import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread

# Model configuration
model_id = "Qwen/Qwen3-Coder-Next"

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load Model in 4-bit to save VRAM
# Note: Requires a high-end GPU (A100 80GB recommended)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    load_in_4bit=True, 
    trust_remote_code=True
)

def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Format the prompt using the chat template
    messages = [{"role": "system", "content": system_message}]
    for msg in history:
        messages.append(msg)
    messages.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(
        messages, 
        add_generation_prompt=True, 
        return_tensors="pt"
    ).to(model.device)

    # Setup Streaming
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    
    generate_kwargs = dict(
        input_ids=input_ids,
        streamer=streamer,
        max_new_tokens=max_tokens,
        do_sample=True,
        temperature=temperature,
        top_p=top_p,
        pad_token_id=tokenizer.eos_token_id,
    )

    # Run generation in a separate thread
    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()

    response = ""
    for new_text in streamer:
        response += new_text
        yield response

# Gradio Interface
chatbot = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a helpful coding assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=4096, value=2048, 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"),
    ],
)

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