""" Gradio-only UI components for ARF Ensures full compatibility with app.py Updated with proper imports and error handling """ import gradio as gr from typing import Dict, List, Any import logging logger = logging.getLogger(__name__) # Try to import scenarios from registry first try: from config.scenario_registry import ScenarioRegistry INCIDENT_SCENARIOS = ScenarioRegistry.load_scenarios() logger.info(f"Loaded {len(INCIDENT_SCENARIOS)} scenarios from registry") except ImportError: logger.warning("Scenario registry not available, falling back to demo scenarios") from demo.scenarios import INCIDENT_SCENARIOS # ----------------------------- # Header & Status # ----------------------------- def create_header(version="3.3.6", mock_mode=False) -> gr.HTML: mock_text = " ยท MOCK MODE" if mock_mode else "" return gr.HTML(f"""

๐Ÿš€ Agentic Reliability Framework

v{version} (OSS Edition){mock_text}

Production-grade multi-agent AI for autonomous system reliability intelligence

""") def create_status_bar() -> gr.HTML: return gr.HTML("""
โœ… System Online ๐Ÿง  Agentic Core Active ๐Ÿ“ฆ OSS Mode (Advisory Only) ๐Ÿ”’ MCP Safety Enabled ๐Ÿ’ฐ Enterprise ROI: 5.2ร—
""") # ----------------------------- # Tab 1: Live Incident Demo - UPDATED TO USE INLINE STYLES # ----------------------------- def create_tab1_incident_demo(scenarios=INCIDENT_SCENARIOS, default_scenario="Cache Miss Storm") -> tuple: """ Create an expressive, comprehensive incident demo tab for ARF. Shows the complete OSS analysis โ†’ Enterprise execution workflow. """ # Get the default scenario data default_scenario_data = scenarios.get(default_scenario, {}) business_impact = default_scenario_data.get("business_impact", {}) metrics = default_scenario_data.get("metrics", {}) # Left Column: Scenario Selection & Live Visualization with gr.Column(scale=1, variant="panel") as left_col: # Scenario Selection with rich preview scenario_dropdown = gr.Dropdown( choices=list(scenarios.keys()), value=default_scenario, label="๐ŸŽฏ Select Incident Scenario", info="Choose a production incident to analyze", interactive=True, container=False ) # Scenario Card with rich information - USING INLINE STYLES scenario_card = gr.HTML(f"""

๐Ÿšจ {default_scenario}

{default_scenario_data.get('severity', 'HIGH')}
Component: {default_scenario_data.get('component', 'Unknown').replace('_', ' ').title()}
Affected Users: {metrics.get('affected_users', 'Unknown') if 'affected_users' in metrics else 'Unknown'}
Revenue Risk: ${business_impact.get('revenue_loss_per_hour', 0):,}/hour
Detection Time: 45 seconds (ARF AI)
{default_scenario_data.get('component', 'unknown').split('_')[0]} {default_scenario_data.get('severity', 'high').lower()} production incident
""") # Live Telemetry Visualization telemetry_header = gr.Markdown("### ๐Ÿ“ˆ Live Telemetry") telemetry_viz = gr.Plot(label="", show_label=False) # Business Impact Visualization impact_header = gr.Markdown("### ๐Ÿ’ฐ Business Impact") impact_viz = gr.Plot(label="", show_label=False) # Middle Column: Agent Workflow with gr.Column(scale=2, variant="panel") as middle_col: # Agent Workflow Header workflow_header = gr.Markdown("## ๐Ÿ”„ ARF Agent Workflow") workflow_subheader = gr.Markdown("### How ARF transforms incidents into autonomous healing") # Agent Status Cards - USING INLINE STYLES with gr.Row(): detection_agent = gr.HTML("""
๐Ÿ•ต๏ธโ€โ™‚๏ธ

Detection Agent

Anomaly detected: 99.8% confidence

Response: 45s Accuracy: 98.7%
ACTIVE
""") recall_agent = gr.HTML("""
๐Ÿง 

Recall Agent

3 similar incidents found in RAG memory

Recall: 92% Context: 5 patterns
ACTIVE
""") decision_agent = gr.HTML("""
๐ŸŽฏ

Decision Agent

Generating healing intent with 94% confidence

Success Rate: 87% Safety: 100%
ACTIVE
""") # OSS vs Enterprise Boundary Visualization boundary_header = gr.Markdown("### ๐ŸŽญ OSS vs Enterprise: The Safety Boundary") with gr.Row(): oss_section = gr.HTML("""
๐Ÿ†“

OSS Edition

Apache 2.0

Analysis & Advisory Only - No execution, permanently safe

๐Ÿ“ Healing Intent Created

94% confidence

Action: Scale Redis cluster from 3 to 5 nodes

Pattern Match: Similar incident resolved with scaling (87% success rate)

Safety Check: โœ… Passed (blast radius: 2 services)

Estimated Impact: Reduce MTTR from 45min to 12min

๐Ÿšซ OSS STOPS HERE - No execution
""") enterprise_section = gr.HTML("""
๐Ÿ’ฐ

Enterprise Edition

Commercial

Full Execution & Learning - Autonomous healing with safety guarantees

โšก Ready to Execute

AUTONOMOUS

Mode: Autonomous (Requires Enterprise license)

