#no oom?
import gradio as gr
import numpy as np
import random

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
import torch
targets = {"pussy", "boobs", "breasts", "vagina", "penis", "sex", "oral", "anal", "butt", "ass"}
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "John6666/spicy-realism-nsfw-mix-v30-sdxl"  # 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)
print(pipe)
pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
from PIL import Image
def safe_infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_steps):
    clean = ''.join(c for c in prompt.lower() if c.isalnum() or c.isspace())
    if any(word in clean for word in targets):
        print("Found at least one banned word!")
        blank = Image.new("RGB", (width, height), (0, 0, 0))
        return blank, seed

    return infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_steps)

# @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:
    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",
                value = "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn,  (breasts:3), (nipple:3), (nipples:3), (boobs:3), (butt:3), (ass:3), (butthole:3), (sex:3), (fetish:3), (pussy:3), (vagina:3), (porn:3), (hentai:3), (explicit:3)",
                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=64,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=384,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=64,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=384,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=20.0,
                    step=0.1,
                    value=3.6,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=80,
                    step=1,
                    value=15,  # Replace with defaults that work for your model
                )

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

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