""" Gradio-only UI components for ARF Ensures full compatibility with app.py """ import gradio as gr from typing import Dict, List, Any from demo.scenarios import INCIDENT_SCENARIOS # ----------------------------- # Header & Status # ----------------------------- def create_header(version="3.3.6", mock_mode=False) -> gr.HTML: mock_text = " · MOCK MODE" if mock_mode else "" return gr.HTML(f"""

🚀 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("""

✅ System Online 🧠 Agentic Core Active 📦 OSS Mode (Advisory Only) 🔒 MCP Safety Enabled 💰 Enterprise ROI: 5.2×

""") # ----------------------------- # Tab 1: Live Incident Demo - ENHANCED VERSION # ----------------------------- 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 → Enterprise execution workflow. """ # 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="🎯 Select Incident Scenario", info="Choose a production incident to analyze", interactive=True, container=False ) # Scenario Card with rich information scenario_card = gr.HTML("""

🚨 Cache Miss Storm

HIGH

Component: Redis Cache Cluster

Impact Radius: 85% of users

Revenue Risk: $8,500/hour

Detection Time: 45 seconds

cache redis latency scaling

""") # Live Telemetry Visualization telemetry_header = gr.Markdown("### 📈 Live Telemetry") telemetry_viz = gr.Plot(label="", show_label=False) # Business Impact Visualization impact_header = gr.Markdown("### 💰 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("## 🔄 ARF Agent Workflow") workflow_subheader = gr.Markdown("### How ARF transforms incidents into autonomous healing") # Agent Status Cards with gr.Row(): detection_agent = gr.HTML("""

🕵️‍♂️

Detection Agent

Anomaly detected: 99.8% confidence

Response: 45s Accuracy: 98.7%

ACTIVE

""") recall_agent = gr.HTML("""

🧠

Recall Agent

3 similar incidents found in RAG memory

Recall: 92% Context: 5 patterns

ACTIVE

""") decision_agent = gr.HTML("""

🎯

Decision Agent

Generating healing intent with 94% confidence

Success Rate: 87% Safety: 100%

ACTIVE

""") # OSS vs Enterprise Boundary Visualization boundary_header = gr.Markdown("### 🎭 OSS vs Enterprise: The Safety Boundary") with gr.Row(): oss_section = gr.HTML("""

🆓

OSS Edition

Apache 2.0

Analysis & Advisory Only - No execution, permanently safe

📝 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: ✅ Passed (blast radius: 2 services)

Estimated Impact: Reduce MTTR from 45min to 12min

🚫 OSS STOPS HERE - No execution

""") enterprise_section = gr.HTML("""

💰

Enterprise Edition

Commercial

Full Execution & Learning - Autonomous healing with safety guarantees

⚡ 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 → 0 impacted

✅ Enterprise executes with MCP safety

""") # Execution Controls with gr.Row(): with gr.Column(scale=1): oss_btn = gr.Button( "🆓 Run OSS Analysis", variant="secondary", size="lg", elem_classes="oss-btn" ) oss_info = gr.Markdown("*Free, open-source analysis*", elem_classes="button-info") with gr.Column(scale=1): enterprise_btn = gr.Button( "💰 Execute Enterprise Healing", variant="primary", size="lg", elem_classes="enterprise-btn" ) enterprise_info = gr.Markdown("*Requires Enterprise license*", elem_classes="button-info") # Mode Selection & Safety Controls with gr.Row(): with gr.Column(scale=1): approval_toggle = gr.CheckboxGroup( choices=["👤 Require Human Approval"], label="Safety Controls", value=[], info="Toggle human oversight" ) with gr.Column(scale=2): mcp_mode = gr.Radio( choices=["🛡️ Advisory (OSS Only)", "👥 Approval", "⚡ Autonomous"], value="🛡️ Advisory (OSS Only)", label="MCP Safety Mode", info="Control execution safety level", interactive=True ) # Timeline Visualization timeline_header = gr.Markdown("### ⏰ 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("## 📊 Performance Metrics") # Metric Cards Grid with gr.Row(): detection_time = gr.HTML("""

⏱️

Detection Time

45s

↓ 89% faster than average

""") mttr = gr.HTML("""

⚡

Mean Time to Resolve

12m

↓ 73% faster than manual

""") with gr.Row(): auto_heal = gr.HTML("""

🤖

Auto-Heal Rate

81.7%

↑ 5.4× industry average

""") savings = gr.HTML("""

💰

Cost Saved

$6.3K

Per incident avoided

""") # Results Display Areas oss_results_header = gr.Markdown("### 🆓 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("### 💰 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 approval_display = gr.HTML("""

👤 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( "▶️ Run Complete Demo Walkthrough", variant="secondary", size="lg", full_width=True ) demo_info = gr.Markdown("*Experience the full ARF workflow from detection to resolution*", elem_classes="demo-info") 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 to match new return signature # ----------------------------- 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("📊 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 - Updated # ----------------------------- def create_tab3_enterprise_features() -> tuple: license_display = gr.JSON( value={"status": "🆓 OSS Mode Active", "features": ["Advisory Analysis", "RAG Memory", "HealingIntent"]}, label="License Status" ) validate_btn = gr.Button("🔍 Validate License", variant="secondary") trial_btn = gr.Button("🆓 Start 30-Day Trial", variant="secondary") upgrade_btn = gr.Button("🚀 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", "✅ Active", "Enterprise"], ["RAG Graph Memory", "✅ Active", "Both"], ["Predictive Analytics", "🔒 Enterprise", "Enterprise"], ["Audit Trail", "🔒 Enterprise", "Enterprise"], ["Compliance (SOC2)", "🔒 Enterprise", "Enterprise"], ["Multi-Cloud", "🔒 Enterprise", "Enterprise"] ] integrations_data = [ ["Prometheus", "✅ Connected", "Monitoring"], ["Grafana", "✅ Connected", "Visualization"], ["Slack", "🔒 Enterprise", "Notifications"], ["PagerDuty", "🔒 Enterprise", "Alerting"], ["Jira", "🔒 Enterprise", "Ticketing"], ["Datadog", "🔒 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 - Updated # ----------------------------- def create_tab4_audit_trail() -> tuple: refresh_btn = gr.Button("🔄 Refresh Audit Trail", variant="secondary") clear_btn = gr.Button("🗑️ Clear History", variant="secondary") export_btn = gr.Button("📥 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 - Updated # ----------------------------- 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("🔍 Search Patterns", variant="secondary") clear_btn_search = gr.Button("🗑️ 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(""" """)