Expected Recovery: 12 minutes (vs 45 min manual)

Cost Saved: $6,375

Users Protected: 45,000 โ†’ 0 impacted

โœ… Enterprise executes with MCP safety
""") # Execution Controls with gr.Row(): with gr.Column(scale=1): oss_btn = gr.Button( "๐Ÿ†“ Run OSS Analysis", variant="secondary", size="lg" ) oss_info = gr.Markdown("*Free, open-source analysis*") with gr.Column(scale=1): enterprise_btn = gr.Button( "๐Ÿ’ฐ Execute Enterprise Healing", variant="primary", size="lg" ) enterprise_info = gr.Markdown("*Requires Enterprise license*") # Mode Selection & Safety Controls with gr.Row(): with gr.Column(scale=1): approval_toggle = gr.CheckboxGroup( choices=["๐Ÿ‘ค Require Human Approval"], label="Safety Controls", value=[], info="Toggle human oversight" ) with gr.Column(scale=2): mcp_mode = gr.Radio( choices=["๐Ÿ›ก๏ธ Advisory (OSS Only)", "๐Ÿ‘ฅ Approval", "โšก Autonomous"], value="๐Ÿ›ก๏ธ Advisory (OSS Only)", label="MCP Safety Mode", info="Control execution safety level", interactive=True ) # Timeline Visualization timeline_header = gr.Markdown("### โฐ Incident Timeline") timeline_viz = gr.Plot(label="", show_label=False) # Right Column: Results & Metrics with gr.Column(scale=1, variant="panel") as right_col: # Real-time Metrics Dashboard metrics_header = gr.Markdown("## ๐Ÿ“Š Performance Metrics") # Metric Cards Grid - USING INLINE STYLES with gr.Row(): detection_time = gr.HTML("""
โฑ๏ธ

Detection Time

45s

โ†“ 89% faster than average

""") mttr = gr.HTML("""
โšก

Mean Time to Resolve

12m

โ†“ 73% faster than manual

""") with gr.Row(): auto_heal = gr.HTML("""
๐Ÿค–

Auto-Heal Rate

81.7%

โ†‘ 5.4ร— industry average

""") savings = gr.HTML(f"""
๐Ÿ’ฐ

Cost Saved

${int(business_impact.get('revenue_loss_per_hour', 8500) * 0.85 / 1000):.1f}K

Per incident avoided

""") # Results Display Areas oss_results_header = gr.Markdown("### ๐Ÿ†“ OSS Analysis Results") oss_results_display = gr.JSON( label="", value={ "status": "Analysis Pending", "agents": ["Detection", "Recall", "Decision"], "mode": "Advisory Only", "action": "Generate HealingIntent" }, height=200 ) enterprise_results_header = gr.Markdown("### ๐Ÿ’ฐ Enterprise Results") enterprise_results_display = gr.JSON( label="", value={ "status": "Execution Pending", "requires_license": True, "available_modes": ["Approval", "Autonomous"], "expected_outcome": "12m MTTR, $6.3K saved" }, height=200 ) # Approval Status - USING INLINE STYLES approval_display = gr.HTML("""

๐Ÿ‘ค Human Approval Status

Not Required

Current Mode: Advisory (OSS Only)

