import gradio as gr
from transformers import pipeline

models = {
    "DistilGPT2 SD": "FredZhang7/distilgpt2-stable-diffusion",
    "Llama-SmolTalk-3.2-1B": "prithivMLmods/Llama-SmolTalk-3.2-1B-Instruct",
    "Dolphin-Phi 3 2.9.2": "cognitivecomputations/dolphin-2.9.2-Phi-3-Medium",
    "EXAONE 3.5 2.4B": "LGAI-EXAONE/EXAONE-3.5-2.4B-Instruct",
    "Granite 3.3 2B": "ibm-granite/granite-3.3-2b-instruct"
}

def respond(
    message,
    _: list[tuple[str, str]],
    system_prompt: str,
    model: str,
    max_new_tokens: int,
    temperature: float,
    top_p: float,
    top_k: int
):
    pipe = pipeline("text-generation", model=model)

    yield pipe(
        [
            {
                "role": "system",
                "content": system_prompt
            },
            {
                "role": "user",
                "content": message
            }
        ],
        max_new_tokens=max_new_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        top_k=top_k
    )[0]['generated_text'][-1]


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    title="Prompt Enhancer Test",
    type="messages",
    additional_inputs=[
        gr.Textbox(value="When the user provides two sentences, return a longer sentence that fuses the two together with a natural motion in between.", lines=5, show_label=True, label="System prompt"),
        gr.Radio(list(models.items()), value="FredZhang7/distilgpt2-stable-diffusion", type="value", label="Model"),
        # gr.Textbox(value="Enhance the provided text so that it is more vibrant and detailed.", label="System prompt"),
        gr.Slider(minimum=8, maximum=128, value=64, step=8, 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",
        ),
        gr.Slider(
            minimum=10,
            maximum=100,
            value=30,
            step=5,
            label="Top-k",
        ),
    ],
)


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