used the wtq dataset fine tuned model

app.py

@@ -3,9 +3,9 @@ import pandas as pd
 from io import BytesIO
 from transformers import AutoTokenizer, AutoModelForTableQuestionAnswering, TableQuestionAnsweringPipeline
 
-# Load the tokenizer and model directly
-tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wikisql-supervised")
-model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wikisql-supervised")
+# Load the tokenizer and model with "google/tapas-large-finetuned-wtq"
+tokenizer = AutoTokenizer.from_pretrained("google/tapas-large-finetuned-wtq")
+model = AutoModelForTableQuestionAnswering.from_pretrained("google/tapas-large-finetuned-wtq")
 
 # Initialize the TableQuestionAnsweringPipeline manually
 pipe = TableQuestionAnsweringPipeline(model=model, tokenizer=tokenizer)
@@ -27,7 +27,6 @@ def answer_question(uploaded_file, question):
     answer = result['answer']
     return answer
 
-
 logo_url = "https://i.ibb.co/Brr7bPP/xflow.png"
 # Define the Gradio app interface
 iface = gr.Interface(
@@ -40,3 +39,4 @@ iface = gr.Interface(
 
 # Run the app
 iface.launch()
+