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| from keras.models import load_model | |
| from graph import zeropad, zeropad_output_shape | |
| from pathlib import Path | |
| import joblib | |
| def load_mitbih_model(): | |
| return load_model( | |
| "src/MLII-latest.keras", | |
| custom_objects={ | |
| "zeropad": zeropad, | |
| "zeropad_output_shape": zeropad_output_shape | |
| }, | |
| compile=False | |
| ) | |
| def load_pcg_model(): | |
| model_path = Path("src/pcg_model.h5") | |
| if not model_path.exists(): | |
| raise FileNotFoundError(f"PCG model not found at {model_path.resolve()}") | |
| model = load_model(model_path, compile=False) | |
| model.compile() | |
| return model | |
| def load_emg_model(): | |
| model_path = Path("src/emg_classifier_txt.h5") | |
| if not model_path.exists(): | |
| raise FileNotFoundError(f"EMG model not found at {model_path.resolve()}") | |
| model = load_model(model_path, compile=False) | |
| model.compile() | |
| return model | |
| def load_vag_model(): | |
| p = Path("src/vag_feature_classifier.pkl") | |
| if not p.exists(): | |
| raise FileNotFoundError(f"No VAG model at {p.resolve()}") | |
| return joblib.load(p) | |