v1.0 - Version stable: multi-PC, détection UI-DETR-1, 3 modes exécution
- Frontend v4 accessible sur réseau local (192.168.1.40) - Ports ouverts: 3002 (frontend), 5001 (backend), 5004 (dashboard) - Ollama GPU fonctionnel - Self-healing interactif - Dashboard confiance Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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core/analytics/realtime/__init__.py
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core/analytics/realtime/__init__.py
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"""Real-time analytics components."""
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from .realtime_analytics import RealtimeAnalytics
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__all__ = ['RealtimeAnalytics']
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283
core/analytics/realtime/realtime_analytics.py
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core/analytics/realtime/realtime_analytics.py
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"""Real-time analytics for active workflows."""
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import logging
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import threading
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from typing import Dict, Any, Optional, List, Callable
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from datetime import datetime
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from dataclasses import dataclass, field
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from ..collection.metrics_collector import MetricsCollector, ExecutionMetrics
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logger = logging.getLogger(__name__)
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@dataclass
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class LiveExecution:
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"""Live execution tracking."""
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execution_id: str
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workflow_id: str
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started_at: datetime
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current_step: int = 0
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total_steps: int = 0
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steps_completed: int = 0
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steps_failed: int = 0
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current_node_id: Optional[str] = None
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last_update: datetime = field(default_factory=datetime.now)
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@property
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def progress_percent(self) -> float:
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"""Calculate progress percentage."""
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if self.total_steps == 0:
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return 0.0
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return (self.steps_completed / self.total_steps) * 100
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@property
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def estimated_completion(self) -> Optional[datetime]:
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"""Estimate completion time."""
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if self.steps_completed == 0 or self.total_steps == 0:
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return None
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elapsed = (datetime.now() - self.started_at).total_seconds()
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avg_time_per_step = elapsed / self.steps_completed
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remaining_steps = self.total_steps - self.steps_completed
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estimated_remaining = avg_time_per_step * remaining_steps
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from datetime import timedelta
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return datetime.now() + timedelta(seconds=estimated_remaining)
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def to_dict(self) -> Dict[str, Any]:
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"""Convert to dictionary."""
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return {
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'execution_id': self.execution_id,
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'workflow_id': self.workflow_id,
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'started_at': self.started_at.isoformat(),
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'current_step': self.current_step,
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'total_steps': self.total_steps,
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'steps_completed': self.steps_completed,
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'steps_failed': self.steps_failed,
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'current_node_id': self.current_node_id,
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'progress_percent': self.progress_percent,
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'estimated_completion': self.estimated_completion.isoformat() if self.estimated_completion else None,
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'last_update': self.last_update.isoformat()
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}
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class RealtimeAnalytics:
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"""Real-time analytics for active workflows."""
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def __init__(self, metrics_collector: Optional[MetricsCollector] = None):
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"""
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Initialize real-time analytics.
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Args:
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metrics_collector: Metrics collector instance
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"""
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self.collector = metrics_collector
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self.active_executions: Dict[str, LiveExecution] = {}
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self.subscribers: Dict[str, List[Callable]] = {}
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self._lock = threading.Lock()
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logger.info("RealtimeAnalytics initialized")
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def track_execution(
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self,
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execution_id: str,
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workflow_id: str,
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total_steps: int = 0
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) -> None:
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"""
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Start tracking an execution in real-time.
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Args:
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execution_id: Execution identifier
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workflow_id: Workflow identifier
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total_steps: Total number of steps
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"""
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with self._lock:
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self.active_executions[execution_id] = LiveExecution(
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execution_id=execution_id,
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workflow_id=workflow_id,
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started_at=datetime.now(),
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total_steps=total_steps
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)
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# Notify subscribers
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self._notify_subscribers(execution_id, 'started')
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logger.info(f"Tracking execution: {execution_id}")
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def update_progress(
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self,
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execution_id: str,
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current_step: int,
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total_steps: Optional[int] = None,
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current_node_id: Optional[str] = None
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) -> None:
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"""
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Update execution progress.
