Update app.py

app.py

@@ -132,14 +132,15 @@ def plot_confusion_matrix(y_true, y_pred, title):
     plt.savefig(f"{title}.png")
     return Image.open(f"{title}.png")
 
-def identical_train_test_split(output_emb, output_raw, labels, percentage):
+def identical_train_test_split(output_emb, output_raw, labels, percentage_idx):
     N = output_emb.shape[0]  # Get the total number of samples
     
     # Generate the indices for shuffling and splitting
     indices = torch.randperm(N)  # Randomly shuffle the indices
     
     # Calculate the split index
-    split_index = int(N * percentage)
+    split_index = int(N * percentage_values[percentage_idx])
+    print(f'Training Size: {split_index}')
     
     # Split indices into train and test
     train_indices = indices[:split_index]  # First 80% for training
@@ -229,7 +230,7 @@ def process_hdf5_file(uploaded_file, percentage_idx):
         # Step 8: Perform classification using the Euclidean distance for both raw and embeddings
         pred_raw = classify_based_on_distance(train_data_raw, train_labels, test_data_raw)
         pred_emb = classify_based_on_distance(train_data_emb, train_labels, test_data_emb)
-
+        print(f'pred_emb: {pred_emb}')
         # Step 9: Generate confusion matrices for both raw and embeddings
         raw_cm_image = plot_confusion_matrix(test_labels, pred_raw, title="Confusion Matrix (Raw Channels)")
         emb_cm_image = plot_confusion_matrix(test_labels, pred_emb, title="Confusion Matrix (Embeddings)")