import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "Qwen/Qwen2.5-Coder-14B-Instruct"

# Load model and tokenizer (outside the function for efficiency)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True # Add this line for Qwen models
)

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) # Add this line for Qwen models


@spaces.GPU(required=True)
def generate_code(prompt):
    messages = [
        {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
        {"role": "user", "content": prompt}
    ]

    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=512
    )

    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response

# Example usage (optional - remove for Spaces deployment)
if __name__ == "__main__":
    prompt = "write a quick sort algorithm."
    generated_code = generate_code(prompt)
    print(generated_code)