app.py

# import streamlit as st
# from transformers import pipeline
# pipe=pipeline('sentiment-analysis')
# text=st.text_area('enter some text!')

# if text:
#     out = pipe(text)
#     st.json(out)

import streamlit as st
from transformers import pipeline
import pandas as pd

# prepare table + question
data = {"Neighborhood": ["Upper East Side", "Soho", "Upper West Side"], "Number of Apartments": ["87", "53", "69"]}
table = pd.DataFrame.from_dict(data)
question = "how many apartments does Upper East Side have?"

# pipeline model
# Note: you must to install torch-scatter first.
tqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq")

# result
st.json(tqa(table=table, query=question))
# print(tqa(table=table, query=query)['cells'][0])
#53