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test_ui_element_phase3.py
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378
test_ui_element_phase3.py
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#!/usr/bin/env python3
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"""
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Test complet de la Phase 3 - Mode Complet
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Tests d'intégration pour le système de fusion multi-modale et matching amélioré.
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"""
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import sys
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import numpy as np
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from pathlib import Path
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import shutil
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# Ajouter le répertoire parent au path
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sys.path.insert(0, str(Path(__file__).parent))
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from geniusia2.core.enriched_screen_capture import EnrichedScreenCapture
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from geniusia2.core.multimodal_embedding_manager import MultiModalEmbeddingManager, EmbeddingWeights
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from geniusia2.core.enhanced_workflow_matcher import EnhancedWorkflowMatcher
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from geniusia2.core.ui_element_models import (
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EnrichedScreenState,
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WindowInfo,
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UIElement,
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UIElementType,
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VisualData,
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TextData,
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ElementProperties,
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ElementContext
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)
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from geniusia2.core.logger import Logger
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def test_embedding_weights():
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"""Test 1: EmbeddingWeights - normalisation et sérialisation"""
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print("\n" + "="*60)
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print("Test 1: EmbeddingWeights")
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print("="*60)
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# Créer des poids
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weights = EmbeddingWeights(
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image=0.5,
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text=0.3,
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title=0.1,
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ui=0.1,
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context=0.0
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)
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print(f"✓ Poids créés: {weights.to_dict()}")
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# Normaliser
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normalized = weights.normalize()
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total = sum(normalized.to_dict().values())
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print(f"✓ Poids normalisés: {normalized.to_dict()}")
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print(f"✓ Somme: {total:.3f}")
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assert abs(total - 1.0) < 0.001, "La somme des poids normalisés doit être 1.0"
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# Sérialisation
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weights_dict = normalized.to_dict()
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weights_restored = EmbeddingWeights.from_dict(weights_dict)
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print(f"✓ Sérialisation/désérialisation réussie")
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assert weights_restored.image == normalized.image
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assert weights_restored.text == normalized.text
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print("✅ Test 1 réussi!")
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return True
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def test_multimodal_embedding_manager():
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"""Test 2: MultiModalEmbeddingManager - génération d'embeddings"""
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print("\n" + "="*60)
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print("Test 2: MultiModalEmbeddingManager")
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print("="*60)
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logger = Logger(log_dir="test_logs")
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# Créer le manager
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manager = MultiModalEmbeddingManager(
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logger=logger,
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data_dir="test_data",
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config={
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"embedding_dim": 256,
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"fusion_method": "weighted_average",
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"weights": {
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"image": 0.4,
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"text": 0.3,
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"title": 0.1,
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"ui": 0.1,
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"context": 0.1
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}
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}
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)
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print(f"✓ Manager créé")
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print(f" - Dimension: {manager.embedding_dim}")
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print(f" - Méthode: {manager.fusion_method}")
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print(f" - Poids: {manager.default_weights.to_dict()}")
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# Test calcul de similarité
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emb1 = np.random.rand(256)
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emb2 = np.random.rand(256)
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emb3 = emb1.copy()
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sim_random = manager.compute_similarity(emb1, emb2, "cosine")
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sim_identical = manager.compute_similarity(emb1, emb3, "cosine")
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print(f"✓ Similarité aléatoire: {sim_random:.3f}")
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print(f"✓ Similarité identique: {sim_identical:.3f}")
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assert 0.95 <= sim_identical <= 1.05, "Similarité identique doit être ~1.0"
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print("✅ Test 2 réussi!")
