#!/usr/bin/env python3 """ ARF v3.3.9 - Agentic Reliability Framework Enterprise Edition with Modern UI & Complete Psychological Integration ARCHITECTURE: OSS advises ā†’ Enterprise executes ENHANCEMENTS: - Modern UI components with psychological UX - Complete design token system - Enhanced security & validation - State management with undo/redo - Telemetry & performance monitoring - Historical evidence dominance - Formal HealingIntent system """ import logging import sys import traceback import json import datetime import asyncio import time import random import re from pathlib import Path from typing import Dict, List, Any, Optional, Tuple, Callable from dataclasses import dataclass, asdict from enum import Enum # =========================================== # ENHANCED CONFIGURATION # =========================================== logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.StreamHandler(sys.stdout), logging.FileHandler('arf_demo.log') ] ) logger = logging.getLogger(__name__) logger.info("šŸš€ ARF v3.3.9 Initializing with Complete Modern Integration") # Add parent directory to path sys.path.insert(0, str(Path(__file__).parent)) # =========================================== # MODERN COMPONENTS INTEGRATION # =========================================== try: from ui.modern_components import ( ObservationGate, SequencingFlow, ProcessDisplay, HistoricalEvidencePanel, HealingIntentDisplay, initialize_modern_ui, DESIGN_TOKENS ) MODERN_COMPONENTS_AVAILABLE = True logger.info("āœ… Modern UI components loaded successfully") except ImportError as e: MODERN_COMPONENTS_AVAILABLE = False logger.warning(f"Modern components not available: {e}. Using fallback implementations.") # =========================================== # PHASE 1: DESIGN TOKENS & CONSTANTS # =========================================== class DesignTokens: """Single source of truth for all design values - INTEGRATED""" # Colors (semantic naming) - Aligned with modern components COLORS = { # OSS Theme "oss_primary": "#10b981", "oss_secondary": "#34d399", "oss_background": "#f0fdf4", "oss_border": "#86efac", # Enterprise Theme "enterprise_primary": "#3b82f6", "enterprise_secondary": "#60a5fa", "enterprise_background": "#eff6ff", "enterprise_border": "#93c5fd", # Semantic Colors (aligned with modern components) "success": "#10b981", "warning": "#f59e0b", "danger": "#ef4444", "info": "#3b82f6", # Neutral Scale "neutral_50": "#f8fafc", "neutral_100": "#f1f5f9", "neutral_200": "#e2e8f0", "neutral_300": "#cbd5e1", "neutral_400": "#94a3b8", "neutral_500": "#64748b", "neutral_600": "#475569", "neutral_700": "#334155", "neutral_800": "#1e293b", "neutral_900": "#0f172a", # Surface Colors "surface_light": "#ffffff", "surface_dark": "#1e293b", "background_light": "#f8fafc", "background_dark": "#0f172a" } # CSS Classes (consistent naming) CLASSES = { # Buttons "btn_primary": "btn btn-primary", "btn_secondary": "btn btn-secondary", "btn_success": "btn btn-success", "btn_danger": "btn btn-danger", "btn_warning": "btn btn-warning", # Cards "card_default": "card", "card_elevated": "card card-elevated", "card_outlined": "card card-outlined", # Layout "container": "container mx-auto px-4", "container_fluid": "container-fluid", # Grid "grid_2": "grid grid-cols-1 md:grid-cols-2 gap-4", "grid_3": "grid grid-cols-1 md:grid-cols-3 gap-4", "grid_4": "grid grid-cols-2 md:grid-cols-4 gap-4", # Spacing "spacing_sm": "p-2", "spacing_md": "p-4", "spacing_lg": "p-6", "spacing_xl": "p-8" } # Typography TYPOGRAPHY = { "font_family": "system-ui, -apple-system, sans-serif", "font_size_sm": "0.875rem", "font_size_base": "1rem", "font_size_lg": "1.125rem", "font_size_xl": "1.25rem", "font_size_2xl": "1.5rem", "font_size_3xl": "1.875rem", "font_size_4xl": "2.25rem" } # Borders & Radius BORDERS = { "radius_sm": "0.25rem", "radius_md": "0.5rem", "radius_lg": "0.75rem", "radius_xl": "1rem", "radius_full": "9999px" } # Shadows SHADOWS = { "shadow_sm": "0 1px 2px 0 rgb(0 0 0 / 0.05)", "shadow_md": "0 4px 6px -1px rgb(0 0 0 / 0.1)", "shadow_lg": "0 10px 15px -3px rgb(0 0 0 / 0.1)", "shadow_xl": "0 20px 25px -5px rgb(0 0 0 / 0.1)" } # Transitions TRANSITIONS = { "fast": "150ms cubic-bezier(0.4, 0, 0.2, 1)", "normal": "300ms cubic-bezier(0.4, 0, 0.2, 1)", "slow": "500ms cubic-bezier(0.4, 0, 0.2, 1)" } @classmethod def get_border_color(cls, component_type: str) -> str: """Get appropriate border color for component type""" color_map = { "oss": cls.COLORS["oss_primary"], "enterprise": cls.COLORS["enterprise_primary"], "success": cls.COLORS["success"], "warning": cls.COLORS["warning"], "danger": cls.COLORS["danger"], "default": cls.COLORS["neutral_300"] } return color_map.get(component_type, color_map["default"]) @classmethod def get_background_color(cls, component_type: str, dark_mode: bool = False) -> str: """Get appropriate background color""" if dark_mode: return cls.COLORS["surface_dark"] bg_map = { "oss": cls.COLORS["oss_background"], "enterprise": cls.COLORS["enterprise_background"], "surface": cls.COLORS["surface_light"], "background": cls.COLORS["background_light"] } return bg_map.get(component_type, bg_map["surface"]) # =========================================== # PHASE 1: INPUT VALIDATION # =========================================== class InputValidator: """Validate all user inputs for security and correctness""" # Whitelist of allowed scenarios SCENARIO_WHITELIST = { "Cache Miss Storm", "Database Connection Pool Exhaustion", "Kubernetes Memory Leak", "API Rate Limit Storm", "Network Partition", "Storage I/O Saturation" } # Allowed characters for various inputs PATTERNS = { "scenario_name": r'^[a-zA-Z0-9\s\-_]+$', # Alphanumeric, spaces, dashes, underscores "numeric_id": r'^[0-9]+$', "safe_text": r'^[a-zA-Z0-9\s\.,!?\-_\(\)]+$' } @classmethod def validate_scenario(cls, scenario: str) -> Tuple[bool, str]: """Validate scenario name - returns (is_valid, sanitized_value)""" if not scenario or not isinstance(scenario, str): return False, "Cache Miss Storm" # Check against whitelist if scenario in cls.SCENARIO_WHITELIST: return True, scenario # Log attempt for security monitoring logger.warning(f"Invalid scenario attempt: {scenario}") # Return safe default return False, list(cls.SCENARIO_WHITELIST)[0] @classmethod def validate_numeric_input(cls, value: Any, min_val: float = 0, max_val: float = float('inf')) -> Tuple[bool, float]: """Validate numeric input with bounds""" try: num = float(value) if min_val <= num <= max_val: return True, num else: logger.warning(f"Numeric input out of bounds: {value}") return False, max(min_val, min(num, max_val)) except (ValueError, TypeError): logger.warning(f"Invalid numeric input: {value}") return False, min_val @classmethod def sanitize_html(cls, html: str) -> str: """Basic HTML sanitization""" if not html: return "" # Remove script tags and event handlers sanitized = re.sub(r'.*?', '', html, flags=re.IGNORECASE | re.DOTALL) sanitized = re.sub(r'on\w+="[^"]*"', '', sanitized) sanitized = re.sub(r'on\w+=\'[^\']*\'', '', sanitized) # Limit length if len(sanitized) > 10000: sanitized = sanitized[:10000] + "..." return sanitized @classmethod def validate_email(cls, email: str) -> bool: """Simple email validation""" if not email: return False pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$' return bool(re.match(pattern, email)) # =========================================== # PHASE 1: ENHANCED PSYCHOLOGICAL UX # =========================================== class PsychologicalUX: """Manage user cognitive load and expectations - MODERN INTEGRATED""" class LoadingStage(Enum): DETECTION = "detection" RECALL = "recall" DECISION = "decision" EXECUTION = "execution" ANALYSIS = "analysis" VISUALIZATION = "visualization" @staticmethod def create_loading_state(stage: LoadingStage, progress: int = 0) -> str: """Show what's happening during long operations""" stage_configs = { PsychologicalUX.LoadingStage.DETECTION: { "title": "šŸ” Detecting Patterns", "description": "Analyzing telemetry for anomaly signatures", "details": "Comparing against 150+ known patterns", "color": DesignTokens.COLORS["info"], "icon": "šŸ”" }, PsychologicalUX.LoadingStage.RECALL: { "title": "šŸ§  Historical Recall", "description": "Searching similar incidents in memory", "details": "Accessing RAG database with 10,000+ incidents", "color": DesignTokens.COLORS["oss_primary"], "icon": "šŸ§ " }, PsychologicalUX.LoadingStage.DECISION: { "title": "šŸŽÆ Decision Making", "description": "Formulating optimal healing strategy", "details": "Evaluating 5+ action sequences for safety", "color": DesignTokens.COLORS["warning"], "icon": "šŸŽÆ" }, PsychologicalUX.LoadingStage.EXECUTION: { "title": "āš” Safe Execution", "description": "Executing with rollback guarantees", "details": "Atomic operations with real-time validation", "color": DesignTokens.COLORS["enterprise_primary"], "icon": "āš”" } } config = stage_configs.get(stage, { "title": "Processing", "description": "ARF is working...", "details": "Please wait", "color": DesignTokens.COLORS["neutral_500"], "icon": "ā³" }) progress_bar = "" if progress > 0: progress_bar = f"""
""" return f"""
{config['icon']}

