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| from transformers import TapasTokenizer, TFTapasForQuestionAnswering | |
| import pandas as pd | |
| import datetime | |
| def execute_query(query, csv_file): | |
| a = datetime.datetime.now() | |
| table = pd.read_csv(csv_file.name, delimiter=",") | |
| table.fillna(0, inplace=True) | |
| table = table.astype(str) | |
| model_name = "google/tapas-base-finetuned-wtq" | |
| model = TFTapasForQuestionAnswering.from_pretrained(model_name) | |
| tokenizer = TapasTokenizer.from_pretrained(model_name) | |
| queries = [query] | |
| inputs = tokenizer(table=table, queries=queries, padding="max_length", return_tensors="tf") | |
| outputs = model(**inputs) | |
| predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions( | |
| inputs, outputs.logits, outputs.logits_aggregation | |
| ) | |
| # let's print out the results: | |
| id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"} | |
| aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices] | |
| answers = [] | |
| for coordinates in predicted_answer_coordinates: | |
| if len(coordinates) == 1: | |
| # only a single cell: | |
| answers.append(table.iat[coordinates[0]]) | |
| else: | |
| # multiple cells | |
| cell_values = [] | |
| for coordinate in coordinates: | |
| cell_values.append(table.iat[coordinate]) | |
| answers.append(cell_values) | |
| for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string): | |
| if predicted_agg != "NONE": | |
| answers.append(predicted_agg) | |
| query_result = { | |
| "query": query, | |
| "result": answers | |
| } | |
| b = datetime.datetime.now() | |
| print(b - a) | |
| return query_result, table | |