Switch to "Approval" mode to enable human-in-the-loop workflows

1. ARF generates intent
2. Human reviews & approves
3. ARF executes safely
""") # Demo Actions demo_btn = gr.Button( "โ–ถ๏ธ Run Complete Demo Walkthrough", variant="secondary", size="lg" ) demo_info = gr.Markdown("*Experience the full ARF workflow from detection to resolution*") return ( # Left column returns scenario_dropdown, scenario_card, telemetry_viz, impact_viz, # Middle column returns workflow_header, detection_agent, recall_agent, decision_agent, oss_section, enterprise_section, oss_btn, enterprise_btn, approval_toggle, mcp_mode, timeline_viz, # Right column returns detection_time, mttr, auto_heal, savings, oss_results_display, enterprise_results_display, approval_display, demo_btn ) # ----------------------------- # Tab 2: Business ROI - Updated # ----------------------------- def create_tab2_business_roi(scenarios=INCIDENT_SCENARIOS) -> tuple: dashboard_output = gr.Plot(label="Executive Dashboard", show_label=True) roi_scenario_dropdown = gr.Dropdown( choices=list(scenarios.keys()), value="Cache Miss Storm", label="Scenario for ROI Analysis", info="Select the primary incident type for ROI calculation" ) monthly_slider = gr.Slider( minimum=1, maximum=50, value=15, step=1, label="Monthly Incidents", info="Average number of incidents per month" ) team_slider = gr.Slider( minimum=1, maximum=50, value=5, step=1, label="Team Size", info="Number of engineers on reliability team" ) calculate_btn = gr.Button("๐Ÿ“Š Calculate Comprehensive ROI", variant="primary", size="lg") roi_output = gr.JSON(label="ROI Analysis Results", value={}) roi_chart = gr.Plot(label="ROI Visualization") return (dashboard_output, roi_scenario_dropdown, monthly_slider, team_slider, calculate_btn, roi_output, roi_chart) # ----------------------------- # Tab 3: Enterprise Features # ----------------------------- def create_tab3_enterprise_features() -> tuple: license_display = gr.JSON( value={"status": "๐Ÿ†“ OSS Mode Active", "features": ["Advisory Analysis", "RAG Memory", "HealingIntent"]}, label="License Status" ) validate_btn = gr.Button("๐Ÿ” Validate License", variant="secondary") trial_btn = gr.Button("๐Ÿ†“ Start 30-Day Trial", variant="secondary") upgrade_btn = gr.Button("๐Ÿš€ Upgrade to Enterprise", variant="primary") mcp_mode = gr.Dropdown( choices=["advisory", "approval", "autonomous"], value="advisory", label="MCP Safety Mode" ) mcp_mode_info = gr.JSON( value={ "current_mode": "advisory", "description": "OSS Edition - Analysis only, no execution", "features": ["Incident analysis", "RAG similarity", "HealingIntent creation"] }, label="Mode Details" ) features_data = [ ["Self-Healing Core", "โœ… Active", "Enterprise"], ["RAG Graph Memory", "โœ… Active", "Both"], ["Predictive Analytics", "๐Ÿ”’ Enterprise", "Enterprise"], ["Audit Trail", "๐Ÿ”’ Enterprise", "Enterprise"], ["Compliance (SOC2)", "๐Ÿ”’ Enterprise", "Enterprise"], ["Multi-Cloud", "๐Ÿ”’ Enterprise", "Enterprise"] ] integrations_data = [ ["Prometheus", "โœ… Connected", "Monitoring"], ["Grafana", "โœ… Connected", "Visualization"], ["Slack", "๐Ÿ”’ Enterprise", "Notifications"], ["PagerDuty", "๐Ÿ”’ Enterprise", "Alerting"], ["Jira", "๐Ÿ”’ Enterprise", "Ticketing"], ["Datadog", "๐Ÿ”’ Enterprise", "Monitoring"] ] features_table = gr.Dataframe( headers=["Feature", "Status", "Edition"], value=features_data, label="Feature Comparison" ) integrations_table = gr.Dataframe( headers=["Integration", "Status", "Type"], value=integrations_data, label="Integration Status" ) return (license_display, validate_btn, trial_btn, upgrade_btn, mcp_mode, mcp_mode_info, features_table, integrations_table) # ----------------------------- # Tab 4: Audit Trail # ----------------------------- def create_tab4_audit_trail() -> tuple: refresh_btn = gr.Button("๐Ÿ”„ Refresh Audit Trail", variant="secondary") clear_btn = gr.Button("๐Ÿ—‘๏ธ Clear History", variant="secondary") export_btn = gr.Button("๐Ÿ“ฅ Export as JSON", variant="primary") execution_headers = ["Time", "Scenario", "Mode", "Status", "Savings", "Details"] incident_headers = ["Time", "Component", "Scenario", "Severity", "Status"] execution_table = gr.Dataframe( headers=execution_headers, value=[], label="Execution History" ) incident_table = gr.Dataframe( headers=incident_headers, value=[], label="Incident History" ) export_text = gr.JSON( value={"status": "Export ready"}, label="Export Data" ) return (refresh_btn, clear_btn, export_btn, execution_table, incident_table, export_text) # ----------------------------- # Tab 5: Learning Engine # ----------------------------- def create_tab5_learning_engine() -> tuple: learning_graph = gr.Plot(label="RAG Memory Graph") graph_type = gr.Dropdown( choices=["Incident Patterns", "Action-Outcome Chains", "System Dependencies"], value="Incident Patterns", label="Graph Type" ) show_labels = gr.Checkbox(label="Show Labels", value=True) search_query = gr.Textbox(label="Search Patterns", placeholder="Enter pattern to search...") search_btn = gr.Button("๐Ÿ” Search Patterns", variant="secondary") clear_btn_search = gr.Button("๐Ÿ—‘๏ธ Clear Search", variant="secondary") search_results = gr.JSON( value={"status": "Ready for search"}, label="Search Results" ) stats_display = gr.JSON( value={"patterns": 42, "incidents": 156, "success_rate": "87.3%"}, label="Learning Statistics" ) patterns_display = gr.JSON( value={"common_patterns": ["cache_storm", "db_pool", "memory_leak"]}, label="Pattern Library" ) performance_display = gr.JSON( value={"accuracy": "94.2%", "recall": "89.7%", "precision": "92.1%"}, label="Agent Performance" ) return (learning_graph, graph_type, show_labels, search_query, search_btn, clear_btn_search, search_results, stats_display, patterns_display, performance_display) # ----------------------------- # Footer # ----------------------------- def create_footer() -> gr.HTML: return gr.HTML("""

Agentic Reliability Framework ยฉ 2025

Production-grade multi-agent AI for autonomous system reliability intelligence

GitHub โ€ข Demo โ€ข PyPI โ€ข Enterprise Inquiries
""")