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| import joblib | |
| import sys | |
| import os | |
| print("=" * 60) | |
| print("PHASE 1: MODEL VERIFICATION") | |
| print("=" * 60) | |
| model_path = "app/ml/voice_auth_model.pkl" | |
| # Check if file exists | |
| if not os.path.exists(model_path): | |
| print(f"❌ FAIL: Model file not found at {model_path}") | |
| print("ERROR: MODEL NOT TRAINED – model file missing") | |
| sys.exit(1) | |
| print(f"✅ Model file exists: {model_path}") | |
| # Get file stats | |
| file_size = os.path.getsize(model_path) | |
| print(f" Size: {file_size:,} bytes ({file_size/1024:.2f} KB)") | |
| # Try to load the model | |
| try: | |
| model = joblib.load(model_path) | |
| print("✅ Model loaded successfully") | |
| except Exception as e: | |
| print(f"❌ FAIL: Could not load model") | |
| print(f"ERROR: MODEL FILE CORRUPTED OR INVALID") | |
| print(f"Exception: {e}") | |
| sys.exit(1) | |
| # Check model type | |
| print(f" Model type: {type(model)}") | |
| # If it's a pipeline, check the classifier | |
| if hasattr(model, 'named_steps'): | |
| print(" Pipeline detected:") | |
| for step_name, step in model.named_steps.items(): | |
| print(f" - {step_name}: {type(step).__name__}") | |
| if 'classifier' in model.named_steps: | |
| classifier = model.named_steps['classifier'] | |
| classifier_type = type(classifier).__name__ | |
| print(f" Classifier: {classifier_type}") | |
| # Check if it's XGBoost or LogisticRegression | |
| if 'XGB' in classifier_type or 'LogisticRegression' in classifier_type: | |
| print(f"✅ Valid classifier type: {classifier_type}") | |
| else: | |
| print(f"⚠️ Unexpected classifier type: {classifier_type}") | |
| else: | |
| print(f" Direct model (not a pipeline): {type(model).__name__}") | |
| print("\n✅ PHASE 1 PASSED: Model artifact is valid and loadable") | |