Update app.py

app.py

@@ -36,7 +36,6 @@ def predict(test_query):
     # Add [CLS] at the front 
     temp_token.append('[CLS]')
     token_list = tokenizer.tokenize(test_query)
-    token_list
     for m,token in enumerate(token_list):
         temp_token.append(token)
     # Trim the token to fit the length requirement
@@ -48,13 +47,10 @@ def predict(test_query):
     input_ids = pad_sequences([tokenizer.convert_tokens_to_ids(txt) for txt in tokenized_texts],
                               maxlen=max_len, dtype="long", truncating="post", padding="post")
     attention_masks = [[int(i>0) for i in ii] for ii in input_ids]
-    attention_masks[0];
     segment_ids = [[0] * len(input_id) for input_id in input_ids]
-    segment_ids[0];
     input_ids = torch.tensor(input_ids)
     attention_masks = torch.tensor(attention_masks)
     segment_ids = torch.tensor(segment_ids)
-    import torch
 
     # Assuming you have defined your model and input_ids somewhere before this
     device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -70,11 +66,9 @@ def predict(test_query):
     # Make logits into numpy type predict result
     # The predict result contain each token's all tags predict result
     predict_results = logits.detach().cpu().numpy()
-    predict_results.shape
 
     from scipy.special import softmax
     result_arrays_soft = softmax(predict_results[0])
-    result_arrays_soft[0]
 
     result_array = result_arrays_soft
     result_list = np.argmax(result_array,axis=-1)