Update app.py

app.py

@@ -18,19 +18,19 @@ model = BertForSequenceClassification.from_pretrained("NimaKL/spamd_model")
 token_id = []
 attention_masks = []
 def preprocessing(input_text, tokenizer):
-            '''
+    '''
                   Returns <class transformers.tokenization_utils_base.BatchEncoding> with the following fields:
                     - input_ids: list of token ids
                     - token_type_ids: list of token type ids
                     - attention_mask: list of indices (0,1) specifying which tokens should considered by the model (return_attention_mask = True).
-            '''
+    '''
     return tokenizer.encode_plus(
-            input_text,
-            add_special_tokens = True,
-            max_length = 32,
-            pad_to_max_length = True,
-            return_attention_mask = True,
-            return_tensors = 'pt'
+        input_text,
+        add_special_tokens = True,
+        max_length = 32,
+        pad_to_max_length = True,
+        return_attention_mask = True,
+        return_tensors = 'pt'
             )
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
     with col1: