btn.click(
fn=execute_healing,
inputs=[scenario_dropdown, approval_toggle],
outputs=[
approval_display, config_display, results_display,
execution_chart, execution_table, incident_table,
learning_stats
]
)
def run_quick_demo() -> Tuple[str, Dict, Dict, str, Dict, go.Figure, List[List[str]], List[List[str]], go.Figure, gr.Checkbox, Dict, go.Figure]:
"""Run quick demo"""
analysis = business_logic.run_oss_analysis("Cache Miss Storm")
results = business_logic.execute_enterprise_healing("Cache Miss Storm", False)
new_stats = business_logic.update_learning_stats()
return (
""
"⥠Quick Demo Completed!
"
"1. â
OSS Analysis Completed
"
"2. â
Enterprise Healing Executed
"
"3. â
Audit Trail Updated
"
"4. â
ROI Calculated
"
"5. â
Learning Engine Updated"
"
",
{"status": "demo_completed", "mode": "autonomous"},
analysis,
results[0],
results[2],
viz_engine.create_execution_history_chart(audit_manager),
audit_manager.get_execution_history_table(),
audit_manager.get_incident_history_table(),
viz_engine.create_incident_timeline(),
approval_toggle.update(value=False),
new_stats,
viz_engine.create_memory_graph(audit_manager)
)
demo_mode_btn.click(
fn=run_quick_demo,
outputs=[
scenario_description,
config_display,
results_display,
approval_display,
results_display,
execution_chart,
execution_table,
incident_table,
timeline_output,
approval_toggle,
learning_stats,
memory_graph
]
)
def calculate_roi(monthly_incidents: int, avg_impact: int, team_size: int) -> Dict:
"""Calculate ROI"""
return business_logic.calculate_roi(monthly_incidents, avg_impact, team_size)
calculate_btn.click(
fn=calculate_roi,
inputs=[monthly_slider, impact_slider, team_slider],
outputs=[roi_output]
)
# Slider updates
for slider in [monthly_slider, impact_slider, team_slider]:
slider.change(
fn=calculate_roi,
inputs=[monthly_slider, impact_slider, team_slider],
outputs=[roi_output]
)
# Tab 3: Audit Trail Handlers
def refresh_history() -> Tuple[List[List[str]], List[List[str]], go.Figure, str, go.Figure]:
"""Refresh history display"""
return (
audit_manager.get_execution_history_table(),
audit_manager.get_incident_history_table(),
viz_engine.create_execution_history_chart(audit_manager),
audit_manager.export_audit_trail(),
viz_engine.create_memory_graph(audit_manager)
)
refresh_btn.click(
fn=refresh_history,
outputs=[execution_table, incident_table, execution_chart, export_text, memory_graph]
)
def clear_history() -> Tuple[List[List[str]], List[List[str]], go.Figure, str, go.Figure]:
"""Clear history"""
execution_table_data, incident_table_data = audit_manager.clear_history()
return (
execution_table_data,
incident_table_data,
viz_engine.create_execution_history_chart(audit_manager),
audit_manager.export_audit_trail(),
viz_engine.create_memory_graph(audit_manager)
)
clear_btn.click(
fn=clear_history,
outputs=[execution_table, incident_table, execution_chart, export_text, memory_graph]
)
def run_ai_analysis() -> Tuple[Dict, go.Figure]:
"""Run AI pattern analysis"""
analysis = business_logic.analyze_patterns()
pattern_chart = viz_engine.create_pattern_analysis_chart(analysis)
return analysis, pattern_chart
analyze_btn.click(
fn=run_ai_analysis,
outputs=[ai_analysis, execution_chart]
)
def update_export() -> str:
"""Update export text"""
return audit_manager.export_audit_trail()
export_btn.click(
fn=update_export,
outputs=[export_text]
)
# Tab 4: Enterprise Features Handlers
def validate_license() -> Dict:
"""Validate license"""
return business_logic.validate_license()
validate_btn.click(
fn=validate_license,
outputs=[license_display]
)
def start_trial() -> Dict:
"""Start trial"""
return business_logic.start_trial()
trial_btn.click(
fn=start_trial,
outputs=[license_display]
)
def upgrade_tier() -> Dict:
"""Upgrade license tier"""
return business_logic.upgrade_license()
upgrade_btn.click(
fn=upgrade_tier,
outputs=[license_display]
)
def update_mcp_mode(mode: str) -> Dict:
"""Update MCP mode"""
return business_logic.update_mcp_mode(mode)
update_mode_btn.click(
fn=update_mcp_mode,
inputs=[mcp_mode],
outputs=[config_display]
)
# Tab 5: Memory & Learning Handlers
def update_graph_view(graph_type: str, show_weights: bool, auto_layout: bool) -> go.Figure:
"""Update graph view"""
return viz_engine.create_memory_graph(
audit_manager,
graph_type=graph_type,
show_weights=show_weights,
auto_layout=auto_layout
)
for control in [graph_type, show_weights, auto_layout]:
control.change(
fn=update_graph_view,
inputs=[graph_type, show_weights, auto_layout],
outputs=[memory_graph_plot]
)
def search_memory(query: str) -> Tuple[List[List[str]], str]:
"""Search memory"""
if not query.strip():
return [], "Enter a search query"
results = business_logic.search_similar_incidents(query)
return results, f"Found {len(results)} similar incidents"
search_btn.click(
fn=search_memory,
inputs=[search_query],
outputs=[search_results, export_status]
)
def clear_search() -> Tuple[List, str]:
"""Clear search"""
return [], "Search cleared"
clear_search_btn.click(
fn=clear_search,
outputs=[search_results, export_status]
)
def export_graph_data() -> str:
"""Export graph data"""
export_path = business_logic.export_graph_data()
return f"Graph exported to: {export_path}"
export_graph_btn.click(
fn=export_graph_data,
outputs=[export_status]
)
def export_embeddings() -> str:
"""Export embeddings"""
export_path = business_logic.export_embeddings()
return f"Embeddings exported to: {export_path}"
export_embeddings_btn.click(
fn=export_embeddings,
outputs=[export_status]
)
# ===========================================
# INITIALIZATION
# ===========================================
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),
business_logic.get_learning_stats()
),
outputs=[dashboard_output, timeline_output, execution_chart, memory_graph_plot, learning_stats]
)
# ============ FOOTER ============
gr.Markdown("""
---
### đ ARF Enterprise Platform
**Tabs Overview:**
1. **đĨ Live Incident Demo** - Experience OSS vs Enterprise healing
2. **đ° Business Impact & ROI** - Calculate your savings potential
3. **đ Audit Trail & History** - Complete compliance logging
4. **đĸ Enterprise Features** - License & compliance management
5. **đ§ Memory & Learning** - AI-powered incident memory
**Get Started:**
âĸ **Free Trial:** 30-day enterprise trial
âĸ **Contact:** sales@arfinvestor.com
âĸ **Docs:** docs.arfinvestor.com/enterprise
âĸ **Slack:** Join 2,500+ engineers
Š 2024 Agentic Reliability Framework. Demo v3.8.0 Enterprise Edition
""")
return demo
# ===========================================
# MAIN EXECUTION
# ===========================================
if __name__ == "__main__":
print("đ Starting ARF Ultimate Investor Demo v3.8.0 (Enterprise Edition)...")
print("đ Features: 5 Tabs, Memory Graph, License Management, Compliance")
print("đ§ Learning Engine: Pattern Analysis, Similarity Search")
print("đ Opening web interface...")
demo = create_interface()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
debug=True
)