Update app.py

app.py

@@ -25,16 +25,15 @@ model, tokenizer = load_model_and_tokenizer()
 def get_predict(title: str, abstract: str) -> (str, float, dict):
     text = [title + tokenizer.sep_token + abstract[:128]]
 
-
     tokens_info = tokenizer(
         text,
         padding=True,
         truncation=True,
         return_tensors="pt",
-)
+    )
     
     with torch.no_grad():
-        outputs = model(tokenized_text)
+        out = model(**tokens_info)
         probs = torch.nn.functional.softmax(out.logits, dim=-1).tolist()[0]
 
         return list(sorted([(p, ind_to_target[i]) for i, p in enumerate(probs)], reversed=True))