Update app.py

app.py

@@ -1,6 +1,7 @@
 import pandas as pd
 import streamlit as st
 from transformers import pipeline
+from transformers import AutoTokenizer, DistilBertForQuestionAnswering
 from carga_articulos import cargar_articulos
 from preprocesamiento_articulos import limpieza_articulos
 from entrenamiento_modelo import term_document_matrix, tf_idf_score
@@ -16,14 +17,9 @@ def crear_indice():
     td_idf_matrix.to_csv('articulos_indexados.csv') 
 
 def load_qa_model():
-    model = pipeline(
-    'question-answering', 
-    model='mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',
-    tokenizer=(
-        'mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',  
-        {"use_fast": False} # Este modelo aun no es compatible con fast tokenizer
-    )
-   )
+
+    tokenizer = AutoTokenizer.from_pretrained('mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es', use_fast="false")
+    model = DistilBertForQuestionAnswering.from_pretrained("mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es", device_map="auto", trust_remote_code=True, torch_dtype=torch.bfloat16)
     return model
 
 # 4. Use streamlit to create a web app