Update app.py

app.py

@@ -135,8 +135,8 @@ def plot_confusion_matrix_from_csv(csv_file_path, title, save_path):
     data = pd.read_csv(csv_file_path)
     
     # Extract ground truth and predictions
-    y_true = data['ground-truth']
-    y_pred = data['predicted']
+    y_true = data['Target']
+    y_pred = data['Top-1 Prediction']
     
     # Compute confusion matrix
     cm = confusion_matrix(y_true, y_pred)
@@ -229,7 +229,7 @@ class PrintCapture(io.StringIO):
 
 # Function to load and display predefined images based on user selection
 def display_predefined_images(percentage_idx):
-    percentage = percentage_values[percentage_idx]
+    percentage = percentage_values_los[percentage_idx]
     raw_image_path = os.path.join(RAW_PATH, f"percentage_{percentage}_complexity_16.png")
     embeddings_image_path = os.path.join(EMBEDDINGS_PATH, f"percentage_{percentage}_complexity_16.png")
     
@@ -278,7 +278,7 @@ def load_module_from_path(module_name, file_path):
 
 # Function to split dataset into training and test sets based on user selection
 def split_dataset(channels, labels, percentage_idx):
-    percentage = percentage_values[percentage_idx] / 100
+    percentage = percentage_values_los[percentage_idx] / 100
     num_samples = channels.shape[0]
     train_size = int(num_samples * percentage)
     print(f'Number of Training Samples: {train_size}')
@@ -347,7 +347,7 @@ def identical_train_test_split(output_emb, output_raw, labels, percentage_idx):
     indices = torch.randperm(N)  # Randomly shuffle the indices
     
     # Calculate the split index
-    split_index = int(N * percentage_values[percentage_idx]/100)
+    split_index = int(N * percentage_values_los[percentage_idx]/100)
     print(f'Training Size: {split_index}')
     
     # Split indices into train and test
@@ -429,7 +429,7 @@ def process_hdf5_file(uploaded_file, percentage_idx):
         print(f"Output Raw Shape: {output_raw.shape}")
 
         print(f'percentage_idx: {percentage_idx}')
-        print(f'percentage_value: {percentage_values[percentage_idx]}')
+        print(f'percentage_value: {percentage_values_los[percentage_idx]}')
         train_data_emb, test_data_emb, train_data_raw, test_data_raw, train_labels, test_labels = identical_train_test_split(output_emb.view(len(output_emb),-1),
                                                                                                                              output_raw.view(len(output_raw),-1),
                                                                                                                              labels,