{config['title']}

{config['description']}

{config['details']}

{progress_bar}
""" @staticmethod def create_success_notification(action: str, duration: str = "2.4s") -> str: """Create satisfying completion feedback""" return f"""
āœ“

Success!

{action} completed successfully

{datetime.datetime.now().strftime("%H:%M:%S")}
""" @staticmethod def create_error_notification(error: str, recovery: str = "") -> str: """Create helpful error feedback""" recovery_html = f'

{recovery}

' if recovery else "" return f"""
āš ļø

Operation Failed

{error}

{recovery_html}
""" @staticmethod def create_empty_state(icon: str, title: str, description: str, action: str = "") -> str: """Create helpful empty state""" action_html = f'' if action else "" return f"""
{icon}

{title}

{description}

{action_html}
""" # =========================================== # MODERN PSYCHOLOGICAL COMPONENTS # =========================================== @staticmethod def create_observation_gate(confidence: float = 65.0, **kwargs) -> str: """Create observation gate with modern components""" # Validate input is_valid, safe_confidence = InputValidator.validate_numeric_input(confidence, 0, 100) # Track usage from app import telemetry telemetry.track_user_interaction( "observation_gate_viewed", f"confidence_{safe_confidence}" ) # Use modern component if available if MODERN_COMPONENTS_AVAILABLE: try: html = ObservationGate.create(safe_confidence, **kwargs) except Exception as e: logger.error(f"Error creating modern observation gate: {e}") html = PsychologicalUX._create_observation_gate_fallback(safe_confidence, **kwargs) else: html = PsychologicalUX._create_observation_gate_fallback(safe_confidence, **kwargs) # Sanitize and return return InputValidator.sanitize_html(html) @staticmethod def _create_observation_gate_fallback(confidence: float = 65.0, **kwargs) -> str: """Fallback observation gate implementation""" threshold = 70.0 if confidence < threshold: status_color = DesignTokens.COLORS["warning"] status_text = "Observation Gate: Awaiting confirmation" decision_text = "Decision Intentionally Deferred" else: status_color = DesignTokens.COLORS["success"] status_text = "Observation Gate Cleared" decision_text = "Proceed with Policy Action" return f"""

{status_text}

System restraint engaged

{decision_text}

Confidence: {confidence:.1f}% (Threshold: {threshold}%)

""" @staticmethod def create_sequencing_panel(**kwargs) -> str: """Create sequencing visualization with modern components""" # Track usage from app import telemetry telemetry.track_user_interaction("sequencing_viewed", "panel") # Use modern component if available if MODERN_COMPONENTS_AVAILABLE: try: html = SequencingFlow.create(**kwargs) except Exception as e: logger.error(f"Error creating modern sequencing: {e}") html = PsychologicalUX._create_sequencing_fallback(**kwargs) else: html = PsychologicalUX._create_sequencing_fallback(**kwargs) return InputValidator.sanitize_html(html) @staticmethod def _create_sequencing_fallback(**kwargs) -> str: """Fallback sequencing implementation""" return """

šŸ”„ Doctrinal Sequencing: Policy Over Reaction

1. Dampening (Required)
2. Concurrency Control (Required)
3. Observe (Required)
4. Scale (Optional)
""" @staticmethod def create_historical_evidence_panel(evidence_data: Dict = None, **kwargs) -> str: """Create historical evidence panel""" # Track usage from app import telemetry telemetry.track_user_interaction("historical_evidence_viewed", "panel") if MODERN_COMPONENTS_AVAILABLE: try: html = HistoricalEvidencePanel.create(evidence_data, **kwargs) except Exception as e: logger.error(f"Error creating modern historical evidence: {e}") html = PsychologicalUX._create_historical_evidence_fallback(evidence_data, **kwargs) else: html = PsychologicalUX._create_historical_evidence_fallback(evidence_data, **kwargs) return InputValidator.sanitize_html(html) @staticmethod def _create_historical_evidence_fallback(evidence_data: Dict = None, **kwargs) -> str: """Fallback historical evidence implementation""" return """

šŸ§  Historical Evidence (Why Sequencing Matters)

Historical evidence outweighs model confidence in decision making.

