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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
from rembg import remove
import torch

# ===== εˆε§‹εŒ–ζ¨‘εž‹ =====
device = "cuda" if torch.cuda.is_available() else "cpu"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

if torch.cuda.is_available():
    pipe = DiffusionPipeline.from_pretrained(
        "stabilityai/sdxl-turbo", 
        torch_dtype=torch.float16, 
        variant="fp16",
        use_safetensors=True
    )
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else:
    pipe = DiffusionPipeline.from_pretrained(
        "stabilityai/sdxl-turbo", 
        use_safetensors=True
    )
    pipe = pipe.to(device)

# ===== εŠŸθƒ½ε‡½ζ•Έ =====
def generate_anime(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=f"{prompt}, Anime",
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator
    ).images[0]

    return image

def remove_background(input_img):
    if input_img is None:
        return None
    return remove(input_img)

# ===== Gradio δ»‹ι’θ¨­θ¨ˆ =====
examples = [
    "A well-behaved schoolgirl with glasses",
    "Astronaut in a jungle, cold color palette, 8k",
    "An astronaut riding a green horse",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 520px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown("## 🧠 Anime Character Generator + Background Remover")

        # Prompt row
        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Describe your anime character...",
                container=False,
            )
            run_button = gr.Button("🎨 Generate Anime")

        # Output image (before and after remove background)
        with gr.Row():
            result_img = gr.Image(label="Generated Image")
            removed_img = gr.Image(label="Background Removed")

        # Advanced settings
        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=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=512)
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)

            with gr.Row():
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=0.0)
                num_inference_steps = gr.Slider(label="Steps", minimum=1, maximum=12, step=1, value=2)

        # η―„δΎ‹ζŒ‰ιˆ•ε€
        gr.Markdown("#### ✨ Prompt Examples")
        with gr.Row():
            for example in examples:
                gr.Button(example).click(lambda x=example: x, outputs=prompt)

        # δΈ»ζŒ‰ιˆ• callback
        run_button.click(
            fn=generate_anime,
            inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
            outputs=[result_img]
        ).then(
            fn=remove_background,
            inputs=[result_img],
            outputs=[removed_img]
        )

demo.queue().launch(share=True)