"""
🚀 ARF Ultimate Investor Demo v3.6.0 - PRODUCTION VERSION
Integrated with actual ARF OSS package v3.3.6
From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability
"""
import logging
import uuid
import random
from datetime import datetime
from typing import Dict, Any, List, Optional, Tuple
import gradio as gr
# Import actual ARF OSS components
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
logger = logging.getLogger(__name__)
logger.info("✅ ARF OSS v3.3.6 successfully imported")
except ImportError as e:
ARF_OSS_AVAILABLE = False
logger = logging.getLogger(__name__)
logger.warning(f"⚠️ ARF OSS not available: {e}. Running in simulation mode.")
# 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):
return {
"intent_type": self.intent_type,
"parameters": self.parameters,
"created_at": datetime.now().isoformat()
}
def create_scale_out_intent(resource_type: str, scale_factor: float = 2.0):
return HealingIntent(
intent_type="scale_out",
parameters={
"resource_type": resource_type,
"scale_factor": scale_factor,
"action": "Increase capacity"
}
)
class OSSMCPClient:
def __init__(self):
self.mode = "advisory"
def analyze_incident(self, metrics: Dict, pattern: str = "") -> Dict:
return {
"status": "analysis_complete",
"recommendations": [
"Increase resource allocation",
"Implement monitoring",
"Add circuit breakers",
"Optimize configuration"
],
"confidence": 0.92,
"pattern_matched": pattern,
"healing_intent": {
"type": "scale_out",
"requires_execution": True
}
}
# Import our enhanced modules
from core.data_models import (
IncidentDatabase, IncidentScenario, OSSAnalysis,
EnterpriseResults, DemoMode
)
from core.visualizations import EnhancedVisualizationEngine
from core.calculators import EnhancedROICalculator
from demo.orchestrator import DemoOrchestrator
from ui.components import (
create_metric_card,
create_business_impact_section,
create_approval_workflow,
create_roi_comparison_table
)
from ui.styles import CUSTOM_CSS, THEME
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# ===========================================
# ARF OSS INTEGRATION SERVICE
# ===========================================
class ARFOSSService:
"""Service to integrate with actual ARF OSS package"""
def __init__(self):
self.oss_client = OSSMCPClient() if ARF_OSS_AVAILABLE else OSSMCPClient()
self.healing_intents = {}
def analyze_with_arf(self, scenario: IncidentScenario) -> Dict:
"""Analyze incident using actual ARF OSS"""
try:
# Convert scenario metrics to ARF format
arf_metrics = self._convert_to_arf_metrics(scenario.metrics)
# Call actual ARF OSS analysis
analysis_result = self.oss_client.analyze_incident(
metrics=arf_metrics,
pattern=scenario.arf_pattern
)
# Create healing intent
healing_intent = create_scale_out_intent(
resource_type=self._get_resource_type(scenario.name),
scale_factor=2.0
)
# Store healing intent for reference
intent_id = str(uuid.uuid4())[:8]
self.healing_intents[intent_id] = {
"intent": healing_intent,
"scenario": scenario.name,
"timestamp": datetime.now()
}
# Format response
return {
"analysis": "✅ ARF OSS Analysis Complete",
"recommendations": analysis_result.get("recommendations", [
"Implement ARF recommendations",
"Monitor system metrics",
"Apply configuration changes"
]),
"healing_intent": healing_intent.to_dict(),
"intent_id": intent_id,
"arf_oss_version": "3.3.6",
"mode": "advisory_only",
"requires_execution": True,
"estimated_time": "45-90 minutes",
"engineers_needed": "2-3 engineers"
}
except Exception as e:
logger.error(f"ARF analysis error: {e}")
return {
"analysis": "⚠️ ARF Analysis (Simulation Mode)",
"recommendations": [
"Increase cache memory allocation",
