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from transformers import TapasTokenizer, TFTapasForQuestionAnswering, TapexTokenizer, BartForConditionalGeneration | |
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 = "microsoft/tapex-large-finetuned-wtq" | |
model = BartForConditionalGeneration.from_pretrained(model_name) | |
tokenizer = TapexTokenizer.from_pretrained(model_name) | |
queries = [query] | |
encoding = tokenizer(table=table, queries=queries, padding=True, return_tensors="tf",truncated=True) | |
outputs = model.generate(**encoding) | |
ans = tokenizer.batch_decode(outputs, skip_special_tokens=True) | |
query_result = { | |
"query": query, | |
"answer": ans[0] | |
} | |
b = datetime.datetime.now() | |
print(b - a) | |
return query_result, table | |