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app_tapex.py

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+from transformers import TapasTokenizer, TapexTokenizer, BartForConditionalGeneration
+import pandas as pd
+import datetime
+import torch
+
+
+def execute_query(query, csv_file):
+    a = datetime.datetime.now()
+
+    table = pd.read_csv(csv_file.name, delimiter=",")
+    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, query=queries, padding=True, return_tensors="pt",truncation=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