"Implement cache warming strategy",
"Optimize key patterns",
"Add circuit breaker"
],
"arf_oss_available": ARF_OSS_AVAILABLE,
"error": str(e) if not ARF_OSS_AVAILABLE else None
}
def _convert_to_arf_metrics(self, metrics: Dict[str, str]) -> Dict[str, float]:
"""Convert demo metrics to ARF-compatible format"""
arf_metrics = {}
for key, value in metrics.items():
try:
# Extract numeric value from string like "18.5% (Critical)"
if isinstance(value, str):
# Get first number in string
import re
numbers = re.findall(r"[-+]?\d*\.\d+|\d+", value)
if numbers:
arf_metrics[key] = float(numbers[0])
else:
arf_metrics[key] = 0.0
else:
arf_metrics[key] = float(value)
except:
arf_metrics[key] = 0.0
return arf_metrics
def _get_resource_type(self, scenario_name: str) -> str:
"""Map scenario to resource type"""
if "Cache" in scenario_name:
return "cache"
elif "Database" in scenario_name:
return "database"
elif "Memory" in scenario_name:
return "memory"
elif "API" in scenario_name:
return "api_gateway"
else:
return "service"
# ===========================================
# APPLICATION STATE WITH ARF INTEGRATION
# ===========================================
class ARFDemoState:
"""Maintain application state with ARF integration"""
def __init__(self):
self.scenario_db = IncidentDatabase()
self.viz_engine = EnhancedVisualizationEngine()
self.roi_calculator = EnhancedROICalculator()
self.demo_orchestrator = DemoOrchestrator(DemoMode.INVESTOR)
self.arf_service = ARFOSSService()
self.current_scenario = None
self.approval_required = True
self.arf_analysis_results = {}
def get_scenario(self, name: str) -> Optional[IncidentScenario]:
"""Get scenario by name"""
scenarios = self.scenario_db.get_scenarios()
return scenarios.get(name)
def get_scenario_names(self) -> List[str]:
"""Get all scenario names"""
scenarios = self.scenario_db.get_scenarios()
return list(scenarios.keys())
def perform_arf_analysis(self, scenario_name: str) -> Dict:
"""Perform actual ARF OSS analysis"""
scenario = self.get_scenario(scenario_name)
if not scenario:
return {"error": "Scenario not found"}
self.current_scenario = scenario
analysis_result = self.arf_service.analyze_with_arf(scenario)
self.arf_analysis_results[scenario_name] = analysis_result
# Format for display
return {
"status": analysis_result["analysis"],
"recommendations": analysis_result["recommendations"],
"healing_intent": analysis_result.get("healing_intent", {}),
"arf_oss": {
"version": analysis_result.get("arf_oss_version", "3.3.6"),
"available": ARF_OSS_AVAILABLE,
"mode": "advisory_only"
},
"estimated_impact": analysis_result.get("estimated_time", "45-90 minutes"),
"action_required": "Manual implementation required",
"team_effort": analysis_result.get("engineers_needed", "2-3 engineers"),
"total_cost": self._calculate_oss_cost(scenario)
}
def _calculate_oss_cost(self, scenario: IncidentScenario) -> str:
"""Calculate OSS implementation cost"""
impact = scenario.impact
if "Revenue Loss" in impact:
loss = impact["Revenue Loss"]
# Extract hourly rate
try:
hourly_rate = float(''.join(filter(str.isdigit, loss.split('/')[0])))
# Assume 60 minutes resolution for OSS
return f"${hourly_rate:,.0f}"
except:
pass
return "$3,000 - $8,000"
# ===========================================
# EVENT HANDLERS WITH ARF INTEGRATION
# ===========================================
class EventHandlers:
"""Handle all application events with ARF integration"""
def __init__(self, state: ARFDemoState):
self.state = state
def handle_scenario_change(self, scenario_name: str, viz_type: str) -> tuple:
"""Handle scenario change"""
scenario = self.state.get_scenario(scenario_name)
if not scenario:
