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test_assisted_mode_integration.py
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test_assisted_mode_integration.py
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#!/usr/bin/env python3
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"""
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Test d'intégration du Mode Assisté avec suggestions de workflows.
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Vérifie que l'Orchestrator détecte les workflows et crée des suggestions.
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"""
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import sys
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from pathlib import Path
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from datetime import datetime, timedelta
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sys.path.insert(0, str(Path(__file__).parent / "geniusia2"))
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from core.logger import Logger
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from core.config import get_config
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from core.orchestrator import Orchestrator
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from core.learning_manager import LearningManager
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from core.embeddings_manager import EmbeddingsManager
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from core.utils.vision_utils import VisionUtils
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from core.llm_manager import LLMManager
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def test_workflow_suggestion_integration():
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"""Test que l'Orchestrator crée des suggestions de workflows"""
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print("\n" + "="*60)
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print("🧪 TEST: Intégration Mode Assisté + Workflows")
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print("="*60)
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logger = Logger()
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config = get_config()
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# Créer les composants
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embeddings = EmbeddingsManager()
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learning = LearningManager(embeddings, logger, config)
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vision = VisionUtils()
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llm = LLMManager(logger=logger, fallback_to_vision=True)
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# Créer l'Orchestrator
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orchestrator = Orchestrator(
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learning_manager=learning,
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vision_utils=vision,
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llm_manager=llm,
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logger=logger,
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gui=None,
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config=config
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)
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# Mettre en mode Assist
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learning.set_mode("assist")
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print("\n📋 Phase 1: Apprentissage d'un workflow...")
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# Simuler 3 répétitions d'un workflow
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for i in range(3):
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base_time = datetime.now() - timedelta(hours=3-i)
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actions = [
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{
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"action_type": "click",
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"position": [100, 100],
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"window": "Calculatrice",
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"timestamp": base_time.isoformat(),
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"description": "Ouvrir calculatrice"
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},
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{
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"action_type": "type",
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"text": "2+2",
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"window": "Calculatrice",
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"timestamp": (base_time + timedelta(seconds=1)).isoformat(),
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"description": "Taper calcul"
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},
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{
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"action_type": "click",
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"position": [150, 150],
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"window": "Calculatrice",
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"timestamp": (base_time + timedelta(seconds=2)).isoformat(),
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"description": "Cliquer égal"
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}
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]
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for action in actions:
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orchestrator.session_manager.add_action(action)
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orchestrator.session_manager.force_finalize_session()
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print(f" ✅ Répétition {i+1} enregistrée")
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# Analyser les sessions pour détecter le workflow
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sessions = orchestrator.session_manager.get_recent_sessions(10)
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orchestrator.workflow_detector.analyze_sessions(sessions)
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workflows = orchestrator.workflow_detector.get_workflows()
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print(f"\n📊 Workflows détectés: {len(workflows)}")
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if not workflows:
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print(" ❌ Aucun workflow détecté")
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return False
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workflow = workflows[0]
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print(f" ✅ Workflow: {workflow.name}")
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print(f" Confiance: {workflow.confidence:.2%}")
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print(f" Étapes: {len(workflow.steps)}")
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print("\n📋 Phase 2: Simulation du début du workflow...")
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# Simuler les 2 premières actions du workflow
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base_time = datetime.now()
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action1 = {
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"action_type": "click",
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"position": [100, 100],
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"window": "Calculatrice",
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"timestamp": base_time.isoformat(),
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"description": "Ouvrir calculatrice"
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}
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orchestrator.session_manager.add_action(action1)
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print(f" ✅ Action 1: {action1['description']}")
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action2 = {
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"action_type": "type",
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"text": "2+2",
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"window": "Calculatrice",
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"timestamp": (base_time + timedelta(seconds=1)).isoformat(),
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"description": "Taper calcul"
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}
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orchestrator.session_manager.add_action(action2)
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print(f" ✅ Action 2: {action2['description']}")
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print("\n🔍 Phase 3: Vérification de suggestion...")
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# Vérifier s'il y a un match de workflow
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workflow_match = orchestrator._check_workflow_match()
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if workflow_match:
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print(f"\n 🎉 Match trouvé !")
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print(f" Workflow: {workflow_match.workflow_name}")
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print(f" Confiance: {workflow_match.confidence:.2%}")
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print(f" Étapes matchées: {workflow_match.matched_steps}/{workflow_match.total_steps}")
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print(f" Étapes restantes: {len(workflow_match.remaining_steps)}")
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# Créer une suggestion
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suggestion = orchestrator.suggestion_manager.create_workflow_suggestion(workflow_match)
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if suggestion:
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print(f"\n 💡 Suggestion créée !")
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print(f" Type: {suggestion['type']}")
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print(f" Workflow: {suggestion['workflow_name']}")
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print(f" Prochaine étape: {suggestion['next_steps_preview'][0]['action_type']}")
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print(f" Confiance: {suggestion['confidence']:.2%}")
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return True
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else:
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print(f"\n ⚠️ Match trouvé mais suggestion non créée (confiance trop faible ?)")
