| import os |
| import joblib |
| import json |
|
|
| def inspect(): |
| base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) |
| model_path = os.path.join(base_dir, "model.joblib") |
| meta_path = os.path.join(base_dir, "model_metadata.json") |
| |
| print("--- Model Inspection ---") |
| print(f"model.joblib path: {model_path}") |
| |
| if os.path.exists(model_path): |
| model = joblib.load(model_path) |
| print(f"Model object class/type: {type(model)}") |
| is_xgb = "xgboost" in str(type(model)).lower() |
| is_rf = "randomforest" in str(type(model)).lower() |
| print(f"Is XGBoost: {is_xgb}") |
| print(f"Is Random Forest: {is_rf}") |
| else: |
| print("Model file not found.") |
|
|
| print(f"\nmodel_metadata.json path: {meta_path}") |
| if os.path.exists(meta_path): |
| with open(meta_path, 'r') as f: |
| meta = json.load(f) |
| print(f"Threshold value: {meta.get('threshold')}") |
| print(f"Feature count: {meta.get('feature_count')}") |
| feature_names = meta.get("feature_names", []) |
| print(f"Feature names count: {len(feature_names)}") |
| else: |
| print("Metadata file not found.") |
|
|
| print("\nPreprocessing settings:") |
| print("All images are resized to 1024x1024 with light Gaussian Blur (as per features.py)") |
| |
| print("\nRule boosts enabled:") |
| print("Yes (as per predict.py and backend/app.py logic)") |
|
|
| if __name__ == "__main__": |
| inspect() |
|
|