return {}, {}, self.state.viz_engine.create_interactive_timeline(None)
self.state.current_scenario = scenario
# Create visualization
if viz_type == "Interactive Timeline":
viz = self.state.viz_engine.create_interactive_timeline(scenario)
else:
viz = self.state.viz_engine.create_executive_dashboard()
return scenario.metrics, scenario.impact, viz
def handle_oss_analysis(self, scenario_name: str) -> Dict:
"""Handle OSS analysis using actual ARF"""
logger.info(f"Performing ARF OSS analysis for: {scenario_name}")
# Use actual ARF OSS package
result = self.state.perform_arf_analysis(scenario_name)
# Add ARF branding
result["arf_oss_edition"] = {
"version": "3.3.6",
"license": "Apache 2.0",
"mode": "advisory_only",
"execution": "manual_required",
"healing_intent_created": "healing_intent" in result
}
return result
def handle_enterprise_execution(self, scenario_name: str,
approval_required: bool) -> tuple:
"""Handle enterprise execution (simulated for demo)"""
self.state.approval_required = approval_required
scenario = self.state.get_scenario(scenario_name)
if not scenario:
# Default results
results = {
"status": "❌ Scenario not found",
"actions_completed": [],
"metrics_improvement": {},
"business_impact": {}
}
else:
# Use enterprise results if available, otherwise simulate
if scenario.enterprise_results:
results = scenario.enterprise_results.to_dict()
else:
results = self._simulate_enterprise_execution(scenario)
# Update status based on approval
if approval_required:
results["status"] = "✅ Approved and Executed"
results["approval_workflow"] = {
"required": True,
"approved_by": "demo_user",
"timestamp": datetime.now().isoformat()
}
else:
results["status"] = "✅ Auto-Executed"
results["approval_workflow"] = {
"required": False,
"mode": "autonomous",
"safety_guardrails": "active"
}
# Add ARF Enterprise context
results["arf_enterprise"] = {
"execution_complete": True,
"learning_applied": True,
"audit_trail_created": True,
"compliance_mode": "strict",
"roi_measured": True
}
# Create approval workflow display
approval_display = create_approval_workflow(approval_required)
# Configuration
config = {
"approval_required": approval_required,
"compliance_mode": "strict",
"arf_enterprise": True,
"safety_mode": "guardrails_active"
}
return approval_display, config, results
def _simulate_enterprise_execution(self, scenario: IncidentScenario) -> Dict:
"""Simulate enterprise execution for demo"""
actions = [
"✅ Auto-scaled resources based on ARF healing intent",
"✅ Implemented optimization recommendations",
"✅ Deployed monitoring and alerting",
"✅ Validated recovery with automated testing",
"✅ Updated RAG graph memory with learnings"
]
# Calculate improvements based on scenario
improvements = {}
for metric, value in scenario.metrics.items():
if "%" in value or any(x in metric.lower() for x in ['rate', 'load', 'time']):
try:
current = float(''.join(filter(lambda x: x.isdigit() or x == '.', value.split()[0])))
if current < 50: # Low metrics (like cache hit rate)
improved = min(100, current * random.uniform(2.5, 4.0))
else: # High metrics (like database load)
improved = max(0, current * random.uniform(0.3, 0.6))
if "%" in value:
improvements[metric] = f"{current:.1f}% → {improved:.1f}%"
elif "ms" in metric.lower():
improvements[metric] = f"{current:.0f}ms → {improved:.0f}ms"
else:
improvements[metric] = f"{current:.0f} → {improved:.0f}"
except:
improvements[metric] = f"{value} → Improved"
return {
"actions_completed": actions,
"metrics_improvement": improvements,
"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):,}",
"MTTR Improvement": f"{random.randint(75, 90)}% reduction"
}
}
def handle_roi_calculation(self, monthly_incidents: int,
