changed the inputs into tensors and added the maximum amount of padding elements

app.py

@@ -16,9 +16,13 @@ def model_classifier(text):
         if len(text)==0:
             return f"the input text is {text}"
         else:
-            encoded_input=tokenizer(text,return_tensors="pt",truncation=True,padding=True) #this is where the encoding happens
+            encoded_input=tokenizer(text,return_tensors="pt",truncation=True,padding=True,max_length=512) #this is where the encoding happens
             input_ids=encoded_input["input_ids"]
             attention_mask=encoded_input["attention_mask"]
+
+            #turning the inputs into tensors
+            inputs_ids=torch.tensor(input_ids)
+            attention_mask=torch.tensor(attention_mask)
             
             logits=model(input_ids,attention_mask).logits  #this is the logits of the labels
             probs_label=softmax(logits,dim=-1)  #turning the probability distribution into normalize form