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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Dom
286
core/healing/recovery_logger.py
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286
core/healing/recovery_logger.py
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"""Logging and monitoring for self-healing operations."""
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import json
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import logging
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from pathlib import Path
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from typing import Dict, List, Optional
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from datetime import datetime
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from .models import RecoveryContext, RecoveryResult
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class RecoveryLogger:
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"""Logger for self-healing recovery operations."""
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def __init__(self, log_path: Optional[Path] = None):
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"""
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Initialize recovery logger.
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Args:
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log_path: Path for storing recovery logs
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"""
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self.log_path = log_path or Path('logs/healing')
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self.log_path.mkdir(parents=True, exist_ok=True)
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# Setup file logger
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self.logger = logging.getLogger('healing')
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self.logger.setLevel(logging.INFO)
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# File handler
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log_file = self.log_path / 'recovery.log'
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handler = logging.FileHandler(log_file)
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handler.setFormatter(logging.Formatter(
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'%(asctime)s - %(levelname)s - %(message)s'
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))
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self.logger.addHandler(handler)
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# Metrics storage
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self.metrics_file = self.log_path / 'metrics.json'
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self.metrics = self._load_metrics()
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def log_recovery_attempt(
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self,
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context: RecoveryContext,
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result: RecoveryResult
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):
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"""
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Log a recovery attempt with full details.
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Args:
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context: Recovery context
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result: Recovery result
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"""
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log_entry = {
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'timestamp': datetime.now().isoformat(),
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'workflow_id': context.workflow_id,
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'node_id': context.node_id,
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'original_action': context.original_action,
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'target_element': context.target_element,
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'failure_reason': context.failure_reason,
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'attempt_count': context.attempt_count,
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'strategy_used': result.strategy_used,
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'success': result.success,
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'confidence_score': result.confidence_score,
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'execution_time': result.execution_time,
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'new_element': result.new_element,
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'requires_user_input': result.requires_user_input,
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'error_message': result.error_message
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}
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# Log to file
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if result.success:
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self.logger.info(f"Recovery SUCCESS: {json.dumps(log_entry)}")
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else:
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self.logger.warning(f"Recovery FAILED: {json.dumps(log_entry)}")
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# Update metrics
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self._update_metrics(context, result)
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def log_user_intervention(
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self,
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context: RecoveryContext,
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user_action: str,
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details: Dict
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):
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"""
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Log user intervention in recovery process.
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Args:
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context: Recovery context
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user_action: Action taken by user
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details: Additional details
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"""
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log_entry = {
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'timestamp': datetime.now().isoformat(),
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'workflow_id': context.workflow_id,
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'node_id': context.node_id,
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'user_action': user_action,
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'details': details
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}
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self.logger.info(f"User intervention: {json.dumps(log_entry)}")
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def get_recovery_statistics(
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self,
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workflow_id: Optional[str] = None
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) -> Dict:
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"""
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Get recovery statistics.
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Args:
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workflow_id: Optional workflow ID to filter by
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Returns:
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Dictionary with statistics
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"""
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metrics = self.metrics.copy()
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if workflow_id and workflow_id in metrics.get('by_workflow', {}):
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return metrics['by_workflow'][workflow_id]
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return metrics
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def generate_insights(self) -> List[str]:
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"""
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Generate insights and recommendations from recovery patterns.
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Returns:
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List of insight strings
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"""
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insights = []
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metrics = self.metrics
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# Overall success rate
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total = metrics.get('total_attempts', 0)
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successes = metrics.get('successful_recoveries', 0)
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if total > 0:
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success_rate = (successes / total) * 100
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insights.append(f"Overall recovery success rate: {success_rate:.1f}%")
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# Strategy performance
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strategy_perf = metrics.get('strategy_performance', {})
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if strategy_perf:
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best_strategy = max(
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strategy_perf.items(),
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key=lambda x: x[1].get('success_rate', 0)
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)
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insights.append(
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f"Best performing strategy: {best_strategy[0]} "
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f"({best_strategy[1].get('success_rate', 0):.1f}% success)"
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)
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# Time savings
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time_saved = metrics.get('time_saved_hours', 0)
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if time_saved > 0:
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insights.append(f"Estimated time saved: {time_saved:.1f} hours")
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# Repeated failures
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repeated_failures = self._detect_repeated_failures()
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if repeated_failures:
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insights.append(
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f"Warning: {len(repeated_failures)} workflows have repeated failures"
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)
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return insights
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def check_for_alerts(self) -> List[Dict]:
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"""
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Check for conditions that require administrator attention.
