add fix for qa

app.py

@@ -81,8 +81,6 @@ qg_tokenizer = T5Tokenizer.from_pretrained('valhalla/t5-base-e2e-qg')
 
 
 def generate_questions(text):
-    print('qg')
-    print(text)
     qg_final_model = E2EQGPipeline(qg_model, qg_tokenizer)
     questions = qg_final_model.generate_questions(text)
     translator = Translator()
@@ -111,14 +109,12 @@ qa_model = T5ForConditionalGeneration.from_pretrained(ckpt).to(device)
 def generate_question_response(question, context):
     input_text = 'question: %s  context: %s' % (question, context)
     print(input_text)
-    features = tokenizer([input_text], padding='max_length', truncation=True, max_length=512, return_tensors='pt')
+    features = qa_tokenizer([input_text], padding='max_length', truncation=True, max_length=512, return_tensors='pt')
     output = qa_model.generate(
         input_ids=features['input_ids'].to(device),
         attention_mask=features['attention_mask'].to(device),
-        temperature=1.0  # Ajusta la temperatura
+        temperature=1.0
     )
-    print('output')
-    print(output)
     return qa_tokenizer.decode(output[0], skip_special_tokens=True)