Update app.py

app.py

@@ -8,12 +8,16 @@ from transformers import (
     pipeline,
 )
 
-model_tapex = "microsoft/tapex-large-finetuned-wtq"
-tokenizer_tapex = AutoTokenizer.from_pretrained(model_tapex)
-model_tapex = AutoModelForSeq2SeqLM.from_pretrained(model_tapex)
-pipe_tapex = pipeline(
-    "table-question-answering", model=model_tapex, tokenizer=tokenizer_tapex
-)
+# model_tapex = "microsoft/tapex-large-finetuned-wtq"
+# tokenizer_tapex = AutoTokenizer.from_pretrained(model_tapex)
+# model_tapex = AutoModelForSeq2SeqLM.from_pretrained(model_tapex)
+# pipe_tapex = pipeline(
+#     "table-question-answering", model=model_tapex, tokenizer=tokenizer_tapex
+# )
+
+#new
+pipe_tapex = pipeline(task="table-question-answering", model="microsoft/tapex-large-finetuned-wtq")
+
 
 # model_tapas = "google/tapas-large-finetuned-wtq"
 # tokenizer_tapas = AutoTokenizer.from_pretrained(model_tapas)
@@ -35,9 +39,9 @@ def process(query, file, correct_answer, rows=20):
 def process2(query, csv_data):
     csv_data={"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
     table = pd.DataFrame.from_dict(csv_data)
-    # result_tapex = pipe_tapex(table=table, query=query)
+    result_tapex = pipe_tapex(table=table, query=query)['cells'][0]
     result_tapas = pipe_tapas(table=table, query=query)['cells'][0]
-    return "", result_tapas
+    return result_tapex, result_tapas
 
 
 # Inputs