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
426
visual_workflow_builder/backend/api/analytics.py
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426
visual_workflow_builder/backend/api/analytics.py
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"""
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Analytics API endpoints for Visual Workflow Builder.
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Provides analytics data and metrics for workflows executed through the visual builder.
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Exigence: 18.3
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"""
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import sys
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from pathlib import Path
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from flask import Blueprint, request, jsonify
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional, Any
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# Ajouter le chemin racine pour importer les modules core
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sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent))
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try:
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from core.analytics.analytics_system import get_analytics_system
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from core.analytics.integration.execution_integration import get_analytics_integration
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ANALYTICS_AVAILABLE = True
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except ImportError:
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ANALYTICS_AVAILABLE = False
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from services.execution_integration import get_executor
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from services.serialization import WorkflowDatabase
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# Blueprint pour les endpoints Analytics
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analytics_bp = Blueprint('analytics', __name__)
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@analytics_bp.route('/workflow/<workflow_id>/metrics', methods=['GET'])
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def get_workflow_metrics(workflow_id: str):
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"""
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Récupère les métriques d'un workflow.
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Exigence: 18.3
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Query Parameters:
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- hours: Fenêtre de temps en heures (défaut: 24)
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- metric_type: Type de métrique (execution, step, performance)
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"""
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try:
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if not ANALYTICS_AVAILABLE:
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return jsonify({
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'success': False,
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'error': 'Analytics system not available'
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}), 503
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hours = int(request.args.get('hours', 24))
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metric_type = request.args.get('metric_type', 'execution')
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# Récupérer les métriques via l'exécuteur
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executor = get_executor()
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analytics_data = executor.get_workflow_analytics(workflow_id, hours)
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if analytics_data is None:
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return jsonify({
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'success': False,
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'error': 'No analytics data available'
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}), 404
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return jsonify({
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'success': True,
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'workflow_id': workflow_id,
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'time_window_hours': hours,
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'metric_type': metric_type,
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'data': analytics_data
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})
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except Exception as e:
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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@analytics_bp.route('/workflow/<workflow_id>/performance', methods=['GET'])
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def get_workflow_performance(workflow_id: str):
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"""
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Récupère les métriques de performance d'un workflow.
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Exigence: 18.3
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"""
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try:
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if not ANALYTICS_AVAILABLE:
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return jsonify({
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'success': False,
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'error': 'Analytics system not available'
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}), 503
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hours = int(request.args.get('hours', 24))
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analytics_system = get_analytics_system()
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# Calculer la fenêtre de temps
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end_time = datetime.now()
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start_time = end_time - timedelta(hours=hours)
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# Analyser les performances
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performance_stats = analytics_system.performance_analyzer.analyze_performance(
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workflow_id=workflow_id,
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start_time=start_time,
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end_time=end_time
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)
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# Calculer le taux de succès
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success_stats = analytics_system.success_rate_calculator.calculate_success_rate(
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workflow_id=workflow_id,
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time_window_hours=hours
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)
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return jsonify({
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'success': True,
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'workflow_id': workflow_id,
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'time_window_hours': hours,
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'performance': performance_stats.to_dict() if performance_stats else None,
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'success_rate': success_stats.to_dict() if success_stats else None
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})
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except Exception as e:
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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@analytics_bp.route('/workflow/<workflow_id>/executions', methods=['GET'])
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def get_workflow_executions(workflow_id: str):
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"""
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Récupère l'historique des exécutions d'un workflow.
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Exigence: 18.3
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"""
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try:
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executor = get_executor()
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executions = executor.list_executions(workflow_id=workflow_id)
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# Ajouter des métriques calculées
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for execution in executions:
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if execution.get('analytics_data'):
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# Enrichir avec des métriques calculées
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execution['calculated_metrics'] = _calculate_execution_metrics(execution)
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return jsonify({
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'success': True,
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'workflow_id': workflow_id,
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'executions': executions,
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'total_count': len(executions)
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})
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except Exception as e:
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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@analytics_bp.route('/dashboard/workflows', methods=['GET'])
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def get_workflows_dashboard():
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"""
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Récupère les données du dashboard pour tous les workflows.
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Exigence: 18.3
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"""
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try:
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if not ANALYTICS_AVAILABLE:
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return jsonify({
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'success': False,
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'error': 'Analytics system not available'
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}), 503
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hours = int(request.args.get('hours', 24))
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# Récupérer tous les workflows
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try:
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db = WorkflowDatabase()
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all_workflows = db.list_workflows()
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except Exception as e:
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return jsonify({
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'success': False,
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'error': f'Database error: {str(e)}'
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}), 500
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dashboard_data = {
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'summary': {
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'total_workflows': len(all_workflows),
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'time_window_hours': hours,
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'generated_at': datetime.now().isoformat()
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},
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'workflows': []
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}
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try:
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analytics_system = get_analytics_system()
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except Exception as e:
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return jsonify({
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'success': False,
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'error': f'Analytics system error: {str(e)}'
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}), 503
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# Collecter les métriques pour chaque workflow
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for workflow_info in all_workflows:
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workflow_id = workflow_info['workflow_id']
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try:
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# Métriques de performance
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end_time = datetime.now()
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start_time = end_time - timedelta(hours=hours)
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performance_stats = analytics_system.performance_analyzer.analyze_performance(
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workflow_id=workflow_id,
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start_time=start_time,
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end_time=end_time
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)
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success_stats = analytics_system.success_rate_calculator.calculate_success_rate(
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workflow_id=workflow_id,
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time_window_hours=hours
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)
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# Exécutions récentes
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executor = get_executor()
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recent_executions = executor.list_executions(workflow_id=workflow_id)[:5] # 5 plus récentes
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workflow_metrics = {
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'workflow_id': workflow_id,
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'name': workflow_info.get('name', 'Unnamed Workflow'),
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'performance': performance_stats.to_dict() if performance_stats else None,
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'success_rate': success_stats.to_dict() if success_stats else None,
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'recent_executions': recent_executions,
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'last_execution': recent_executions[0] if recent_executions else None
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}
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dashboard_data['workflows'].append(workflow_metrics)
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except Exception as e:
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# Continuer même si un workflow échoue
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dashboard_data['workflows'].append({
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'workflow_id': workflow_id,
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'name': workflow_info.get('name', 'Unnamed Workflow'),
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'error': str(e)
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})
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return jsonify({
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'success': True,
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'dashboard': dashboard_data
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})
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except Exception as e:
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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@analytics_bp.route('/dashboard/summary', methods=['GET'])
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def get_dashboard_summary():
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"""
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Récupère un résumé global des métriques.
