# 1. Import spaces FIRST (This prevents the CUDA error)
import spaces  

# 2. Import the rest AFTER spaces
import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch

# --- The rest of your code stays exactly the same below this line ---

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

# @spaces.GPU  # <--- UNCOMMENT THIS LINE TO ENABLE ZERO GPU!
def infer(
    prompt,
    negative_prompt="",
    seed=0,
    randomize_seed=True,
    width=1024,
    height=1024,
    guidance_scale=0.0,
    num_inference_steps=2,
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image, seed

examples = [
    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
    "An astronaut riding a green horse",
    "A delicious ceviche cheesecake slice",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks() as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("# SDXL Turbo Generator")
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt", 
                placeholder="Enter your prompt", 
                container=False, 
                max_lines=1
            )
            run_button = gr.Button("Run", variant="primary")

        result = gr.Image(label="Result")

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(label="Negative prompt", max_lines=1, visible=False)
            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
            
            with gr.Row():
                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
            
            with gr.Row():
                guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
                num_inference_steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=2)

        gr.Examples(examples=examples, inputs=[prompt])

    # Connect the button
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        css=css
    )