import gradio as gr
import numpy as np
import random
from typing import Optional

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch

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

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

DEFAULT_SEED = 42
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
DEFAULT_WIDTH = 1024
DEFAULT_HEIGHT = 1024
DEFAULT_GS = 0.0
DEFAULT_NUM_INF_STEPS = 20


# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(model_id: Optional[str] = "CompVis/stable-diffusion-v1-4",
          prompt: str = "",
          negative_prompt: str = "",
          seed: Optional[int] = DEFAULT_SEED,
          randomize_seed: bool = True,
          width: int = DEFAULT_WIDTH,
          height: int = DEFAULT_HEIGHT,
          guidance_scale: Optional[float] = DEFAULT_GS,
          num_inference_steps: Optional[int] = DEFAULT_NUM_INF_STEPS,
          progress = gr.Progress(track_tqdm=True)):
    if model_id:
        model_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

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

    pipe = DiffusionPipeline.from_pretrained(
        pretrained_model_name_or_path=model_id,
        torch_dtype=torch_dtype)
    pipe = pipe.to(device)

    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",
    "Cute animal",
]

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

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Form")

        with gr.Row():
            model_id = gr.Dropdown(
                choices=["stabilityai/sdxl-turbo", "CompVis/stable-diffusion-v1-4"],
                multiselect=False,
                allow_custom_value=True,
                label="Model",
                #info="Choose model ID",
            )
        
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=True,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=DEFAULT_SEED,
            )

            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=DEFAULT_WIDTH,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=DEFAULT_HEIGHT,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=DEFAULT_GS,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=DEFAULT_NUM_INF_STEPS,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

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