""" @staticmethod def create_healing_intent_display(healing_intent: Dict, **kwargs) -> str: """Create formal HealingIntent display""" # Validate input if not isinstance(healing_intent, dict): healing_intent = {} # Track usage from app import telemetry telemetry.track_user_interaction( "healing_intent_viewed", f"confidence_{healing_intent.get('confidence', 0)}" ) if MODERN_COMPONENTS_AVAILABLE: try: html = HealingIntentDisplay.create(healing_intent, **kwargs) except Exception as e: logger.error(f"Error creating modern healing intent: {e}") html = PsychologicalUX._create_healing_intent_fallback(healing_intent, **kwargs) else: html = PsychologicalUX._create_healing_intent_fallback(healing_intent, **kwargs) return InputValidator.sanitize_html(html) @staticmethod def _create_healing_intent_fallback(healing_intent: Dict, **kwargs) -> str: """Fallback healing intent implementation""" confidence = healing_intent.get('confidence', 0) action = healing_intent.get('primary_action', 'No action specified') return f"""

šŸ“ Formal HealingIntent Created

Confidence: {confidence:.1f}%
Action: {action}
""" @staticmethod def create_process_display(process_type: str, data: Dict) -> str: """Create process display (detection, recall, decision)""" # Validate process type valid_types = ['detection', 'recall', 'decision', 'safety', 'execution', 'learning'] if process_type not in valid_types: process_type = 'detection' # Track usage from app import telemetry telemetry.track_user_interaction(f"process_{process_type}_viewed", "display") if MODERN_COMPONENTS_AVAILABLE: try: html = ProcessDisplay.create(process_type, data) except Exception as e: logger.error(f"Error creating modern process display: {e}") html = PsychologicalUX._create_process_display_fallback(process_type, data) else: html = PsychologicalUX._create_process_display_fallback(process_type, data) return InputValidator.sanitize_html(html) @staticmethod def _create_process_display_fallback(process_type: str, data: Dict) -> str: """Fallback process display implementation""" icons = { 'detection': 'šŸ•µļøā€ā™‚ļø', 'recall': 'šŸ§ ', 'decision': 'šŸŽÆ', 'safety': 'šŸ›”ļø', 'execution': 'āš”', 'learning': 'šŸ“š' } icon = icons.get(process_type, 'šŸ“Š') title = process_type.title() status = data.get('status', 'inactive') return f"""
{icon}
{title}
Status: {status}
""" # =========================================== # FEATURE MANAGEMENT # =========================================== FEATURE_FLAGS = { 'modern_ui': True, 'dark_mode': True, 'responsive_design': True, 'loading_animations': True, 'telemetry': True, 'performance_monitoring': True, 'psychological_components': MODERN_COMPONENTS_AVAILABLE } def is_feature_enabled(feature: str) -> bool: """Simple, transparent feature checking""" return FEATURE_FLAGS.get(feature, False) # =========================================== # PHASE 2: STATE MANAGEMENT # =========================================== class AppState: """Centralized state management with history""" def __init__(self): self._state = {} self._history = [] self._subscribers = [] self._max_history = 50 # Initialize with defaults including psychological state self._state.update({ "theme": "system", "last_scenario": "Cache Miss Storm", "user_interactions": 0, "errors_count": 0, "loading": False, "dark_mode": False, # Psychological state "confidence_level": 65.0, "observation_gate_active": True, "sequencing_stage": "dampening", "historical_evidence_weight": 0.85, "healing_intent_confidence": 87.3 }) def set(self, key: str, value: Any, track_history: bool = True): """Set state with optional history tracking""" old_value = self._state.get(key) if track_history and old_value != value: # Save to history (limited size) self._history.append({**self._state}) if len(self._history) > self._max_history: self._history.pop(0) self._state[key] = value self._notify(key, old_value, value) def get(self, key: str, default=None): """Get state value""" return self._state.get(key, default) def update(self, updates: Dict[str, Any], track_history: bool = True): """Update multiple state values at once""" if track_history: self._history.append({**self._state}) if len(self._history) > self._max_history: self._history.pop(0) self._state.update(updates) for key in updates: self._notify(key, None, updates[key]) def undo(self) -> bool: """Revert to previous state""" if not self._history: return False self._state = self._history.pop() self._notify_all() return True def subscribe(self, callback: Callable[[str, Any, Any], None]): """Subscribe to state changes""" if callback not in self._subscribers: self._subscribers.append(callback) def unsubscribe(self, callback: Callable): """Unsubscribe from state changes""" if callback in self._subscribers: self._subscribers.remove(callback) def _notify(self, key: str, old_value: Any, new_value: Any): """Notify subscribers of state change""" for callback in self._subscribers: try: callback(key, old_value, new_value) except Exception as e: logger.error(f"State subscriber error: {e}") def _notify_all(self): """Notify all keys changed""" for key in self._state: self._notify(key, None, self._state[key]) def get_snapshot(self) -> Dict: """Get complete state snapshot""" return {**self._state} def restore_snapshot(self, snapshot: Dict): """Restore from snapshot""" self._history.append({**self._state}) self._state = {**snapshot} self._notify_all() # Global state instance app_state = AppState() # =========================================== # PHASE 2: TELEMETRY & ANALYTICS # =========================================== class TelemetryCollector: """Anonymous usage analytics and performance monitoring""" # Singleton instance _instance = None def __new__(cls): if cls._instance is None: cls._instance = super().__new__(cls) cls._instance._initialize() return cls._instance def _initialize(self): """Initialize telemetry collector""" self._session_id = self._generate_session_id() self._events = [] self._performance_metrics = [] self._start_time = time.time() # Default consent (anonymous, no PII) self._consent_given = True self._max_events = 1000 logger.info(f"Telemetry initialized with session: {self._session_id[:8]}...") @staticmethod def _generate_session_id() -> str: """Generate anonymous session ID""" import hashlib import uuid # Create deterministic but anonymous session ID unique_str = f"{uuid.getnode()}{datetime.datetime.now().timestamp():.0f}" return hashlib.sha256(unique_str.encode()).hexdigest()[:16] def track_event(self, event: str, metadata: Dict = None, severity: str = "info"): """Track user interactions and system events""" if not is_feature_enabled('telemetry'): return event_data = { "event": event, "timestamp": datetime.datetime.now().isoformat(), "session_id": self._session_id, "metadata": metadata or {}, "severity": severity } # Store locally (in production, this would send to backend) self._events.append(event_data) if len(self._events) > self._max_events: self._events.pop(0) # Log based on severity log_methods = { "error": logger.error, "warning": logger.warning, "info": logger.info, "debug": logger.debug } log_method = log_methods.get(severity, logger.info) log_method(f"Telemetry[{severity}]: {event}") def track_performance(self, component: str, duration_ms: float, metadata: Dict = None): """Track component performance metrics""" if not is_feature_enabled('performance_monitoring'): return metric = { "component": component, "duration_ms": duration_ms, "timestamp": datetime.datetime.now().isoformat(), "metadata": metadata or {} } self._performance_metrics.append(metric) # Alert on slow components if duration_ms > 1000: self.track_event( f"slow_component_{component}", {"duration_ms": duration_ms}, "warning" ) logger.warning(f"Slow component: {component} took {duration_ms:.0f}ms") def track_error(self, error: Exception, context: str = ""): """Track errors with context""" self.track_event( f"error_{context}", { "error_type": type(error).__name__, "error_message": str(error), "context": context }, "error" ) def track_user_interaction(self, interaction: str, element: str = ""): """Track user interactions for UX analysis""" self.track_event( f"user_interaction_{interaction}", { "element": element, "timestamp": datetime.datetime.now().isoformat() }, "info" ) # Update app state app_state.set("user_interactions", app_state.get("user_interactions", 0) + 1) def get_session_summary(self) -> Dict: """Get telemetry summary for current session""" session_duration = time.time() - self._start_time # Count events by severity severity_counts = {} for event in self._events: severity = event.get("severity", "info") severity_counts[severity] = severity_counts.get(severity, 0) + 1 # Calculate average performance avg_performance = 0 if self._performance_metrics: avg_performance = sum(m["duration_ms"] for m in self._performance_metrics) / len(self._performance_metrics) # Psychological component usage psych_events = [e for e in self._events if any(keyword in e["event"] for keyword in [ "observation_gate", "sequencing", "historical_evidence", "healing_intent", "process_" ])] return { "session_id": self._session_id, "session_duration_seconds": round(session_duration, 1), "total_events": len(self._events), "event_counts_by_severity": severity_counts, "psychological_component_events": len(psych_events), "performance_metrics_count": len(self._performance_metrics), "average_component_time_ms": round(avg_performance, 1), "user_interactions": app_state.get("user_interactions", 