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Returns:
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List of alert dictionaries
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"""
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alerts = []
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# Check for repeated failures
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repeated_failures = self._detect_repeated_failures()
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for workflow_id, count in repeated_failures.items():
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if count >= 5:
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alerts.append({
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'severity': 'high',
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'type': 'repeated_failures',
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'workflow_id': workflow_id,
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'count': count,
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'message': f'Workflow {workflow_id} has {count} repeated failures'
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})
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# Check for low success rates
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strategy_perf = self.metrics.get('strategy_performance', {})
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for strategy, perf in strategy_perf.items():
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success_rate = perf.get('success_rate', 0)
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attempts = perf.get('attempts', 0)
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if attempts >= 10 and success_rate < 50:
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alerts.append({
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'severity': 'medium',
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'type': 'low_success_rate',
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'strategy': strategy,
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'success_rate': success_rate,
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'message': f'Strategy {strategy} has low success rate: {success_rate:.1f}%'
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})
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return alerts
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def _update_metrics(self, context: RecoveryContext, result: RecoveryResult):
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"""Update metrics with recovery result."""
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# Total attempts
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self.metrics['total_attempts'] = self.metrics.get('total_attempts', 0) + 1
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# Successful recoveries
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if result.success:
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self.metrics['successful_recoveries'] = \
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self.metrics.get('successful_recoveries', 0) + 1
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# Estimate time saved (assume 5 minutes per manual intervention)
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time_saved_hours = self.metrics.get('time_saved_hours', 0.0)
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self.metrics['time_saved_hours'] = time_saved_hours + (5.0 / 60.0)
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# Strategy performance
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if 'strategy_performance' not in self.metrics:
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self.metrics['strategy_performance'] = {}
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strategy = result.strategy_used
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if strategy not in self.metrics['strategy_performance']:
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self.metrics['strategy_performance'][strategy] = {
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'attempts': 0,
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'successes': 0,
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'success_rate': 0.0
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}
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perf = self.metrics['strategy_performance'][strategy]
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perf['attempts'] += 1
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if result.success:
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perf['successes'] += 1
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perf['success_rate'] = (perf['successes'] / perf['attempts']) * 100
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# By workflow
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if 'by_workflow' not in self.metrics:
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self.metrics['by_workflow'] = {}
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workflow_id = context.workflow_id
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if workflow_id not in self.metrics['by_workflow']:
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self.metrics['by_workflow'][workflow_id] = {
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'attempts': 0,
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'successes': 0,
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'failures': 0
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}
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wf_metrics = self.metrics['by_workflow'][workflow_id]
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wf_metrics['attempts'] += 1
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if result.success:
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wf_metrics['successes'] += 1
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else:
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wf_metrics['failures'] += 1
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# Save metrics
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self._save_metrics()
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def _detect_repeated_failures(self) -> Dict[str, int]:
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"""Detect workflows with repeated failures."""
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repeated = {}
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by_workflow = self.metrics.get('by_workflow', {})
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for workflow_id, metrics in by_workflow.items():
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failures = metrics.get('failures', 0)
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if failures >= 3:
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repeated[workflow_id] = failures
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return repeated
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def _load_metrics(self) -> Dict:
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"""Load metrics from storage."""
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if not self.metrics_file.exists():
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return {}
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try:
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with open(self.metrics_file, 'r') as f:
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return json.load(f)
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except Exception as e:
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self.logger.error(f"Error loading metrics: {e}")
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return {}
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def _save_metrics(self):
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"""Save metrics to storage."""
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try:
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with open(self.metrics_file, 'w') as f:
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json.dump(self.metrics, f, indent=2)
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except Exception as e:
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self.logger.error(f"Error saving metrics: {e}")
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