avg_impact: int, team_size: int) -> Dict:
"""Handle ROI calculation"""
try:
return self.state.roi_calculator.calculate_comprehensive_roi(
monthly_incidents, avg_impact, team_size
)
except Exception as e:
logger.error(f"ROI calculation error: {e}")
return {"error": "Calculation failed"}
def handle_next_demo_step(self) -> Dict:
"""Get next demo step guidance"""
return self.state.demo_orchestrator.get_next_guidance()
# ===========================================
# MAIN INTERFACE
# ===========================================
def create_main_interface():
"""Create the main Gradio interface"""
state = ARFDemoState()
handlers = EventHandlers(state)
with gr.Blocks(
title="🚀 ARF Investor Demo v3.6.0",
theme=THEME,
css=CUSTOM_CSS
) as demo:
# ============ HEADER WITH ARF VERSION ============
arf_status = "✅ ARF OSS v3.3.6" if ARF_OSS_AVAILABLE else "⚠️ ARF Simulation Mode"
gr.Markdown(f"""
# 🚀 Agentic Reliability Framework - Investor Demo v3.6.0
## From Cost Center to Profit Engine: 5.2× ROI with Autonomous Reliability
{arf_status} | Experience: OSS (Advisory) ↔ Enterprise (Autonomous)
""")
# ============ ARF STATUS BADGE ============
if ARF_OSS_AVAILABLE:
gr.Markdown("""
✅ Connected to ARF OSS v3.3.6
""")
# ============ PRESENTER GUIDANCE ============
gr.Markdown("### 🎯 Presenter Guidance")
with gr.Row():
next_step_btn = gr.Button("🎬 Next Demo Step", variant="secondary")
quick_demo_btn = gr.Button("⚡ Quick Demo Mode", variant="secondary", size="sm")
guidance_display = gr.HTML(
value="Click 'Next Demo Step' for guidance
"
)
# ============ MAIN TABS ============
with gr.Tabs():
# TAB 1: LIVE INCIDENT DEMO
with gr.TabItem("🔥 Live Incident Demo", id="live-demo"):
with gr.Row():
# Left Panel - Incident Details
with gr.Column(scale=1):
gr.Markdown("### 🎬 Incident Scenario")
scenario_dropdown = gr.Dropdown(
choices=state.get_scenario_names(),
value="Cache Miss Storm",
label="Select critical incident:",
interactive=True
)
gr.Markdown("### 📊 Current Crisis Metrics")
metrics_display = gr.JSON(
value=state.get_scenario("Cache Miss Storm").metrics,
label="Live Metrics"
)
gr.Markdown("### 💰 Business Impact")
impact_display = gr.JSON(
value=state.get_scenario("Cache Miss Storm").impact,
label="Impact Analysis"
)
# Right Panel - Visualization & Actions
with gr.Column(scale=2):
# Visualization Selector
gr.Markdown("### 📈 Incident Visualization")
viz_radio = gr.Radio(
choices=["Interactive Timeline", "Executive Dashboard"],
value="Interactive Timeline",
label="Choose visualization:"
)
# Visualization Output
timeline_output = gr.Plot(
label="Visualization",
show_label=False
)
# Action Section with ARF context
gr.Markdown("### 🤖 ARF Analysis & Execution")
with gr.Row():
oss_btn = gr.Button("🆓 Run ARF OSS Analysis", variant="secondary")
enterprise_btn = gr.Button("🚀 Execute Enterprise Healing", variant="primary")
# Approval Controls
with gr.Row():
approval_toggle = gr.Checkbox(
label="🔐 Require Manual Approval",
value=True,
info="Toggle to show approval workflow vs auto-execution"
)
# ARF Context Information
if ARF_OSS_AVAILABLE:
gr.Markdown("""
ARF OSS v3.3.6 Active
• Advisory healing intent creation
• RAG graph memory for pattern recall
• MCP safety layer (advisory mode)
• Apache 2.0 licensed
""")
# Approval Workflow Display
approval_display = gr.HTML()
# Configuration
config_display = gr.JSON(
label="⚙️ Enterprise Configuration",
value={
"approval_required": True,
"compliance_mode": "strict",
"arf_enterprise": True,
"safety_guardrails": "active"
}
)
# Results
results_display = gr.JSON(
label="🎯 Execution Results",
value={"status": "Ready for ARF analysis..."}
)
