Update prediction.py

prediction.py

@@ -1,34 +1,27 @@
 import numpy as np
-import tensorflow as tf
 import pandas as pd
-from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
 from preprocessing import preprocess_eeg_file
 from preprocessing_2dcnn import convert_epoch_to_spectrogram
-import random
-random.seed(42)
 
 def aggregate_predictions(spectrogram_list, model, threshold=0.5):
-
-    # Convert each spectrogram to channels-last format.
-    X = np.array([np.transpose(s, (1, 2, 0)) for s in spectrogram_list])
-    print(f'---Aggregating predictions from {len(spectrogram_list)} segments---')
-    preds = model.predict(X)
-    mean_prob = np.mean(preds[:, 1])
+    X = np.array([np.transpose(s, (1, 2, 0)) for s in spectrogram_list])  # (n, H, W, C)
+    preds = model.predict(X, verbose=0)
+    segment_probs = preds[:, 1]  # probas pour la classe "épilepsie"
+    mean_prob = np.mean(segment_probs)
     final_label = 1 if mean_prob >= threshold else 0
-    return final_label, mean_prob
+    return final_label, mean_prob, segment_probs
 
 def predict_eeg_recording(edf_path, model, threshold=0.5):
-    #Process the edf file
     preprocessed_df = preprocess_eeg_file(edf_path, fmin=1.0, fmax=45.0, segment_lenght=5, overlap=2)
-    
+
     if preprocessed_df is None or preprocessed_df.empty:
         raise ValueError("EEG file could not be preprocessed or no valid segments found.")
     
     channels = ["EEG FP1-REF", "EEG FP2-REF", "EEG F3-REF", "EEG F4-REF", "EEG C3-REF"]
-    # Convert each 5-second segment (each row) into a spectrogram.
+    
     spectrogram_list = preprocessed_df.apply(
-        lambda row: convert_epoch_to_spectrogram(row, channels, fs=250, nperseg=128, noverlap=64), axis=1
+        lambda row: convert_epoch_to_spectrogram(row, channels, fs=250, nperseg=128, noverlap=64),
+        axis=1
     ).tolist()
-    
-    return aggregate_predictions(spectrogram_list, model, threshold)
 
+    return aggregate_predictions(spectrogram_list, model, threshold)