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Args:
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execution_id: Execution identifier
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current_step: Current step number
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total_steps: Total steps (updates if provided)
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current_node_id: Current node ID
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"""
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with self._lock:
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if execution_id not in self.active_executions:
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logger.warning(f"Execution not tracked: {execution_id}")
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return
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execution = self.active_executions[execution_id]
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execution.current_step = current_step
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if total_steps is not None:
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execution.total_steps = total_steps
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if current_node_id is not None:
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execution.current_node_id = current_node_id
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execution.last_update = datetime.now()
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# Notify subscribers
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self._notify_subscribers(execution_id, 'progress')
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def record_step_complete(
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self,
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execution_id: str,
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success: bool
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) -> None:
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"""
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Record step completion.
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Args:
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execution_id: Execution identifier
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success: Whether step succeeded
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"""
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with self._lock:
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if execution_id not in self.active_executions:
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return
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execution = self.active_executions[execution_id]
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if success:
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execution.steps_completed += 1
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else:
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execution.steps_failed += 1
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execution.last_update = datetime.now()
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# Notify subscribers
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self._notify_subscribers(execution_id, 'step_complete')
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def complete_execution(
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self,
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execution_id: str,
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status: str
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) -> None:
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"""
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Mark execution as complete.
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Args:
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execution_id: Execution identifier
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status: Final status
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"""
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with self._lock:
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if execution_id in self.active_executions:
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del self.active_executions[execution_id]
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# Notify subscribers
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self._notify_subscribers(execution_id, 'completed', {'status': status})
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logger.info(f"Execution completed: {execution_id} ({status})")
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def get_live_metrics(self, execution_id: str) -> Optional[Dict[str, Any]]:
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"""
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Get live metrics for an execution.
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Args:
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execution_id: Execution identifier
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Returns:
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Live metrics dictionary or None
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"""
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with self._lock:
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execution = self.active_executions.get(execution_id)
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if not execution:
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return None
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return execution.to_dict()
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def get_all_active(self) -> List[Dict[str, Any]]:
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"""
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Get all active executions.
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Returns:
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List of active execution metrics
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"""
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with self._lock:
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return [e.to_dict() for e in self.active_executions.values()]
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def subscribe(
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self,
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execution_id: str,
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callback: Callable[[str, Dict], None]
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) -> None:
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"""
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Subscribe to real-time updates for an execution.
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Args:
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execution_id: Execution identifier
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callback: Callback function (event_type, data)
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"""
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with self._lock:
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if execution_id not in self.subscribers:
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self.subscribers[execution_id] = []
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self.subscribers[execution_id].append(callback)
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logger.debug(f"Subscriber added for {execution_id}")
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def unsubscribe(
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self,
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execution_id: str,
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callback: Optional[Callable] = None
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) -> None:
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"""
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Unsubscribe from updates.
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Args:
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execution_id: Execution identifier
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callback: Specific callback to remove (None = remove all)
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"""
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with self._lock:
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if execution_id not in self.subscribers:
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return
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if callback is None:
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del self.subscribers[execution_id]
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else:
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self.subscribers[execution_id] = [
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cb for cb in self.subscribers[execution_id] if cb != callback
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]
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def _notify_subscribers(
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self,
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execution_id: str,
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event_type: str,
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data: Optional[Dict] = None
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) -> None:
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"""Notify subscribers of an event."""
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with self._lock:
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callbacks = self.subscribers.get(execution_id, []).copy()
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if not callbacks:
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return
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# Get current metrics
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metrics = self.get_live_metrics(execution_id)
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event_data = {
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'event_type': event_type,
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'execution_id': execution_id,
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'metrics': metrics,
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**(data or {})
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}
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# Call subscribers (outside lock)
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for callback in callbacks:
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try:
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callback(event_type, event_data)
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except Exception as e:
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logger.error(f"Subscriber callback error: {e}")
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