prueba

app.py

@@ -8,7 +8,6 @@ from transformers import BartTokenizer
 from transformers import PreTrainedModel
 from transformers import PreTrainedTokenizer
 from transformers import AutoTokenizer
-from transformers import AutoModelForSeq2SeqLM
 
 tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
 model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
@@ -106,14 +105,15 @@ 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')
     output = qa_model.generate(
         input_ids=features['input_ids'].to(device),
         attention_mask=features['attention_mask'].to(device),
-        max_length=200,  # Permite respuestas más largas
         temperature=1.0  # Ajusta la temperatura
     )
-
+    print('output')
+    print(output)
     return qa_tokenizer.decode(output[0], skip_special_tokens=True)
 
 
@@ -133,8 +133,6 @@ class SummarizerAndQA:
             self.study_generated_questions = generate_questions(text)
 
         if question != self.question and text != '':
-            print('entro a generar responses')
-            print(question)
             self.question = question
             self.question_response = generate_question_response(question, text)