0), "errors_count": app_state.get("errors_count", 0) } def export_data(self, format: str = "json") -> str: """Export telemetry data (for debugging)""" if format == "json": return json.dumps({ "session_summary": self.get_session_summary(), "recent_events": self._events[-100:], # Last 100 events "recent_performance": self._performance_metrics[-50:] # Last 50 metrics }, indent=2) return "" # Global telemetry instance telemetry = TelemetryCollector() # =========================================== # PHASE 2: PERFORMANCE BUDGET MONITOR # =========================================== class PerformanceBudget: """Enforce performance budgets and prevent regression""" BUDGETS = { "css_size_kb": 50, # Max CSS size "initial_load_time_ms": 3000, # Time to interactive "component_render_ms": 100, # Max component render time "api_response_ms": 500, # Max API response time "animation_duration_ms": 300 # Max animation duration } THRESHOLDS = { "warning": 0.8, # 80% of budget (warning) "critical": 0.95 # 95% of budget (critical) } @classmethod def check_css_budget(cls, css_content: str) -> Tuple[bool, str]: """Check if CSS exceeds budget""" size_kb = len(css_content) / 1024 budget = cls.BUDGETS["css_size_kb"] if size_kb > budget: message = f"CSS budget exceeded: {size_kb:.1f}KB > {budget}KB" logger.error(message) telemetry.track_event("css_budget_exceeded", {"size_kb": size_kb}, "error") return False, message # Check thresholds ratio = size_kb / budget if ratio > cls.THRESHOLDS["critical"]: logger.warning(f"CSS near critical: {size_kb:.1f}KB ({ratio:.0%} of budget)") elif ratio > cls.THRESHOLDS["warning"]: logger.info(f"CSS near warning: {size_kb:.1f}KB ({ratio:.0%} of budget)") return True, f"CSS OK: {size_kb:.1f}KB" @classmethod def check_component_performance(cls, component_name: str, render_time_ms: float) -> bool: """Check component render performance""" budget = cls.BUDGETS["component_render_ms"] if render_time_ms > budget: telemetry.track_performance(component_name, render_time_ms) telemetry.track_event( f"component_slow_{component_name}", {"render_time_ms": render_time_ms}, "warning" ) return False return True @classmethod def measure_execution_time(cls, func: Callable, context: str = "") -> Any: """Decorator to measure execution time""" def wrapper(*args, **kwargs): start_time = time.time() try: result = func(*args, **kwargs) execution_time = (time.time() - start_time) * 1000 # Convert to ms # Check against budget cls.check_component_performance(context or func.__name__, execution_time) # Track performance telemetry.track_performance(context or func.__name__, execution_time) return result except Exception as e: execution_time = (time.time() - start_time) * 1000 telemetry.track_error(e, f"perf_measure_{context}") raise return wrapper @classmethod def get_budget_status(cls) -> Dict: """Get current budget status""" return { "budgets": cls.BUDGETS, "thresholds": cls.THRESHOLDS, "timestamp": datetime.datetime.now().isoformat() } # =========================================== # PHASE 2: UPDATE OPTIMIZATOR # =========================================== class UpdateOptimizer: """Prevent unnecessary UI updates and re-renders""" def __init__(self): self._component_states = {} self._update_throttle_ms = 100 # Minimum time between updates self._last_update_times = {} def should_update_component(self, component_id: str, new_state: Dict) -> bool: """Check if component update is necessary""" old_state = self._component_states.get(component_id, {}) # Deep compare states if self._states_equal(old_state, new_state): logger.debug(f"Skipping update for {component_id} (no state change)") return False # Throttle updates current_time = time.time() * 1000 last_update = self._last_update_times.get(component_id, 0) if current_time - last_update < self._update_throttle_ms: logger.debug(f"Throttling update for {component_id}") return False # Update tracking self._component_states[component_id] = new_state self._last_update_times[component_id] = current_time return True def _states_equal(self, state1: Dict, state2: Dict) -> bool: """Deep compare two states""" try: return json.dumps(state1, sort_keys=True) == json.dumps(state2, sort_keys=True) except: # Fallback for non-serializable states return str(state1) == str(state2) def clear_component_state(self, component_id: str): """Clear cached state for component""" if component_id in self._component_states: del self._component_states[component_id] if component_id in self._last_update_times: del self._last_update_times[component_id] def get_optimization_stats(self) -> Dict: """Get optimization statistics""" return { "tracked_components": len(self._component_states), "throttle_ms": self._update_throttle_ms, "memory_usage_kb": sys.getsizeof(self._component_states) / 1024 } # Global optimizer instance update_optimizer = UpdateOptimizer() # =========================================== # ENHANCED CSS LOADING WITH MODERN INTEGRATION # =========================================== def load_css_files() -> str: """Load and optimize CSS files with modern components integration""" css_parts = [] # Start performance measurement start_time = time.time() # Load base design tokens base_css = f""" :root {{ /* Design Tokens */ {chr(10).join([f'--color-{k.replace("_", "-")}: {v};' for k, v in DesignTokens.COLORS.items()])} {chr(10).join([f'--font-{k.replace("_", "-")}: {v};' for k, v in DesignTokens.TYPOGRAPHY.items()])} {chr(10).join([f'--radius-{k.replace("_", "-")}: {v};' for k, v in DesignTokens.BORDERS.items()])} {chr(10).join([f'--shadow-{k.replace("_", "-")}: {v};' for k, v in DesignTokens.SHADOWS.items()])} {chr(10).join([f'--transition-{k.replace("_", "-")}: {v};' for k, v in DesignTokens.TRANSITIONS.items()])} }} /* Base Styles */ * {{ box-sizing: border-box; }} body {{ font-family: var(--font-family); color: var(--color-neutral-900); background: var(--color-background-light); margin: 0; padding: 0; line-height: 1.5; }} [data-theme="dark"] {{ color: var(--color-neutral-100); background: var(--color-background-dark); }} /* Container */ .container {{ width: 100%; max-width: 1200px; margin: 0 auto; padding: 0 1rem; }} """ css_parts.append(base_css) # Load modern components CSS if available if MODERN_COMPONENTS_AVAILABLE and is_feature_enabled('modern_ui'): try: # Import the CSS generation function from ui.modern_components import create_component_styles modern_css = create_component_styles() css_parts.append(modern_css) logger.info("āœ… Modern component styles loaded") except Exception as e: logger.warning(f"Could not load modern component styles: {e}") # Load responsive utilities if enabled if is_feature_enabled('responsive_design'): responsive_css = """ /* Responsive Utilities */ @media (max-width: 768px) { .container { padding: 0 0.75rem; } .grid-cols-1 { grid-template-columns: 1fr !important; } .md\\:grid-cols-2 { grid-template-columns: 1fr !important; } .md\\:grid-cols-3 { grid-template-columns: 1fr !important; } .md\\:grid-cols-4 { grid-template-columns: 1fr !important; } } @media (hover: none) and (pointer: coarse) { .btn, button, [role="button"] { min-height: 44px; min-width: 44px; padding: 12px 20px; } input, select, textarea { font-size: 16px; padding: 12px; } } """ css_parts.append(responsive_css) # Load dark mode enhancements if is_feature_enabled('dark_mode'): dark_mode_css = f""" /* Dark Mode Enhancements */ [data-theme="dark"] {{ color-scheme: dark; }} [data-theme="dark"] .gradio-container {{ background: var(--color-background-dark) !important; }} /* Smooth theme transitions */ * {{ transition: background-color var(--transition-normal), border-color var(--transition-normal), color var(--transition-normal); }} """ css_parts.append(dark_mode_css) # Load animations if enabled if is_feature_enabled('loading_animations'): animations_css = """ /* Loading Animations */ .loading-state { position: relative; overflow: hidden; } .loading-state::after { content: ''; position: absolute; top: 0; left: -100%; width: 100%; height: 100%; background: linear-gradient( 90deg, transparent, rgba(255, 255, 255, 0.2), transparent ); animation: shimmer 1.5s infinite; } @keyframes shimmer { 0% { left: -100%; } 100% { left: 100%; } } @keyframes bounce { 0%, 100% { transform: translateY(0); } 50% { transform: translateY(-10px); } } .animate-bounce { animation: bounce 1s infinite; } """ css_parts.append(animations_css) # Combine all CSS css_content = "\n".join(css_parts) # Check performance budget is_valid, message = PerformanceBudget.check_css_budget(css_content) if not is_valid: logger.warning(f"CSS budget issue: {message}") # Track performance load_time = (time.time() - start_time) * 1000 telemetry.track_performance("css_loading", load_time) logger.info(f"āœ… CSS loaded: {len(css_content)/1024:.1f}KB in {load_time:.0f}ms") return css_content # =========================================== # MODERN UI COMPONENTS WITH ENHANCEMENTS # =========================================== class ModernUI: """Modern UI component wrappers with validation and telemetry""" @staticmethod def create_header(version: str) -> str: """Create modern header with validation""" # Validate version is_valid, safe_version = InputValidator.validate_numeric_input(version.replace(".", ""), 100, 999) version_display = f"{int(safe_version)//100}.{int(safe_version)%100}" if is_valid else "3.3.9" # Track usage telemetry.track_user_interaction("view_header", "header") return f"""

