Update app.py

app.py

@@ -23,11 +23,19 @@ model, tokenizer = load_model_and_tokenizer()
 
 
 def get_predict(title: str, abstract: str) -> (str, float, dict):
-    tokenized_text = tokenizer(title + tokenizer.sep_token + abstract[:128], padding="max_length", truncation=True)
+    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)
-        probs = torch.nn.functional.softmax(out.logits, dim=-1)
+        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))