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| from transformers import AutoModelForTableQuestionAnswering, AutoTokenizer, pipeline | |
| # Use a pipeline as a high-level helper | |
| import streamlit as st | |
| import pandas as pd | |
| # Load model & tokenizer | |
| tapas_model = AutoModelForTableQuestionAnswering.from_pretrained('navteca/tapas-large-finetuned-wtq') | |
| tapas_tokenizer = AutoTokenizer.from_pretrained('navteca/tapas-large-finetuned-wtq') | |
| # Get predictions | |
| nlp = pipeline('table-question-answering', model=tapas_model, tokenizer=tapas_tokenizer) | |
| st.title('Query your data with text') | |
| file = st.file_uploader('Upload a csv file here') | |
| if file is not None: | |
| query = st.text_input('Query') | |
| data = pd.read_csv(file) | |
| st.write(pipe(table=data)) | |
| #print(data) | |
| ''' | |
| st.subheader('Data') | |
| st.table(data.head()) | |
| if query: | |
| st.write(pipe(table=data, query=query))''' |