app.py

import gradio as gr
from transformers import AutoModel, AutoTokenizer

# Load the model and tokenizer
model = AutoModel.from_pretrained("kakaobank/kf-deberta-base")
tokenizer = AutoTokenizer.from_pretrained("kakaobank/kf-deberta-base")

def process_text(text):
    # Tokenize the input text
    tokens = tokenizer.tokenize(text)
    token_output = f"Tokens: {tokens}"

    # Generate model output
    inputs = tokenizer(text, return_tensors="pt")
    model_output = model(**inputs)

    # You might want to format this output in a more readable way
    model_output_str = str(model_output)

    return token_output, model_output_str

# Create a Gradio interface
iface = gr.Interface(
    fn=process_text,
    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
    outputs=[gr.Textbox(label="Tokenized Output"), gr.Textbox(label="Model Output")],
    title="DeBERTa Model Text Processing",
    description="This interface tokenizes the input text and processes it with the DeBERTa model."
)

iface.launch()