šŸš€ Agentic Reliability Framework v{version_display}

OSS advises ā†’ Enterprise executes ā€¢ Enhanced with Modern Psychological Components

āœ… OSS Advisory Apache 2.0
ā†’
šŸ¢ Enterprise Execution Commercial
Modern UI v{version_display}
šŸ§  Psychological UX
System Ready ā€¢ Modern Components: {'āœ… Active' if MODERN_COMPONENTS_AVAILABLE else 'āš ļø Fallback'} ā€¢ Performance Budget: OK
""" @staticmethod def create_metric_card(value: str, label: str, icon: str = "", trend: str = "") -> str: """Create validated metric card with modern styling""" # Sanitize inputs safe_value = InputValidator.sanitize_html(value) safe_label = InputValidator.sanitize_html(label) # Track usage telemetry.track_user_interaction("metric_card_viewed", label) trend_classes = { "up": "text-green-600 dark:text-green-400", "down": "text-red-600 dark:text-red-400", "neutral": "text-gray-600 dark:text-gray-400" } trend_icon = {"up": "ā†—", "down": "ā†˜", "neutral": "ā†’"}.get(trend, "") trend_class = trend_classes.get(trend, "") return f"""
{f'
{icon}
' if icon else ''}
{safe_value}
{safe_label}
{f'
{trend_icon}
' if trend else ''}
""" @staticmethod def create_scenario_dropdown() -> str: """Create validated scenario dropdown""" options_html = "" for scenario in InputValidator.SCENARIO_WHITELIST: options_html += f'' return f"""

Validated scenarios only ā€¢ Telemetry active

""" # =========================================== # ENHANCED MAIN APPLICATION # =========================================== def create_enhanced_demo_interface(): """Create enhanced demo interface with modern psychological components""" import gradio as gr import pandas as pd import numpy as np import plotly.graph_objects as go import plotly.express as px # Track initialization telemetry.track_event("app_initialization_started") start_time = time.time() # Load optimized CSS with modern components css_content = PerformanceBudget.measure_execution_time(load_css_files, "css_loading")() # Create the main interface with gr.Blocks( title="šŸš€ ARF v3.3.9 - Modern Psychological Edition", css=css_content, theme=gr.themes.Soft( primary_hue="blue", secondary_hue="purple", neutral_hue="slate" ), head=f''' ''' ) as demo: # =========================================== # INITIALIZATION # =========================================== # Dark mode toggle dark_mode_html = """
""" gr.HTML(dark_mode_html) # =========================================== # ENHANCED HEADER # =========================================== gr.HTML(ModernUI.create_header("3.3.9")) # =========================================== # TELEMETRY STATUS INDICATOR # =========================================== gr.HTML(f"""
āœ… Modern UI: {'Active' if MODERN_COMPONENTS_AVAILABLE else 'Fallback'} šŸ“Š Telemetry: Session {telemetry._session_id[:8]}... šŸ§  Psychological Components āš” Performance Budget: OK
""") # =========================================== # MAIN CONTENT CONTAINER # =========================================== with gr.Column(elem_classes="container mx-auto px-4"): # =========================================== # TAB NAVIGATION WITH STATE MANAGEMENT # =========================================== with gr.Tabs(elem_classes="modern-tabs") as tabs: # Subscribe tab changes to state def on_tab_change(evt: gr.SelectData): telemetry.track_user_interaction("tab_switch", f"tab{evt.index + 1}") app_state.set("current_tab", evt.index) return "" tabs.select(on_tab_change) # TAB 1: ENHANCED LIVE DEMO WITH PSYCHOLOGICAL COMPONENTS with gr.TabItem("šŸ§  Enhanced Live Demo", elem_id="tab1"): with gr.Column(elem_classes="space-y-6"): # Scenario Selection with Validation gr.HTML("
Validated scenarios only:
") scenario_dropdown = gr.Dropdown( choices=list(InputValidator.SCENARIO_WHITELIST), value="Cache Miss Storm", label="Incident Scenario", elem_classes="w-full p-3" ) # Validation feedback gr.HTML("""
āœ“ Scenario validated ā€¢ āœ“ Input sanitized ā€¢ āœ“ Telemetry active
""") # =========================================== # MODERN PSYCHOLOGICAL COMPONENTS SECTION # =========================================== # Historical Evidence Panel (Recall Dominance) gr.HTML("

šŸ§  Historical Evidence Dominance

") # Create example historical evidence data evidence_data = { "scaling_failures": [ { "date": "2024-11-15", "environment": "prod-east", "action": "Scale during retry storm", "outcome": "Amplification increased 300%", "lesson": "Scaling during amplification worsens the problem" } ], "dampening_successes": [ { "date": "2024-12-03", "environment": "prod-west", "action": "Request coalescing + backoff", "outcome": "Resolved in 8 min, $5.1K saved", "lesson": "Dampening broke amplification cycle" } ] } historical_evidence_html = gr.HTML() # Observation Gate gr.HTML("

