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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=True, return_tensors="tf",truncated=True)
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
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