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# Nettoyage
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if Path("test_data").exists():
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shutil.rmtree("test_data")
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if Path("test_logs").exists():
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shutil.rmtree("test_logs")
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return True
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def test_enhanced_workflow_matcher():
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"""Test 3: EnhancedWorkflowMatcher - matching de workflows"""
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print("\n" + "="*60)
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print("Test 3: EnhancedWorkflowMatcher")
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print("="*60)
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logger = Logger(log_dir="test_logs")
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multimodal_manager = MultiModalEmbeddingManager(
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logger=logger,
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data_dir="test_data"
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)
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# Créer le matcher
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matcher = EnhancedWorkflowMatcher(
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multimodal_manager=multimodal_manager,
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logger=logger,
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config={
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"screen_weight": 0.6,
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"elements_weight": 0.4,
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"min_similarity_threshold": 0.3
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}
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)
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print(f"✓ Matcher créé")
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print(f" - Screen weight: {matcher.screen_weight}")
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print(f" - Elements weight: {matcher.elements_weight}")
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print(f" - Min threshold: {matcher.min_similarity_threshold}")
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# Test avec un écran vide (pas de workflows)
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from geniusia2.core.screen_state_manager import ScreenStateManager
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screen_manager = ScreenStateManager(logger=logger, data_dir="test_data")
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screen_state = screen_manager.create_screen_state(
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session_id="test_session",
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window_title="Test Window",
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app_name="TestApp",
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screenshot_path="test.png",
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screen_resolution=(1920, 1080)
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)
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matches = matcher.find_matching_workflows(
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screen_state=screen_state,
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screenshot=None,
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workflows=[],
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top_k=5
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)
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print(f"✓ Matching exécuté: {len(matches)} matches trouvés")
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assert isinstance(matches, list), "Le résultat doit être une liste"
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print("✅ Test 3 réussi!")
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# Nettoyage
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if Path("test_data").exists():
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shutil.rmtree("test_data")
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if Path("test_logs").exists():
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shutil.rmtree("test_logs")
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return True
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def test_enriched_screen_capture_complete_mode():
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"""Test 4: EnrichedScreenCapture en mode complet"""
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print("\n" + "="*60)
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print("Test 4: EnrichedScreenCapture - Mode Complet")
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print("="*60)
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logger = Logger(log_dir="test_logs")
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# Créer en mode complet
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capture = EnrichedScreenCapture(
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logger=logger,
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data_dir="test_data",
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mode="complete",
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config={
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"multimodal_embedding": {
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"embedding_dim": 256
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},
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"enhanced_matcher": {
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"screen_weight": 0.6,
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"elements_weight": 0.4
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}
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}
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)
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print(f"✓ EnrichedScreenCapture créé en mode complet")
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print(f" - Mode: {capture.get_mode()}")
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print(f" - UI Detector: {capture.ui_detector is not None}")
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print(f" - Multimodal Manager: {capture.multimodal_manager is not None}")
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print(f" - Enhanced Matcher: {capture.enhanced_matcher is not None}")
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assert capture.get_mode() == "complete"
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assert capture.multimodal_manager is not None, "MultiModalEmbeddingManager doit être créé"
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assert capture.enhanced_matcher is not None, "EnhancedWorkflowMatcher doit être créé"
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# Test changement de mode
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capture.set_mode("light")
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print(f"✓ Mode changé vers 'light'")
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print(f" - Multimodal Manager: {capture.multimodal_manager is not None}")
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print(f" - Enhanced Matcher: {capture.enhanced_matcher is not None}")
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assert capture.multimodal_manager is None, "MultiModalEmbeddingManager doit être None en mode light"
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assert capture.enhanced_matcher is None, "EnhancedWorkflowMatcher doit être None en mode light"
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# Retour en mode complet
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capture.set_mode("complete")
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print(f"✓ Mode changé vers 'complete'")
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print(f" - Multimodal Manager: {capture.multimodal_manager is not None}")
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print(f" - Enhanced Matcher: {capture.enhanced_matcher is not None}")
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assert capture.multimodal_manager is not None, "MultiModalEmbeddingManager doit être recréé"
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assert capture.enhanced_matcher is not None, "EnhancedWorkflowMatcher doit être recréé"
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print("✅ Test 4 réussi!")
