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| from transformers import pipeline | |
| import pandas as pd | |
| def TAPAS(question, table_main): | |
| """ | |
| Processing the question using an expression and the main and geom table. | |
| Args: | |
| question (str): the question. | |
| table_main (df): main table | |
| table_geom (df): geom table | |
| Returns: | |
| answer (str): answer to the question | |
| """ | |
| # set up a TAPAS pipeline for table-based question answering | |
| tqa = pipeline(task="table-question-answering", model="google/tapas-large-finetuned-wtq") | |
| # use the tqa pipeline to perform table-based question answering. | |
| i = tqa(table=table_main, query=question)['cells'][0] | |
| # Check if the output is the link to the TEMP DB: | |
| # Has to be done because the entrys for geometry, ... are an array :( | |
| if ';' in i: | |
| i = i.split(";") | |
| path = i[0] | |
| r = int(i[1]) | |
| c = int(i[2]) | |
| answer_table = pd.read_csv(path) | |
| answer = answer_table.iloc[r,c] | |
| return(answer) | |
| answer = str(i) | |
| return(answer) |