ā³ Psychological Restraint System

") observation_gate_html = gr.HTML() # Sequencing Visualization gr.HTML("

šŸ”„ Policy Sequencing Logic

") sequencing_html = gr.HTML() # Process Workflow gr.HTML("

šŸ” Policy Process Workflow

") # Detection Process detection_data = { "status": "active", "title": "Detection Process", "description": "Telemetry analysis & pattern recognition", "metrics": { "Pattern Match": "Retry Amplification", "Confidence": "92.7%", "Detection Time": "0.8 seconds" }, "content": "āœ… Detected: Retry amplification pattern with exponential growth", "next_step": "Activate recall process" } detection_html = gr.HTML() # Recall Process recall_data = { "status": "active", "title": "Recall Process", "description": "Historical evidence & pattern matching", "metrics": { "Incidents Matched": "8", "Failure Rate": "76%", "Evidence Weight": "85%" }, "content": "šŸ§  Historical evidence dominates predictive confidence", "next_step": "Generate formal HealingIntent" } recall_html = gr.HTML() # Decision Process decision_data = { "status": "active", "title": "Decision Process", "description": "HealingIntent creation & sequencing", "metrics": { "Confidence": "87.3%", "Sequencing": "Dampening-first", "Reversibility": "100%" }, "content": "šŸŽÆ Formal HealingIntent created with safety constraints", "next_step": "Await observation gate clearance" } decision_html = gr.HTML() # HealingIntent Display gr.HTML("

šŸ“ Formal HealingIntent

") healing_intent_data = { "confidence": 87.3, "primary_action": "Implement request coalescing with exponential backoff (jitter: 25%)", "sequencing_rule": "dampening_first_then_observe_then_optional_scale", "preconditions": [ "Retry amplification detected", "Confidence > 70%", "No scaling contraindicated" ], "contraindications": [ "Scale during retry storm", "Add capacity during amplification" ], "reversibility_statement": "Backoff can be adjusted, coalescing can be disabled, no stateful changes", "historical_evidence": [ "Similar incident resolved with dampening (87% success rate)", "Scaling-first failed 76% of time during amplification" ] } healing_intent_html = gr.HTML() # Performance Metrics with Modern Styling gr.HTML("""

šŸ“ˆ Enhanced Performance Metrics

""") # Create metric cards using ModernUI metrics = [ {"value": "42s", "label": "Detection Time", "icon": "ā±ļø", "trend": "down"}, {"value": "94%", "label": "Recall Quality", "icon": "šŸ§ ", "trend": "up"}, {"value": "87%", "label": "Confidence Score", "icon": "šŸŽÆ", "trend": "neutral"}, {"value": "Stage 2", "label": "Sequencing", "icon": "šŸ”„", "trend": "up"} ] for i, metric in enumerate(metrics): gr.HTML(ModernUI.create_metric_card(**metric)) gr.HTML("
") # OSS vs Enterprise with Modern Components gr.HTML("""

šŸ—ļø Modern Architecture Boundary

""") # OSS Section with gr.Column(): gr.Markdown("### šŸŸ¢ OSS Advisory Layer", elem_classes="text-lg font-semibold mb-3") gr.Markdown(""" **Modern Psychological Capabilities:** - āœ… Historical evidence dominance - āœ… Observation gate restraint - āœ… Formal HealingIntent creation - āœ… Sequencing policy enforcement **State:** Modern Active **Telemetry:** Recording psychological patterns """, elem_classes="text-sm") def run_modern_oss_analysis(): """Run OSS analysis with modern psychological components""" telemetry.track_user_interaction("modern_oss_analysis", "button") # Show loading state loading_html = PsychologicalUX.create_loading_state( PsychologicalUX.LoadingStage.ANALYSIS ) # Update psychological components historical_html = PsychologicalUX.create_historical_evidence_panel(evidence_data) observation_html = PsychologicalUX.create_observation_gate(65.0) sequencing_html = PsychologicalUX.create_sequencing_panel() detection_html = PsychologicalUX.create_process_display('detection', detection_data) recall_html = PsychologicalUX.create_process_display('recall', recall_data) decision_html = PsychologicalUX.create_process_display('decision', decision_data) healing_html = PsychologicalUX.create_healing_intent_display(healing_intent_data) # Simulate analysis time.sleep(1) # Show success success_html = PsychologicalUX.create_success_notification( "Modern OSS analysis completed with psychological insights" ) # Update state app_state.set("last_analysis", datetime.datetime.now().isoformat()) app_state.set("analysis_count", app_state.get("analysis_count", 0) + 1) app_state.set("psychological_components_used", True) return ( historical_html, observation_html, sequencing_html, detection_html, recall_html, decision_html, healing_html, loading_html + success_html ) oss_btn = gr.Button( "Run Modern Analysis", elem_classes="btn btn-success mt-4" ) oss_output = gr.HTML() oss_btn.click( run_modern_oss_analysis, outputs=[ historical_evidence_html, observation_gate_html, sequencing_html, detection_html, recall_html, decision_html, healing_intent_html, oss_output ] ) # Enterprise Section with gr.Column(): gr.Markdown("### šŸŸ£ Modern Enterprise Execution", elem_classes="text-lg font-semibold mb-3") gr.Markdown(""" **Enhanced Capabilities:** - āœ… Autonomous execution with psychological safety - āœ… Rollback guarantee with observation gates - āœ… MCP integration with sequencing policies - āœ… Commercial license with modern components **State:** Modern Ready **Safety:** Psychological constraints enabled """, elem_classes="text-sm") def execute_modern_enterprise(): """Execute with modern psychological components""" telemetry.track_user_interaction("modern_enterprise_execution", "button") # Validate execution conditions if app_state.get("errors_count", 0) > 5: error_html = PsychologicalUX.create_error_notification( "Too many errors", "Reset application state" ) return error_html # Show execution loading with psychological context execution_html = PsychologicalUX.create_loading_state( PsychologicalUX.LoadingStage.EXECUTION, progress=50 ) # Simulate execution with modern components time.sleep(1.5) # Update observation gate to show cleared state observation_html = PsychologicalUX.create_observation_gate(85.0) # Update sequencing to show progress sequencing_html = PsychologicalUX.create_sequencing_panel(current_step=3) # Track success telemetry.track_event("modern_enterprise_execution_success") app_state.set("execution_count", app_state.get("execution_count", 0) + 1) app_state.set("confidence_level", 85.0) app_state.set("observation_gate_active", False) success_html = PsychologicalUX.create_success_notification( "Modern enterprise execution completed with psychological safety checks" ) return observation_html, sequencing_html, execution_html + success_html enterprise_btn = gr.Button( "Execute with Modern Safety", elem_classes="btn btn-primary mt-4" ) enterprise_observation_output = gr.HTML() enterprise_sequencing_output = gr.HTML() enterprise_output = gr.HTML() enterprise_btn.click( execute_modern_enterprise, outputs=[enterprise_observation_output, enterprise_sequencing_output, enterprise_output] ) gr.HTML("
") # State Management with Modern Integration with gr.Row(elem_classes="mt-6 p-4 bg-gray-50 dark:bg-gray-900 rounded-lg"): gr.Markdown("### šŸ§  Modern State Management", elem_classes="text-lg font-semibold") def show_modern_state(): """Display current app state with psychological context""" state_snapshot = app_state.get_snapshot() telemetry_state = telemetry.get_session_summary() # Filter psychological state psych_state = {k: v for k, v in state_snapshot.items() if any( keyword in k for keyword in [ 'confidence', 'observation', 'sequencing', 'historical', 'healing' ])} state_html = f"""

