Update app.py

app.py

@@ -52,28 +52,30 @@ question_model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_s
 question_tokenizer = T5Tokenizer.from_pretrained('t5-base')
 
 def get_question(sentence,answer):
-  text = "context: {} answer: {} </s>".format(sentence,answer)
-  print (text)
-  max_len = 256
-  encoding = question_tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=True, return_tensors="pt")
-
-  input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
-
-  outs = question_model.generate(input_ids=input_ids,
+    text = "context: {} answer: {} </s>".format(sentence,answer)
+    print (text)
+    max_len = 256
+    encoding = question_tokenizer.encode_plus(text,max_length=max_len, pad_to_max_length=True, return_tensors="pt")
+    
+    input_ids, attention_mask = encoding["input_ids"], encoding["attention_mask"]
+    
+    outs = question_model.generate(input_ids=input_ids,
                                   attention_mask=attention_mask,
                                   early_stopping=True,
                                   num_beams=3,
                                   num_return_sequences=3,
                                   no_repeat_ngram_size=2,
                                   max_length=200)
-
-
-  dec = [question_tokenizer.decode(ids) for ids in outs]
-
-  #Question = dec[0].replace("question:","")
-  #Question= Question.strip()
-  #return Question
-  return dec
+    
+    dec = [question_tokenizer.decode(ids) for ids in outs]
+    questions = ""
+    for i, question in enumerate(dec):
+        question = question.replace("question:", "").replace("<pad>", "").replace("</s>", "")
+        question = question.strip()
+        questions += question
+        if i != len(dec)-1:
+            questions += "§"
+    return questions
 
 input_context = gr.Textbox()
 input_answer = gr.Textbox()