app.py

import gradio as gr
from transformers import pipeline

"""
For more information on `transformers` library, please check the docs: https://huggingface.co/docs/transformers/
"""
model_name = "BabyChou/Deepseek-R1-Distill-Qwen-1.5B-GSM8K-GRPO-beta-0.001"
chatbot = pipeline("text-generation", model=model_name)


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    input_text = "\n".join([msg["content"] for msg in messages])

    response = chatbot(
        input_text,
        max_length=max_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
    )[0]["generated_text"]

    yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


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