Psychological State:

{json.dumps(psych_state, indent=2)}

Modern Components:

{json.dumps({
    "modern_components_available": MODERN_COMPONENTS_AVAILABLE,
    "psychological_events": telemetry_state.get("psychological_component_events", 0),
    "features_enabled": {k: v for k, v in FEATURE_FLAGS.items() if v}
}, indent=2)}
""" return state_html def clear_modern_state(): """Reset application state with psychological defaults""" app_state._state = {} app_state._history = [] # Reset with psychological defaults app_state.update({ "theme": "system", "last_scenario": "Cache Miss Storm", "user_interactions": 0, "errors_count": 0, "loading": False, "dark_mode": False, # Psychological state defaults "confidence_level": 65.0, "observation_gate_active": True, "sequencing_stage": "dampening", "historical_evidence_weight": 0.85, "healing_intent_confidence": 87.3 }) telemetry.track_event("modern_state_cleared") return "Modern state cleared with psychological defaults" state_btn = gr.Button("Show Modern State", elem_classes="btn btn-secondary") clear_btn = gr.Button("Clear Modern State", elem_classes="btn btn-danger") state_output = gr.HTML() state_btn.click(show_modern_state, outputs=[state_output]) clear_btn.click(clear_modern_state, outputs=[state_output]) # TAB 2: ENHANCED ROI CALCULATOR with gr.TabItem("šŸ’° Enhanced ROI Calculator"): with gr.Column(elem_classes="space-y-6"): # Input Validation Example with gr.Row(elem_classes="grid grid-cols-1 md:grid-cols-3 gap-4"): incidents_input = gr.Slider( minimum=1, maximum=100, value=15, label="Incidents/Month", elem_classes="w-full" ) team_input = gr.Slider( minimum=1, maximum=50, value=5, label="Team Size", elem_classes="w-full" ) cost_input = gr.Number( value=200000, label="Engineer Cost/Year ($)", elem_classes="w-full" ) def calculate_enhanced_roi(incidents, team_size, engineer_cost): """Calculate ROI with validation and telemetry""" # Validate inputs is_valid_incidents, incidents_val = InputValidator.validate_numeric_input(incidents, 1, 1000) is_valid_team, team_val = InputValidator.validate_numeric_input(team_size, 1, 100) is_valid_cost, cost_val = InputValidator.validate_numeric_input(engineer_cost, 50000, 1000000) if not all([is_valid_incidents, is_valid_team, is_valid_cost]): telemetry.track_event("roi_calculation_invalid_input") return PsychologicalUX.create_error_notification( "Invalid input values", "Using safe defaults" ) # Track calculation telemetry.track_user_interaction("roi_calculation", "button") # Calculate ROI monthly_impact = incidents_val * 8500 # Average impact per incident annual_impact = monthly_impact * 12 team_cost = team_val * cost_val potential_savings = annual_impact * 0.82 roi_multiplier = potential_savings / (team_cost * 0.3) # ARF cost is 30% of team # Create visualization fig = go.Figure(data=[ go.Bar( name='Without ARF', x=['Cost'], y=[annual_impact], marker_color=DesignTokens.COLORS["danger"] ), go.Bar( name='With ARF', x=['Cost'], y=[annual_impact - potential_savings], marker_color=DesignTokens.COLORS["success"] ) ]) fig.update_layout( title=f"ROI Analysis: {roi_multiplier:.1f}x Return", showlegend=True, height=300 ) # Create result display result_html = f"""

šŸ“Š ROI Analysis Results

${potential_savings:,.0f}
Annual Savings
{roi_multiplier:.1f}x
ROI Multiplier

Based on {incidents_val} incidents/month ā€¢ Validated inputs ā€¢ Telemetry recorded

""" return result_html, fig calculate_btn = gr.Button( "Calculate Enhanced ROI", elem_classes="btn btn-primary" ) roi_output = gr.HTML() roi_chart = gr.Plot() calculate_btn.click( calculate_enhanced_roi, inputs=[incidents_input, team_input, cost_input], outputs=[roi_output, roi_chart] ) # TAB 3: MODERN PERFORMANCE DASHBOARD with gr.TabItem("šŸ“Š Modern Dashboard"): with gr.Column(elem_classes="space-y-6"): # Performance Budget Status gr.Markdown("### āš” Modern Performance Budget Status", elem_classes="text-xl font-semibold") def get_modern_performance_status(): """Get current performance status with modern integration""" budget_status = PerformanceBudget.get_budget_status() telemetry_summary = telemetry.get_session_summary() optimization_stats = update_optimizer.get_optimization_stats() # Calculate psychological component performance psych_events = telemetry_summary.get("psychological_component_events", 0) total_events = telemetry_summary.get("total_events", 1) psych_usage_rate = (psych_events / total_events * 100) if total_events > 0 else 0 status_html = f"""

šŸš€ Modern Integration Status

Modern Components
{'āœ…' if MODERN_COMPONENTS_AVAILABLE else 'āš ļø'}
Psychological Usage
{psych_usage_rate:.1f}%
CSS Size
{len(css_content)/1024:.1f}KB
Components Tracked
{optimization_stats.get('tracked_components', 0)}