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return False
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else:
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print(f"\n ❌ Aucun match de workflow trouvé")
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return False
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def test_orchestrator_check_for_suggestions():
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"""Test que check_for_suggestions() détecte les workflows"""
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print("\n" + "="*60)
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print("🧪 TEST: check_for_suggestions() avec workflows")
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print("="*60)
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logger = Logger()
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config = get_config()
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# Créer les composants
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embeddings = EmbeddingsManager()
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learning = LearningManager(embeddings, logger, config)
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vision = VisionUtils()
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llm = LLMManager(logger=logger, fallback_to_vision=True)
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# Créer l'Orchestrator
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orchestrator = Orchestrator(
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learning_manager=learning,
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vision_utils=vision,
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llm_manager=llm,
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logger=logger,
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gui=None,
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config=config
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)
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# Mettre en mode Assist
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learning.set_mode("assist")
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print("\n📋 Préparation: Créer un workflow...")
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# Créer un workflow manuellement
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from core.workflow_detector import Workflow, WorkflowStep
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workflow = Workflow(
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workflow_id="test_workflow_001",
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name="Test Workflow",
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steps=[
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WorkflowStep(
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step_id=0,
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action_type="click",
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target_description="Action 1",
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position=(100, 100),
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window="TestApp"
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),
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WorkflowStep(
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step_id=1,
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action_type="type",
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target_description="Action 2",
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position=(0, 0),
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window="TestApp"
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),
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WorkflowStep(
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step_id=2,
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action_type="click",
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target_description="Action 3",
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position=(200, 200),
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window="TestApp"
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)
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],
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repetitions=5,
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confidence=0.95,
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last_seen=datetime.now()
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)
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orchestrator.workflow_detector.workflows.append(workflow)
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print(f" ✅ Workflow créé: {workflow.name}")
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print("\n📋 Simulation: Début du workflow...")
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# Simuler les 2 premières actions
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base_time = datetime.now()
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actions = [
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{
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"action_type": "click",
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"position": [100, 100],
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"window": "TestApp",
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"timestamp": base_time.isoformat()
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},
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{
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"action_type": "type",
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"position": [0, 0],
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"window": "TestApp",
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"timestamp": (base_time + timedelta(seconds=1)).isoformat()
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}
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]
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for action in actions:
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orchestrator.session_manager.add_action(action)
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print(f" ✅ {len(actions)} actions simulées")
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print("\n🔍 Appel de check_for_suggestions()...")
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# Appeler check_for_suggestions
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orchestrator.check_for_suggestions()
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# Vérifier s'il y a une suggestion
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suggestion = orchestrator.suggestion_manager.get_current_suggestion()
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if suggestion:
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print(f"\n 🎉 Suggestion créée automatiquement !")
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print(f" Type: {suggestion['type']}")
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print(f" Workflow: {suggestion.get('workflow_name', 'N/A')}")
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print(f" Confiance: {suggestion['confidence']:.2%}")
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if suggestion['type'] == 'workflow':
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print(f" Étape courante: {suggestion['current_step']}/{suggestion['total_steps']}")
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print(f" Étapes restantes: {len(suggestion['remaining_steps'])}")
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return True
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else:
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print(f"\n ❌ Aucune suggestion créée")
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return False
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def main():
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"""Exécute tous les tests"""
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print("\n" + "="*60)
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print("🚀 TEST D'INTÉGRATION MODE ASSISTÉ")
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print("="*60)
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results = []
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try:
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# Test 1: Intégration complète
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results.append(("Intégration workflow → suggestion", test_workflow_suggestion_integration()))
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except Exception as e:
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print(f"\n❌ ERREUR Test 1: {e}")
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import traceback
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traceback.print_exc()
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results.append(("Intégration workflow → suggestion", False))
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try:
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# Test 2: check_for_suggestions()
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results.append(("check_for_suggestions()", test_orchestrator_check_for_suggestions()))
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except Exception as e:
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print(f"\n❌ ERREUR Test 2: {e}")
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import traceback
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traceback.print_exc()
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results.append(("check_for_suggestions()", False))
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# Résumé
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print("\n" + "="*60)
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print("📊 RÉSUMÉ DES TESTS")
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print("="*60)
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for name, success in results:
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status = "✅" if success else "❌"
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print(f"{status} {name}")
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total = len(results)
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passed = sum(1 for _, success in results if success)
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print(f"\n📈 Score: {passed}/{total} tests réussis")
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if passed == total:
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print("\n🎉 TOUS LES TESTS SONT PASSÉS !")
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print("\n💡 Le Mode Assisté avec workflows est intégré !")
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print("\n🚀 Prochaine étape: Améliorer la GUI pour afficher les suggestions")
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return 0
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else:
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print(f"\n⚠️ {total - passed} test(s) échoué(s)")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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