Update app.py

app.py

@@ -16,8 +16,6 @@ tokenizer = MT5TokenizerFast.from_pretrained(
 )
 
 def predict(text):
-    
-# with torch.no_grad():
     input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True)
     generated_ids = model.generate(
         input_ids=input_ids,
@@ -30,7 +28,7 @@ def predict(text):
         top_k=20, #default 20
         num_return_sequences=3,
     )
-
+    
     preds = [
         tokenizer.decode(
             g,
@@ -39,10 +37,12 @@ def predict(text):
         )
         for g in generated_ids
     ]
-
+    
     output = ['Q: ' + text for text in preds]
-    final_str = '\n'.join([f"{i+1}. Question: {s.split('Answer')[0].strip()}\n   Answer{s.split('Answer')[1].strip()}" for i, s in enumerate(output)])
-   return final_str
+    final_str = '\n'.join([f"{i+1}. {s}" for i, s in enumerate(output)])
+    #final_str = '\n'.join([f"{i+1}. Question: {s.split('Answer')[0].strip()}\n   Answer{s.split('Answer')[1].strip()}" for i, s in enumerate(output)])
+    
+    return final_str
 
 # text_to_predict = predict(text)
 # predicted = ['Q: ' + text for text in predict(text_to_predict)]