šŸ§  Psychological Component Insights

""" # Add psychological component stats psych_components = ["observation_gate", "sequencing", "historical_evidence", "healing_intent"] for component in psych_components: component_events = len([e for e in telemetry._events if component in e["event"]]) status_html += f"""
{component.replace('_', ' ').title()}
{component_events}
""" status_html += """
""" return status_html refresh_btn = gr.Button( "šŸ”„ Refresh Modern Dashboard", elem_classes="btn btn-secondary" ) dashboard_output = gr.HTML() refresh_btn.click(get_modern_performance_status, outputs=[dashboard_output]) # Export Modern Telemetry Data def export_modern_telemetry_data(): """Export telemetry data with psychological insights""" telemetry.track_user_interaction("export_modern_telemetry", "button") data = telemetry.export_data() return data export_btn = gr.Button( "šŸ’¾ Export Modern Telemetry", elem_classes="btn btn-primary" ) export_output = gr.Textbox( label="Modern Telemetry Data (JSON)", lines=10, elem_classes="w-full" ) export_btn.click(export_modern_telemetry_data, outputs=[export_output]) # =========================================== # ENHANCED FOOTER WITH MODERN STATUS # =========================================== gr.HTML(f""" """) # =========================================== # INITIALIZATION COMPLETE # =========================================== # Initialize psychological components on startup def initialize_psychological_components(): """Initialize psychological components with current state""" confidence = app_state.get("confidence_level", 65.0) historical_html = PsychologicalUX.create_historical_evidence_panel(evidence_data) observation_html = PsychologicalUX.create_observation_gate(confidence) sequencing_html = PsychologicalUX.create_sequencing_panel() detection_html = PsychologicalUX.create_process_display('detection', detection_data) recall_html = PsychologicalUX.create_process_display('recall', recall_data) decision_html = PsychologicalUX.create_process_display('decision', decision_data) healing_html = PsychologicalUX.create_healing_intent_display(healing_intent_data) return ( historical_html, observation_html, sequencing_html, detection_html, recall_html, decision_html, healing_html ) # Load initial psychological components demo.load( initialize_psychological_components, inputs=[], outputs=[ historical_evidence_html, observation_gate_html, sequencing_html, detection_html, recall_html, decision_html, healing_intent_html ] ) # Track successful initialization init_time = (time.time() - start_time) * 1000 telemetry.track_event("modern_app_initialization_complete", { "init_time_ms": init_time, "modern_components": MODERN_COMPONENTS_AVAILABLE, "css_size_kb": len(css_content)/1024 }) telemetry.track_performance("modern_app_initialization", init_time) logger.info(f"āœ… Modern demo interface created in {init_time:.0f}ms") logger.info(f"šŸ“Š Modern components: {'āœ… Active' if MODERN_COMPONENTS_AVAILABLE else 'āš ļø Fallback'}") # Initialize app state with modern data app_state.update({ "init_time_ms": init_time, "telemetry_session": telemetry._session_id, "performance_budgets": PerformanceBudget.get_budget_status(), "modern_components_available": MODERN_COMPONENTS_AVAILABLE, "psychological_features_enabled": is_feature_enabled('psychological_components') }) return demo # =========================================== # ENHANCED MAIN EXECUTION # =========================================== def main(): """Enhanced main entry point with modern integration""" try: print("\n" + "="*70) print("šŸš€ ARF v3.3.9 - Modern Psychological Edition") print("="*70) print("šŸŽÆ MODERN INTEGRATION STATUS:") print(f" ā€¢ Modern UI Components: {'āœ… ACTIVE' if MODERN_COMPONENTS_AVAILABLE else 'āš ļø FALLBACK'}") print(f" ā€¢ Psychological Features: {'āœ… ENABLED' if is_feature_enabled('psychological_components') else 'āŒ DISABLED'}") print() print("šŸ§  PSYCHOLOGICAL COMPONENTS IMPLEMENTED:") print(" ā€¢ Observation Gate System (Active restraint display)") print(" ā€¢ Sequencing Visualization (Policy over reaction)") print(" ā€¢ Historical Evidence Panel (Recall dominance)") print(" ā€¢ Formal HealingIntent Display (Professional documentation)") print(" ā€¢ Process Workflow (Detection ā†’ Recall ā†’ Decision)") print() print("šŸ“Š ENTERPRISE FEATURES:") print(f" ā€¢ Telemetry: {'āœ…' if is_feature_enabled('telemetry') else 'āŒ'}") print(f" ā€¢ Performance Monitoring: {'āœ…' if is_feature_enabled('performance_monitoring') else 'āŒ'}") print(f" ā€¢ Modern Design System: {'āœ…' if is_feature_enabled('modern_ui') else 'āŒ'}") print(f" ā€¢ Dark Mode: {'āœ…' if is_feature_enabled('dark_mode') else 'āŒ'}") print("="*70 + "\n") # Create and launch the enhanced demo demo = create_enhanced_demo_interface() # Get configuration import os port = int(os.getenv("GRADIO_SERVER_PORT", "7860")) server_name = os.getenv("GRADIO_SERVER_NAME", "0.0.0.0") print(f"šŸŒ Launching on http://{server_name}:{port}") print("šŸ§  Psychology: Historical evidence dominance enabled") print("ā³ Restraint: Observation gate system active") print("šŸ”„ Sequencing: Policy-enforced action sequences") print("šŸ“Š Telemetry: Psychological pattern tracking") print("\nšŸ’” MODERN TIPS:") print(" - Check 'Modern Dashboard' tab for psychological insights") print(" - Try OSS analysis to see historical evidence dominance") print(" - Watch observation gate change with confidence levels") print(" - Export telemetry to see psychological component usage") # Enable queue if needed (for concurrent requests) demo.queue() # This enables the queue in Gradio 6.x # Launch with enhanced configuration demo.launch( server_name=server_name, server_port=port, share=False, debug=False, show_error=True, quiet=True ) except KeyboardInterrupt: print("\nšŸ‘‹ Modern demo stopped by user") telemetry.track_event("modern_app_shutdown_user") except Exception as e: print(f"\nāŒ Modern integration error: {e}") telemetry.track_error(e, "modern_main_execution") import traceback traceback.print_exc() sys.exit(1) # =========================================== # INSTALLATION STATUS FUNCTION # =========================================== def get_installation_status() -> Dict[str, Any]: """Get installation status for modern components""" try: # Try to import ARF package import pkg_resources try: arf_version = pkg_resources.get_distribution("agentic-reliability-framework").version oss_installed = True except pkg_resources.DistributionNotFound: arf_version = None oss_installed = False return { "oss_installed": oss_installed, "oss_version": arf_version, "enterprise_installed": False, # Placeholder for enterprise check "enterprise_version": None, "execution_allowed": False, "recommendations": [ "Modern UI components integrated successfully", "Psychological features active" if MODERN_COMPONENTS_AVAILABLE else "Using fallback psychological components" ], "badges": [ {"text": "Modern UI", "color": "purple", "icon": "šŸŽØ"}, {"text": "Psychological", "color": "amber", "icon": "šŸ§ "}, {"text": f"Components: {'āœ… Modern' if MODERN_COMPONENTS_AVAILABLE else 'āš ļø Fallback'}", "color": "blue", "icon": "āš”"} ] } except Exception as e: logger.error(f"Installation check failed: {e}") return { "oss_installed": False, "oss_version": None, "recommendations": ["Run 'pip install agentic-reliability-framework'"], "badges": [ {"text": "Demo Mode", "color": "gray", "icon": "āš ļø"} ] } # =========================================== # MODERN SPACES ENTRY POINT # =========================================== if __name__ == "__main__": # Enhanced configuration for Hugging Face Spaces import os # Environment configuration with validation os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" os.environ["GRADIO_SERVER_PORT"] = "7860" os.environ["GRADIO_SERVER_NAME"] = "0.0.0.0" os.environ["GRADIO_QUEUE_ENABLED"] = "True" os.environ["UVICORN_LOG_LEVEL"] = "warning" # Validate environment required_vars = ["GRADIO_SERVER_PORT", "GRADIO_SERVER_NAME"] for var in required_vars: if var not in os.environ: logger.warning(f"Environment variable {var} not set, using default") # Check if running in Spaces is_space = "SPACE_ID" in os.environ or "HF_SPACE" in os.environ print(f"\n{'='*70}") print(f"{'šŸ¤— Hugging Face Spaces - Modern Edition' if is_space else 'šŸš€ Local Deployment - Modern Edition'}") print(f"{'='*70}") # Record startup telemetry with modern context telemetry.track_event("modern_app_startup", { "environment": "spaces" if is_space else "local", "modern_components": MODERN_COMPONENTS_AVAILABLE, "psychological_features": is_feature_enabled('psychological_components') }) # Initialize app state with modern context app_state.update({ "environment": "spaces" if is_space else "local", "startup_time": datetime.datetime.now().isoformat(), "python_version": sys.version, "modern_integration": True, "psychological_components_version": "3.3.9+modern" }) # Run the enhanced modern application main()