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import gradio as gr
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd
#wikisql take longer to process
#model_name = "microsoft/tapex-large-finetuned-wikisql" # You can change this to any other model from the list above
#model_name = "microsoft/tapex-base-finetuned-wikisql"
model_name = "microsoft/tapex-large-finetuned-wtq"
#model_name = "microsoft/tapex-base-finetuned-wtq"
tokenizer = TapexTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)
data = {
"year": [1896, 1900, 1904, 2004, 2008, 2012],
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)
def chatbot_response(user_message):
#inputs = tokenizer.encode("User: " + user_message, return_tensors="pt")
inputs = user_message
encoding = tokenizer(table=table, query=inputs, return_tensors="pt")
outputs = model.generate(**encoding)
response = tokenizer.batch_decode(outputs, skip_special_tokens=True)
return response
# Define the chatbot interface using Gradio
iface = gr.Interface(
fn=chatbot_response,
inputs=gr.Textbox(prompt="You:"),
outputs=gr.Textbox(),
live=True,
capture_session=True,
title="ST SQL Chatbot",
description="Type your message in the box above, and the chatbot will respond.",
)
# Launch the Gradio interface
if __name__ == "__main__":
iface.launch()
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