import streamlit as st import pandas as pd import torch from transformers import pipeline from transformers import TapasTokenizer, TapasForQuestionAnswering import datetime df = pd.read_excel('discrepantes.xlsx', index_col='Unnamed: 0') df.fillna(0, inplace=True) table_data = df.astype(str) 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) queries = [user_question] encoding = tokenizer(table=table_data, query=queries, padding=True, return_tensors="pt",truncation=True) outputs = model.generate(**encoding) ans = tokenizer.batch_decode(outputs, skip_special_tokens=True) query_result = { "query": query, "answer": ans[0] } b = datetime.datetime.now() print(b - a) return query_result, table # Streamlit interface st.markdown("""