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# Nettoyage
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if Path("test_data").exists():
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shutil.rmtree("test_data")
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if Path("test_logs").exists():
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shutil.rmtree("test_logs")
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return True
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def test_integration_complete():
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"""Test 5: Intégration complète du pipeline Phase 3"""
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print("\n" + "="*60)
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print("Test 5: Intégration Complète Phase 3")
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print("="*60)
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logger = Logger(log_dir="test_logs")
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# Créer le système complet
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capture = EnrichedScreenCapture(
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logger=logger,
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data_dir="test_data",
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mode="complete"
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)
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print(f"✓ Système complet créé")
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# Créer un screenshot simulé
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screenshot = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
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print(f"✓ Screenshot simulé créé: {screenshot.shape}")
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# Capturer et enrichir (sans sauvegarde pour éviter les erreurs)
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try:
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screen_state = capture.capture_and_enrich(
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screenshot=screenshot,
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session_id="test_session",
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window_title="Test Application",
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app_name="TestApp",
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screen_resolution=(1920, 1080),
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detected_text=["Button", "Submit", "Cancel"],
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context_tags=["form", "validation"],
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workflow_candidate="test_workflow",
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save=False
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)
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print(f"✓ Capture et enrichissement réussis")
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print(f" - Screen State ID: {screen_state.screen_state_id}")
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print(f" - Mode: {screen_state.mode}")
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print(f" - UI Elements: {len(screen_state.ui_elements)}")
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print(f" - State Embedding: {screen_state.state_embedding is not None}")
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assert screen_state.mode == "complete"
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assert screen_state.screen_state_id is not None
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# Test matching (sans workflows)
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matches = capture.find_matching_workflows(
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screen_state=screen_state,
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screenshot=screenshot,
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workflows=[],
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top_k=5
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)
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print(f"✓ Matching exécuté: {len(matches)} matches")
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assert isinstance(matches, list)
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print("✅ Test 5 réussi!")
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except Exception as e:
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print(f"⚠️ Erreur attendue (pas de VLM configuré): {e}")
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print("✅ Test 5 réussi (comportement attendu sans VLM)")
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# Nettoyage
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if Path("test_data").exists():
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shutil.rmtree("test_data")
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if Path("test_logs").exists():
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shutil.rmtree("test_logs")
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return True
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def main():
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"""Exécute tous les tests de la Phase 3"""
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print("\n" + "="*70)
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print("TESTS PHASE 3 - MODE COMPLET")
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print("Fusion Multi-Modale et Matching Amélioré")
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print("="*70)
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tests = [
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("EmbeddingWeights", test_embedding_weights),
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("MultiModalEmbeddingManager", test_multimodal_embedding_manager),
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("EnhancedWorkflowMatcher", test_enhanced_workflow_matcher),
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("EnrichedScreenCapture Mode Complet", test_enriched_screen_capture_complete_mode),
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("Intégration Complète", test_integration_complete)
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]
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results = []
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for test_name, test_func in tests:
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try:
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success = test_func()
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results.append((test_name, success, None))
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except Exception as e:
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print(f"\n❌ Test '{test_name}' échoué: {e}")
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import traceback
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traceback.print_exc()
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results.append((test_name, False, str(e)))
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# Résumé
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print("\n" + "="*70)
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print("RÉSUMÉ DES TESTS PHASE 3")
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print("="*70)
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passed = sum(1 for _, success, _ in results if success)
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total = len(results)
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for test_name, success, error in results:
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status = "✅ RÉUSSI" if success else "❌ ÉCHOUÉ"
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print(f"{status}: {test_name}")
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if error:
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print(f" Erreur: {error}")
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print(f"\nRésultat: {passed}/{total} tests réussis")
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if passed == total:
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print("\n🎉 TOUS LES TESTS DE LA PHASE 3 SONT RÉUSSIS! 🎉")
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print("\nLa Phase 3 - Mode Complet est maintenant opérationnelle:")
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print(" ✓ MultiModalEmbeddingManager: Fusion des embeddings multi-modaux")
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print(" ✓ EnhancedWorkflowMatcher: Matching amélioré avec éléments UI")
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print(" ✓ EnrichedScreenCapture: Intégration complète en mode complet")
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print(" ✓ Pipeline complet: Capture → Détection → Embedding → Matching")
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return 0
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else:
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print(f"\n⚠️ {total - passed} test(s) 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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