update model inputs

app.py

@@ -34,10 +34,7 @@ def predict(text) -> Tuple[Dict, float]:
   model.eval()
 
   # Set up the inputs
-  inputs = tokenizer(text, padding="max_length", truncation=True, return_tensors='pt')
-
-  # Transform the input image for use with the model
-  X = tokenizer(**inputs).unsqueeze(0) # unsqueeze = add batch dimension on 0th index
+  X = tokenizer(text, padding="max_length", truncation=True, return_tensors='pt')
 
   # Put model into eval mode, make prediction
   model.eval()