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Exigence: 18.3
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"""
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try:
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if not ANALYTICS_AVAILABLE:
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return jsonify({
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'success': False,
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'error': 'Analytics system not available'
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}), 503
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hours = int(request.args.get('hours', 24))
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analytics_system = get_analytics_system()
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executor = get_executor()
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# Statistiques globales
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end_time = datetime.now()
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start_time = end_time - timedelta(hours=hours)
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# Compter les exécutions totales
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all_executions = executor.list_executions()
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recent_executions = [
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exec for exec in all_executions
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if exec.get('start_time') and
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datetime.fromisoformat(exec['start_time']) >= start_time
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]
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successful_executions = [
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exec for exec in recent_executions
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if exec.get('status') == 'completed'
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]
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failed_executions = [
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exec for exec in recent_executions
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if exec.get('status') == 'failed'
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]
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# Calculer les métriques de résumé
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total_executions = len(recent_executions)
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success_rate = (len(successful_executions) / total_executions * 100) if total_executions > 0 else 0
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# Durée moyenne
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durations = [
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exec.get('duration_ms', 0) for exec in successful_executions
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if exec.get('duration_ms')
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]
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avg_duration = sum(durations) / len(durations) if durations else 0
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summary = {
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'time_window_hours': hours,
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'total_executions': total_executions,
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'successful_executions': len(successful_executions),
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'failed_executions': len(failed_executions),
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'success_rate_percent': round(success_rate, 2),
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'average_duration_ms': round(avg_duration, 2),
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'generated_at': datetime.now().isoformat()
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}
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return jsonify({
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'success': True,
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'summary': summary
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})
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except Exception as e:
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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@analytics_bp.route('/insights', methods=['GET'])
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def get_analytics_insights():
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"""
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Récupère les insights Analytics générés automatiquement.
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Exigence: 18.3
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"""
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try:
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if not ANALYTICS_AVAILABLE:
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return jsonify({
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'success': False,
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'error': 'Analytics system not available'
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}), 503
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hours = int(request.args.get('hours', 168)) # 1 semaine par défaut
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try:
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analytics_system = get_analytics_system()
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except Exception as e:
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return jsonify({
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'success': False,
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'error': f'Analytics system error: {str(e)}'
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}), 503
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end_time = datetime.now()
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start_time = end_time - timedelta(hours=hours)
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# Générer les insights
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try:
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insights = analytics_system.insight_generator.generate_insights(
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start_time=start_time,
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end_time=end_time
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)
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except Exception as e:
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return jsonify({
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'success': False,
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'error': f'Insights generation error: {str(e)}'
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}), 500
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return jsonify({
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'success': True,
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'time_window_hours': hours,
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'insights': [insight.to_dict() for insight in insights],
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'generated_at': datetime.now().isoformat()
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})
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except Exception as e:
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return jsonify({
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'success': False,
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'error': str(e)
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}), 500
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def _calculate_execution_metrics(execution: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Calcule des métriques supplémentaires pour une exécution.
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Args:
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execution: Données d'exécution
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Returns:
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Métriques calculées
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"""
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metrics = {}
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try:
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# Efficacité (steps completed / steps total)
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steps_completed = execution.get('steps_completed', 0)
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steps_total = execution.get('steps_total', 0)
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if steps_total > 0:
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metrics['efficiency_percent'] = round((steps_completed / steps_total) * 100, 2)
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# Vitesse (steps par seconde)
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duration_ms = execution.get('duration_ms', 0)
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if duration_ms > 0 and steps_completed > 0:
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duration_sec = duration_ms / 1000
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metrics['steps_per_second'] = round(steps_completed / duration_sec, 2)
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# Statut de santé
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if execution.get('status') == 'completed':
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metrics['health_status'] = 'healthy'
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elif execution.get('status') == 'failed':
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metrics['health_status'] = 'unhealthy'
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else:
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metrics['health_status'] = 'unknown'
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except Exception as e:
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metrics['calculation_error'] = str(e)
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return metrics
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# Fonction pour enregistrer le blueprint
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def register_analytics_blueprint(app):
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"""Enregistre le blueprint Analytics dans l'application Flask."""
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app.register_blueprint(analytics_bp)
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Reference in New Issue
Block a user