import gradio as gr
import numpy as np
import random
from os import getenv

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

device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use

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 to use ZeroGPU]
def infer(
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    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(css=css) as demo:
    credentials = gr.State("")

    @gr.render(inputs=credentials)
    def app(app_credentials):
        if app_credentials == getenv("PASSWORD"):
            with gr.Column(elem_id="col-container"):
                gr.Markdown(" # Text-to-Image Gradio Template")
        
                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=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=768,  # Replace with defaults that work for your model
                        )
        
                        height = gr.Slider(
                            label="Height",
                            minimum=256,
                            maximum=MAX_IMAGE_SIZE,
                            step=32,
                            value=768,  # 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=0.0,  # 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=2,  # 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=[
                    prompt,
                    negative_prompt,
                    seed,
                    randomize_seed,
                    width,
                    height,
                    guidance_scale,
                    num_inference_steps,
                ],
                outputs=[result, seed],
            )
        else:
            password = gr.Textbox(placeholder="Provide password...")
        
            def set_password(password):
                return password
        
            gr.Button("Login").click(set_password, [password], credentials)

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