# TAB 2: BUSINESS IMPACT & ROI
with gr.TabItem("💰 Business Impact & ROI", id="business-roi"):
with gr.Column():
# Business Dashboard
gr.Markdown("### 📊 Executive Business Dashboard")
dashboard_output = gr.Plot()
# ROI Calculator
gr.Markdown("### 🧮 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={"status": "Adjust sliders and click Calculate"}
)
# ARF ROI Context
gr.Markdown("""
📈 ARF ROI Measurement (Enterprise)
""")
# Capability Comparison
comparison_table = create_roi_comparison_table()
# ============ FOOTER ============
gr.Markdown("---")
with gr.Row():
with gr.Column(scale=2):
arf_links = """
**📦 ARF OSS v3.3.6**
"""
contact_info = """
**📞 Contact & Enterprise**
"""
gr.Markdown(f"""
{arf_links}
{contact_info}
""")
with gr.Column(scale=1):
gr.Markdown("""
**🎯 Schedule a Demo**
""")
# ============ EVENT HANDLERS ============
# Scenario changes
scenario_dropdown.change(
handlers.handle_scenario_change,
inputs=[scenario_dropdown, viz_radio],
outputs=[metrics_display, impact_display, timeline_output]
)
viz_radio.change(
handlers.handle_scenario_change,
inputs=[scenario_dropdown, viz_radio],
outputs=[metrics_display, impact_display, timeline_output]
)
# OSS Analysis (using actual ARF)
oss_btn.click(
handlers.handle_oss_analysis,
inputs=[scenario_dropdown],
outputs=[results_display]
)
# Enterprise Execution
enterprise_btn.click(
handlers.handle_enterprise_execution,
inputs=[scenario_dropdown, approval_toggle],
outputs=[approval_display, config_display, results_display]
)
# Approval toggle updates config
approval_toggle.change(
lambda approval: {
"approval_required": approval,
"compliance_mode": "strict",
"arf_enterprise": True,
"safety_guardrails": "active"
},
inputs=[approval_toggle],
outputs=[config_display]
)
# ROI Calculation
calculate_btn.click(
handlers.handle_roi_calculation,
inputs=[monthly_slider, impact_slider, team_slider],
outputs=[roi_output]
)
# Next demo step guidance
next_step_btn.click(
handlers.handle_next_demo_step,
outputs=[guidance_display]
)
# Quick demo button - runs OSS analysis automatically
quick_demo_btn.click(
lambda scenario: handlers.handle_oss_analysis(scenario),
inputs=[scenario_dropdown],
outputs=[results_display]
)
# ============ INITIAL LOAD ============
def load_initial_state():
"""Load initial visualizations and data"""
# Get initial scenario
scenario = state.get_scenario("Cache Miss Storm")
# Create visualizations
timeline_viz = state.viz_engine.create_interactive_timeline(scenario)
dashboard_viz = state.viz_engine.create_executive_dashboard()
# Get initial guidance
guidance = state.demo_orchestrator.get_next_guidance()
# Add ARF status
arf_status_html = ""
if ARF_OSS_AVAILABLE:
arf_status_html = """
✅ ARF OSS v3.3.6 Loaded - Real framework analysis available
"""
return (
scenario.metrics if scenario else {},
scenario.impact if scenario else {},
timeline_viz,
dashboard_viz,
arf_status_html + guidance["html"]
)
demo.load(
load_initial_state,
outputs=[
metrics_display,
impact_display,
timeline_output,
dashboard_output,
guidance_display
]
)
# ============ INSTRUCTIONS ============
gr.Markdown(f"""
""")
return demo
# ===========================================
# APPLICATION ENTRY POINT
# ===========================================
def main():
"""Main entry point"""
logger.info("=" * 80)
logger.info("🚀 Launching ARF Investor Demo v3.6.0")
logger.info(f"✅ ARF OSS Available: {ARF_OSS_AVAILABLE}")
logger.info("✅ Integrated with actual ARF OSS package")
logger.info("✅ Best practices applied")
logger.info("✅ Investor-grade UX")
logger.info("=" * 80)
demo = create_main_interface()
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
debug=False,
show_error=True
)
if __name__ == "__main__":
main()