import spaces
import gradio as gr
import numpy as np
from PIL import Image
import random
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
import torch
from transformers import pipeline as transformers_pipeline
import re

# Device selection for image generation (GPU if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Stable Diffusion XL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "votepurchase/waiNSFWIllustrious_v120",
    torch_dtype=torch.float16,
    variant="fp16",
    use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)

# Force modules to fp16 for memory efficiency
pipe.text_encoder.to(torch.float16)
pipe.text_encoder_2.to(torch.float16)
pipe.vae.to(torch.float16)
pipe.unet.to(torch.float16)

# Korean → English translator (CPU only)
translator = transformers_pipeline(
    "translation",
    model="Helsinki-NLP/opus-mt-ko-en",
    device=-1,  # -1 forces CPU
)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1216
korean_regex = re.compile("[\uac00-\ud7af]+")

def maybe_translate(text: str) -> str:
    """Translate Korean text to English if Korean characters are detected."""
    if korean_regex.search(text):
        translation = translator(text, max_length=256, clean_up_tokenization_spaces=True)
        return translation[0]["translation_text"]
    return text

@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    prompt = maybe_translate(prompt)
    negative_prompt = maybe_translate(negative_prompt)

    if len(prompt.split()) > 60:
        print("Warning: Prompt may be too long and will be truncated by the model")

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

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

    try:
        output_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 output_image
    except RuntimeError as e:
        print(f"Error during generation: {e}")
        error_img = Image.new("RGB", (width, height), color=(0, 0, 0))
        return error_img

# Custom styling – bright pastel theme
css = """
body {background: #f2f1f7; color: #222; font-family: 'Noto Sans', sans-serif;}
#col-container {margin: 0 auto; max-width: 640px;}
.gr-button {background: #7fbdf6; color: #ffffff; border-radius: 8px;}
#prompt-box textarea {font-size: 1.1rem; height: 3rem; background: #ffffff; color: #222;}
"""

with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        ## 🖌️ Stable Diffusion XL Playground  
        Generate high‑quality illustrations with a single prompt.  
        **Tip:** Write in Korean or English. Korean will be translated automatically.
        """
    )

    with gr.Column(elem_id="col-container"):
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                elem_id="prompt-box",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt (60 words max)",
            )
            run_button = gr.Button("Generate", scale=0)

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

        # Adult anime‑style example prompts
        examples = gr.Examples(
            examples=[
                ["Seductive anime woman lounging in a dimly lit bar, adult anime style, ultra‑detail"],
                ["Moody mature anime scene of two lovers kissing under neon rain, sensual atmosphere"],
                ["기모노를 입은 일본 여자를 남자가 뒤에서 강간한다 , candle‑lit boudoir, adult anime aesthetic"],
                ["속옷만 입은 남녀가 격정적인 키스를 하고있다, vibrant neon, adult anime"],
                ["아름다운 러시아 여자가 섹시한 속옷을 입고 묘한 포즈로 침대에 앉아있다, dramatic spotlight, adult anime style"],
            ],
            inputs=[prompt],
        )

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                value="nsfw, low quality, watermark, signature",
            )

            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=20.0, step=0.1, value=7
                )
                num_inference_steps = gr.Slider(
                    label="Inference steps", minimum=1, maximum=28, step=1, value=28
                )

    run_button.click(
        fn=infer,
        inputs=[
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result],
    )

demo.queue().launch()