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| import numpy as np | |
| from util import load_uploaded_file, segment_signal | |
| from gemini import query_gemini_rest | |
| CLASSES = ["N", "V", "/", "A", "F", "~"] | |
| LABEL_MAP = { | |
| "N": "Normal sinus beat", | |
| "V": "Premature Ventricular Contraction (PVC)", | |
| "/": "Paced beat (pacemaker)", | |
| "A": "Atrial premature beat", | |
| "F": "Fusion of ventricular & normal beat", | |
| "~": "Unclassifiable / noise" | |
| } | |
| def analyze_signal(file, model, gemini_key="", signal_type="ECG"): | |
| signal = load_uploaded_file(file, signal_type) | |
| segments = segment_signal(signal) | |
| preds = model.predict(segments, verbose=0)[0] | |
| idx = int(np.argmax(preds)) | |
| conf = float(preds[idx]) | |
| label = CLASSES[idx] | |
| human = LABEL_MAP[label] | |
| gemini_txt = None | |
| if gemini_key: | |
| gemini_txt = query_gemini_rest(signal_type, human, conf, gemini_key) | |
| return label, human, conf, gemini_txt | |