Update app.py

app.py

@@ -339,28 +339,16 @@ def plot_confusion_matrix(y_true, y_pred, title):
     return Image.open(f"{title}.png")
     
 def identical_train_test_split(output_emb, output_raw, labels, train_percentage):
-    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 for test (10% of the data)
+    N = output_emb.shape[0]
+    indices = torch.randperm(N) 
     test_split_index = int(N * 0.20)
-    
-    # Test indices (first 10% of the data)
     test_indices = indices[:test_split_index]
-    
-    # Remaining indices for training
     remaining_indices = indices[test_split_index:]
-    
-    # Calculate the split index for training from the remaining 90%
     train_split_index = int(len(remaining_indices) * train_percentage / 100)
     print(f'Training Size: {train_split_index} out of remaining {len(remaining_indices)}')
     
-    # Train indices (based on the provided percentage from the remaining 90%)
     train_indices = remaining_indices[:train_split_index]
 
-    # Select the same indices from both output_emb and output_raw
     train_emb = output_emb[train_indices]
     test_emb = output_emb[test_indices]