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>

examples/test_phase7_simple.py

@@ -0,0 +1,151 @@
+#!/usr/bin/env python3
+"""Test Phase 7 - Learning System (Simplified)"""
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent))
+import logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def test_imports():
+    logger.info("\n=== Testing Imports ===")
+    try:
+        from core.learning.learning_manager import LearningManager, WorkflowStats
+        logger.info("✓ LearningManager imported")
+        from core.learning.feedback_processor import FeedbackProcessor, FeedbackType
+        logger.info("✓ FeedbackProcessor imported")
+        from core.models.workflow_graph import LearningState
+        logger.info("✓ LearningState imported")
+        return True
+    except Exception as e:
+        logger.error(f"✗ Import failed: {e}")
+        return False
+
+def test_learning_manager_basic():
+    logger.info("\n=== Testing LearningManager Basic ===")
+    try:
+        from core.learning.learning_manager import LearningManager, WorkflowStats
+        from core.models.workflow_graph import LearningState
+        
+        manager = LearningManager()
+        logger.info("✓ LearningManager created")
+        
+        # Create stats directly
+        stats = WorkflowStats(
+            workflow_id="test_wf_1",
+            learning_state=LearningState.OBSERVATION
+        )
+        manager.workflows["test_wf_1"] = stats
+        logger.info("✓ Workflow stats created")
+        
+        # Test observations
+        for i in range(5):
+            manager.record_observation("test_wf_1")
+            stats.confidence_scores.append(0.92)
+        
+        state = manager.get_workflow_state("test_wf_1")
+        logger.info(f"  After 5 observations: {state.value}")
+        
+        # Test executions
+        for i in range(10):
+            manager.record_execution("test_wf_1", success=True, confidence=0.93)
+        
+        state = manager.get_workflow_state("test_wf_1")
+        logger.info(f"  After 10 executions: {state.value}")
+        
+        stats_result = manager.get_workflow_stats("test_wf_1")
+        logger.info(f"  Stats: success_rate={stats_result.success_rate:.2f}")
+        
+        return True
+    except Exception as e:
+        logger.error(f"✗ Test failed: {e}", exc_info=True)
+        return False
+
+def test_feedback_processor():
+    logger.info("\n=== Testing FeedbackProcessor ===")
+    try:
+        from core.learning.feedback_processor import FeedbackProcessor, FeedbackType
+        
+        processor = FeedbackProcessor()
+        logger.info("✓ FeedbackProcessor created")
+        
+        result = processor.process_feedback(
+            workflow_id="test_wf_1",
+            execution_id="exec_1",
+            feedback_type=FeedbackType.CORRECT,
+            confidence=0.95
+        )
+        logger.info(f"✓ Feedback processed: {len(result['suggestions'])} suggestions")
+        
+        stats = processor.get_feedback_stats("test_wf_1")
+        logger.info(f"✓ Stats: {stats['total']} total, accuracy={stats['accuracy']:.2f}")
+        
+        return True
+    except Exception as e:
+        logger.error(f"✗ Test failed: {e}", exc_info=True)
+        return False
+
+def test_state_transitions():
+    logger.info("\n=== Testing State Transitions ===")
+    try:
+        from core.learning.learning_manager import LearningManager, WorkflowStats
+        from core.models.workflow_graph import LearningState
+        
+        manager = LearningManager()
+        
+        # Test OBSERVATION → COACHING
+        stats = WorkflowStats(workflow_id="wf_trans", learning_state=LearningState.OBSERVATION)
+        manager.workflows["wf_trans"] = stats
+        
+        logger.info(f"  Initial: {stats.learning_state.value}")
+        
+        # Trigger transition
+        for i in range(5):
+            stats.observation_count += 1
+            stats.confidence_scores.append(0.92)
+        manager._check_state_transition("wf_trans")
+        
+        logger.info(f"  After 5 obs: {stats.learning_state.value}")
+        
+        # COACHING → AUTO_CANDIDATE
+        for i in range(10):
+            stats.execution_count += 1
+            stats.success_count += 1
+        manager._check_state_transition("wf_trans")
+        
+        logger.info(f"  After 10 exec: {stats.learning_state.value}")
+        
+        logger.info("✓ State transitions working")
+        return True
+        
+    except Exception as e:
+        logger.error(f"✗ Test failed: {e}", exc_info=True)
+        return False
+
+def main():
+    logger.info("=" * 60)
+    logger.info("Phase 7 - Learning System Tests")
+    logger.info("=" * 60)
+    
+    tests = [
+        test_imports,
+        test_learning_manager_basic,
+        test_feedback_processor,
+        test_state_transitions
+    ]
+    
+    results = []
+    for test in tests:
+        try:
+            result = test()
+            results.append(result)
+        except Exception as e:
+            logger.error(f"Test crashed: {e}", exc_info=True)
+            results.append(False)
+    
+    passed = sum(results)
+    logger.info(f"\n{'='*60}\nResults: {passed}/{len(results)} tests passed\n{'='*60}")
+    return 0 if passed == len(results) else 1
+
+if __name__ == '__main__':
+    sys.exit(main())