app.py

import gradio as gr
from huggingface_hub import InferenceClient
import re
import time

# Function to create an Inference Client based on selected model
def create_inference_client(model_name):
    return InferenceClient(model_name)

# Function to generate a response
def respond(
    message,
    system_message,
    model,
    max_tokens,
    temperature,
    top_p,
):
    # Create InferenceClient based on model selection
    client = create_inference_client(model)

    messages = [{"role": "system", "content": system_message}]
    messages.append({"role": "user", "content": message})
    
    response = ""
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        print(response)
        yield response

# Gradio interface setup
demo = gr.Interface(
    fn=respond,
    inputs=[
        gr.Textbox(label="User Message"),
        gr.Textbox(value="한국어 문맥상 부자연스러운 부분을 찾으시오. 오류 문장과 개수는 <incorrect grammar> </incorrect grammar> tag, 즉 <incorrect grammar> - 오류 문장과 설명 </incorrect grammar> 안에 담겨 있으며, <wrong count> </wrong count> tag, 즉 <wrong count> 오류 개수 </wrong count> 이다.", label="System message"),
        gr.Dropdown(choices=["davidkim205/kgrammar-2-1b", "davidkim205/kgrammar-2-3b"], value="davidkim205/kgrammar-2-1b", label="Model Selection"),
        gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=1.0, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
    outputs="textbox"
)

if __name__ == "__main__":
    demo.launch()