import gradio as gr
from huggingface_hub import InferenceClient

DEFAULT_MODEL = "openai/gpt-oss-20b"
DEFAULT_SYSTEM_MESSAGE = "You are a friendly Chatbot."


def respond(
    message: str,
    history: list[dict[str, str]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
    hf_token: gr.OAuthToken,
):
    """
    Chat completion handler with streaming, safe auth checks,
    and graceful error handling.
    """
    # --- Auth guard ---------------------------------------------------------
    if hf_token is None or not hf_token.token:
        yield "🔒 **Authentication required.** Please log in using the sidebar button."
        return

    if not message or not message.strip():
        yield "⚠️ Please enter a message before sending."
        return

    # --- Build messages -----------------------------------------------------
    messages = []
    if system_message and system_message.strip():
        messages.append({"role": "system", "content": system_message})

    for entry in history or []:
        # Defensive normalisation: handle both dict and legacy tuple formats.
        if isinstance(entry, dict) and "role" in entry and "content" in entry:
            messages.append(entry)
        elif isinstance(entry, (list, tuple)) and len(entry) >= 2:
            user_msg, assistant_msg = str(entry[0]), str(entry[1])
            messages.append({"role": "user", "content": user_msg})
            if assistant_msg:
                messages.append({"role": "assistant", "content": assistant_msg})

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

    # --- Stream inference ---------------------------------------------------
    try:
        client = InferenceClient(token=hf_token.token, model=DEFAULT_MODEL)
        stream = client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
        )

 response = ""
        for chunk in stream:
            choices = chunk.choices
            if choices and choices[0].delta and choices[0].delta.content:
                response += choices[0].delta.content
                yield response

    except Exception as e:
        yield f"❌ **Inference error:** `{type(e).__name__}: {e}`"


# --- UI -------------------------------------------------------------------
chatbot = gr.ChatInterface(
    respond,
    type="messages",  # Enforce the new {role, content} format
    additional_inputs=[
        gr.Textbox(
            value=DEFAULT_SYSTEM_MESSAGE,
            label="System message",
            placeholder="You are a helpful assistant...",
        ),
        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("## 🔐 Authentication")
        gr.LoginButton()
        gr.LogoutButton()

    chatbot.render()

if __name__ == "__main__":
    demo.queue(default_concurrency_limit=20).launch()