Update app.py

app.py

@@ -388,6 +388,7 @@ def identical_train_test_split(output_emb, output_raw, labels, train_percentage)
     remaining_indices = indices[test_split_index:]
     train_split_index = int(len(remaining_indices) * train_percentage / 100)
     print(f'Training Size: {train_split_index} out of remaining {len(remaining_indices)}')
+    print(f'Test Size: {test_split_index}')
     
     train_indices = remaining_indices[:train_split_index]
 
@@ -471,9 +472,9 @@ def process_hdf5_file(uploaded_file, percentage):
                                                                                                                              percentage)
         
         # Step 8: Perform classification using the Euclidean distance for both raw and embeddings
-        print(f'train_data_emb: {train_data_emb.shape}')
-        print(f'train_labels: {train_labels.shape}')
-        print(f'test_data_emb: {test_data_emb.shape}')
+        #print(f'train_data_emb: {train_data_emb.shape}')
+        #print(f'train_labels: {train_labels.shape}')
+        #print(f'test_data_emb: {test_data_emb.shape}')
         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)