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
css = """
body {background: #0f0f0f; color: #fafafa; font-family: 'Noto Sans', sans-serif;}
#col-container {margin: 0 auto; max-width: 640px;}
.gr-button {background: #2563eb; color: #ffffff; border-radius: 8px;}
#prompt-box textarea {font-size: 1.1rem; height: 3rem;}
"""

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)

        examples = gr.Examples(
            examples=[
                ["어두운 재즈 바에서 담배 연기를 내뿜는 미스터리한 팜파탈, 성인용 애니메이션 스타일"],
                ["노출이 강조된 드레스를 입은 고딕 뱀파이어 여왕, 드라마틱 조명, 성인 애니 아트"],
                ["은은한 조명의 온천에서 두 연인이 마주 서 있는 관능적 장면, 성인용 애니메이션"],
                ["네온이 빛나는 사이버펑크 클럽 무대에서 도발적인 의상을 입은 댄서, 성인 애니 스타일"],
                ["달빛 아래 요염한 마법사가 주문을 외우는 판타지 장면, 성인용 애니 일러스트"],
            ],
            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()