import os
import threading
import gradio as gr
import torch
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    TextIteratorStreamer,
)

MODEL_ID = "Amey9766/qwen-0.6b-hospitality-housekeeping"

# --- Load once (global) so it doesn't reload every message ---
tokenizer = None
model = None
device = None

def load_model(hf_token: str | None = None):
    global tokenizer, model, device

    if model is not None and tokenizer is not None:
        return

    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.float16 if device == "cuda" else torch.float32

    # If your repo is private/gated, you must provide a token.
    # Priority: Gradio OAuth token -> Space secret HF_TOKEN -> None
    use_token = hf_token or os.getenv("HF_TOKEN")

    tokenizer = AutoTokenizer.from_pretrained(
        MODEL_ID,
        token=use_token,
        trust_remote_code=True,
        use_fast=True,
    )

    model = AutoModelForCausalLM.from_pretrained(
        MODEL_ID,
        token=use_token,
        torch_dtype=dtype,
        device_map="auto" if device == "cuda" else None,
        trust_remote_code=True,
    )

    if device == "cpu":
        model.to(device)

def build_prompt(system_message: str, history: list[dict[str, str]], user_message: str) -> str:
    """
    Universal prompt builder.
    Works even if the model doesn't have a strict chat template.
    If your tokenizer supports apply_chat_template, we use it.
    """
    messages = [{"role": "system", "content": system_message}]
    messages.extend(history)
    messages.append({"role": "user", "content": user_message})

    if hasattr(tokenizer, "apply_chat_template"):
        try:
            return tokenizer.apply_chat_template(
                messages,
                tokenize=False,
                add_generation_prompt=True
            )
        except Exception:
            pass

    # Fallback plain prompt
    prompt = f"System: {system_message}\n"
    for m in history:
        prompt += f"{m['role'].capitalize()}: {m['content']}\n"
    prompt += f"User: {user_message}\nAssistant:"
    return prompt

def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token: gr.OAuthToken,
):
    # Load model (once)
    load_model(hf_token.token if hf_token else None)

    prompt = build_prompt(system_message, history, message)

    inputs = tokenizer(prompt, return_tensors="pt")
    inputs = {k: v.to(device) for k, v in inputs.items()}

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    gen_kwargs = dict(
        **inputs,
        max_new_tokens=int(max_tokens),
        do_sample=True,
        temperature=float(temperature),
        top_p=float(top_p),
        streamer=streamer,
    )

    # Run generation in background thread so streamer can yield tokens
    thread = threading.Thread(target=model.generate, kwargs=gen_kwargs)
    thread.start()

    partial = ""
    for token in streamer:
        partial += token
        yield partial

chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="You are a helpful housekeeping assistant for hotels.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, 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"),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.Markdown("### Login (only needed if the model repo is private/gated)")
        gr.LoginButton()
        gr.Markdown(f"**Model:** `{MODEL_ID}`")
    chatbot.render()

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