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test_workflow_fix.py
Executable file
214
test_workflow_fix.py
Executable file
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#!/usr/bin/env python3
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"""
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Test rapide pour vérifier que le fix de workflow fonctionne.
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"""
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import sys
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from pathlib import Path
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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.vision_analysis import VisionAnalysis
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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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import numpy as np
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def test_vision_analysis_fix():
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"""Test que VisionAnalysis n'a plus le bug d'attribut."""
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print("\n🧪 Test 1: VisionAnalysis attribute fix")
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print("=" * 60)
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try:
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logger = Logger()
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embeddings = EmbeddingsManager()
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vision = VisionUtils()
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llm = LLMManager(logger=logger, fallback_to_vision=True)
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analyzer = VisionAnalysis(
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embeddings_manager=embeddings,
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vision_utils=vision,
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llm_manager=llm,
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logger=logger
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)
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# Créer un screenshot factice
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screenshot = np.random.randint(0, 255, (800, 600, 3), dtype=np.uint8)
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# Analyser une action
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signature = analyzer.analyze_action(
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screenshot=screenshot,
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x=100,
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y=100,
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action_type="mouse_click",
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window="Test Window"
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)
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# Vérifier que la signature est valide
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assert signature is not None, "Signature should not be None"
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assert "position" in signature, "Signature should have position"
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assert "embedding" in signature, "Signature should have embedding"
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assert "element_type" in signature, "Signature should have element_type"
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print("✅ VisionAnalysis fonctionne correctement")
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print(f" - Position: {signature['position']}")
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print(f" - Element type: {signature['element_type']}")
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print(f" - Has embedding: {signature['embedding'] is not None}")
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return True
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except AttributeError as e:
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print(f"❌ AttributeError détecté: {e}")
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return False
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except Exception as e:
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print(f"⚠️ Autre erreur: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_orchestrator_integration():
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"""Test que l'Orchestrator a bien SessionManager et WorkflowDetector."""
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print("\n🧪 Test 2: Orchestrator integration")
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print("=" * 60)
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try:
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from core.orchestrator import Orchestrator
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from core.learning_manager import LearningManager
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logger = Logger()
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config = get_config()
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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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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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# Vérifier que les composants sont présents
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assert hasattr(orchestrator, 'session_manager'), "Orchestrator should have session_manager"
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assert hasattr(orchestrator, 'workflow_detector'), "Orchestrator should have workflow_detector"
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assert orchestrator.session_manager is not None, "session_manager should not be None"
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assert orchestrator.workflow_detector is not None, "workflow_detector should not be None"
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# Vérifier que EventCapture utilise les mêmes instances
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assert orchestrator.event_capture.session_manager is orchestrator.session_manager, \
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"EventCapture should use Orchestrator's session_manager"
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assert orchestrator.event_capture.workflow_detector is orchestrator.workflow_detector, \
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"EventCapture should use Orchestrator's workflow_detector"
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print("✅ Orchestrator intégration OK")
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print(f" - SessionManager: {orchestrator.session_manager}")
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print(f" - WorkflowDetector: {orchestrator.workflow_detector}")
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print(f" - EventCapture uses same instances: ✓")
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return True
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except Exception as e:
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print(f"❌ Erreur: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_suggestion_manager_workflow():
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"""Test que SuggestionManager a la méthode on_workflow_detected."""
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print("\n🧪 Test 3: SuggestionManager workflow support")
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print("=" * 60)
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try:
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from core.suggestion_manager import SuggestionManager
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from core.learning_manager import LearningManager
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logger = Logger()
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config = get_config()
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embeddings = EmbeddingsManager()
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learning = LearningManager(embeddings, logger, config)
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suggestion_mgr = SuggestionManager(
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learning_manager=learning,
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embeddings_manager=embeddings,
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logger=logger,
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config=config
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)
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# Vérifier que la méthode existe
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assert hasattr(suggestion_mgr, 'on_workflow_detected'), \
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"SuggestionManager should have on_workflow_detected method"
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# Tester l'appel
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test_workflow = {
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"workflow_id": "test_123",
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"name": "Test Workflow",
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"confidence": 0.85
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}
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suggestion_mgr.on_workflow_detected(test_workflow)
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print("✅ SuggestionManager workflow support OK")
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print(f" - on_workflow_detected method exists: ✓")
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print(f" - Method callable: ✓")
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return True
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except Exception as e:
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print(f"❌ Erreur: {e}")
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import traceback
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traceback.print_exc()
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return False
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def main():
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"""Point d'entrée principal."""
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print("\n" + "=" * 60)
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print("🔧 TEST DU FIX DE WORKFLOW DETECTION")
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print("=" * 60)
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results = []
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# Test 1: VisionAnalysis fix
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results.append(("VisionAnalysis fix", test_vision_analysis_fix()))
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# Test 2: Orchestrator integration
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results.append(("Orchestrator integration", test_orchestrator_integration()))
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# Test 3: SuggestionManager workflow
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results.append(("SuggestionManager workflow", test_suggestion_manager_workflow()))
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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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passed = sum(1 for _, result in results if result)
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total = len(results)
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for name, result in results:
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status = "✅ PASS" if result else "❌ FAIL"
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print(f"{status}: {name}")
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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💡 Prochaine étape:")
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print(" Lance GeniusIA et teste avec la calculatrice:")
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print(" cd geniusia2 && ./run.sh --mode shadow")
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return 0
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else:
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print("\n⚠️ Certains tests ont échoué")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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