Update app.py

app.py

@@ -44,7 +44,7 @@ def predict(outputs):
             top += percent
             top_cats.append(cat)
             top_probs.append(round(percent, 1))
-     return pd.DataFrame(top_probs, index=top_cats, columns=['Percent'])
+    return pd.DataFrame(top_probs, index=top_cats, columns=['Percent'])
 
 tokenizer = RobertaTokenizer.from_pretrained("roberta-large-mnli")
 model = init_model()
@@ -64,7 +64,7 @@ if not title:
 else:    
     st.markdown("### Result")
     encoded_input = tokenizer(title + ". " + abstract, return_tensors="pt", padding=True, 
-                          max_length = 512, truncation=True)
+                          max_length=1024, truncation=True)
     with torch.no_grad():
         outputs = model(**encoded_input).pooler_output[:, 0, :]
         res = predict(outputs)