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| # 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 |