Update app.py

app.py

@@ -2,7 +2,8 @@ import streamlit as st
 import pandas as pd
 import torch
 from transformers import pipeline
-from transformers import TapasTokenizer, TapexTokenizer, BartForConditionalGeneration
+#from transformers import TapasTokenizer, TapexTokenizer, BartForConditionalGeneration
+from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering
 import datetime
 
 #df = pd.read_excel('discrepantes.xlsx', index_col='Unnamed: 0')
@@ -14,10 +15,13 @@ print(table_data.head())
 def response(user_question, table_data):
     a = datetime.datetime.now()
 
-    model_name = "microsoft/tapex-large-finetuned-wtq"
-    model = BartForConditionalGeneration.from_pretrained(model_name)
-    tokenizer = TapexTokenizer.from_pretrained(model_name)
-
+    #model_name = "microsoft/tapex-large-finetuned-wtq"
+    model_name = ""google/tapas-base-finetuned-wtq""
+    #model = BartForConditionalGeneration.from_pretrained(model_name)
+    model = AutoModelForTableQuestionAnswering.from_pretrained(model_name)
+    #tokenizer = TapexTokenizer.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
     queries = [user_question]
 
     encoding = tokenizer(table=table_data, query=queries, padding=True, return_tensors="pt", truncation=True)