import os
import gradio as gr
from huggingface_hub import InferenceClient

MODEL_ID = "HauhauCS/GLM-4.7-Flash-Uncensored-HauhauCS-Balanced"


def get_token(hf_token: gr.OAuthToken | None) -> str | None:
    """
    Priority:
    1) Gradio OAuth token (when user clicks LoginButton)
    2) Space secret HF_TOKEN (Settings -> Secrets)
    """
    if hf_token is not None and getattr(hf_token, "token", None):
        return hf_token.token
    return os.getenv("HF_TOKEN")


def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token: gr.OAuthToken,
):
    token = get_token(hf_token)

    # If the repo is gated/private, you need a token.
    # If it's public, token can be None and it may still work depending on the endpoint limits.
    client = InferenceClient(model=MODEL_ID, token=token)

    messages = [{"role": "system", "content": system_message}]
    messages.extend(history)
    messages.append({"role": "user", "content": message})

    response = ""

    # Stream output token-by-token
    for chunk in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token_text = ""
        if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content:
            token_text = chunk.choices[0].delta.content

        response += token_text
        yield response


chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="You are a helpful coding assistant.", 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)",
        ),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.Markdown(f"### Model\n`{MODEL_ID}`")
        gr.LoginButton()
        gr.Markdown(
            "If this model requires authentication, click **Login** "
            "or set a Space secret named `HF_TOKEN`."
        )
    chatbot.render()

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