import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "Qwen/Qwen2.5-Coder-7B-Instruct"

# Load tokenizer at startup
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# Global model - loaded lazily on first GPU call
model = None


def load_model():
    global model
    if model is None:
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_ID,
            torch_dtype=torch.float16,
            device_map="auto",
        )
    return model


@spaces.GPU(duration=120)
def generate_response(message, history, system_message, max_tokens, temperature, top_p):
    loaded_model = load_model()

    messages = [{"role": "system", "content": system_message}]

    for user_msg, assistant_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if assistant_msg:
            messages.append({"role": "assistant", "content": assistant_msg})

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

    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    inputs = tokenizer([text], return_tensors="pt").to(loaded_model.device)

    with torch.no_grad():
        outputs = loaded_model.generate(
            **inputs,
            max_new_tokens=int(max_tokens),
            temperature=float(temperature),
            top_p=float(top_p),
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )

    response = tokenizer.decode(
        outputs[0][inputs['input_ids'].shape[1]:],
        skip_special_tokens=True
    )
    return response


demo = gr.ChatInterface(
    generate_response,
    title="Qwen2.5 Coder 7B",
    description="A coding assistant powered by Qwen2.5-Coder-7B-Instruct on ZeroGPU",
    additional_inputs=[
        gr.Textbox(
            value="You are Qwen, a helpful coding assistant. You excel at writing clean, efficient code and explaining programming concepts clearly.",
            label="System message",
            lines=2,
        ),
        gr.Slider(minimum=64, maximum=4096, value=1024, step=64, label="Max tokens"),
        gr.Slider(minimum=0.1, maximum=1.5, 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"),
    ],
    examples=[
        ["Hello! What programming languages are you best at?"],
        ["Write a Python function to check if a string is a palindrome"],
        ["Explain the difference between async/await and promises in JavaScript"],
        ["Help me optimize this SQL query: SELECT * FROM users WHERE name LIKE '%john%'"],
    ],
)

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