Update app.py

app.py

@@ -139,7 +139,7 @@ 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])
+    split_index = int(N * percentage_values[percentage_idx-1]/10)
     print(f'Training Size: {split_index}')
     
     # Split indices into train and test
@@ -211,13 +211,11 @@ def process_hdf5_file(uploaded_file, percentage_idx):
 
         # Step 7: Tokenize the data using the tokenizer from input_preprocess
         preprocessed_chs = input_preprocess.tokenizer(manual_data=channels)
-        print(preprocessed_chs[0][0][1])
+        #print(preprocessed_chs[0][0][1])
 
         # Step 7: Perform inference using the functions from inference.py
         output_emb = inference.lwm_inference(preprocessed_chs, 'channel_emb', model)
-        #print(f'output_emb:{output_emb[10][0]}')
         output_raw = inference.create_raw_dataset(preprocessed_chs, device)
-        #print(f'output_raw:{output_raw[10][0]}')
 
         print(f"Output Embeddings Shape: {output_emb.shape}")
         print(f"Output Raw Shape: {output_raw.shape}")