import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the InferenceClient with the appropriate model
client = InferenceClient("wop/kosmox")

def format_messages(history, user_message):
    # Create a formatted string according to the specified chat template
    formatted_message = "<s>"
    #if system_message:
    #    formatted_message += f"<|system|>\n{system_message}\n"

    for user_msg, assistant_msg in history:
        if user_msg:
            formatted_message += f"<|user|>\n{user_msg}\n"
        if assistant_msg:
            formatted_message += f"<|assistant|>\n{assistant_msg}\n"
    
    formatted_message += f"<|user|>\n{user_message}\n"
    return formatted_message

def respond(
    message: str,
    history: list[tuple[str, str]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    # Format the messages
    formatted_message = format_messages(history, message)

    response = ""

    # Stream the response from the model
    for message in client.chat_completion(
        formatted_message,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

# Define the Gradio interface
demo = gr.ChatInterface(
    fn=respond,
    additional_inputs=[
       # gr.Textbox(value="", 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()