app.py

import gradio as gr
from all_models import models
from externalmod import gr_Interface_load, save_image, randomize_seed
import asyncio
import os
from threading import RLock
from datetime import datetime

preSetPrompt = "cute tall slender athletic 20+ caucasian woman. gorgeous face. perky tits. sensual expression. lifting shirt. photorealistic. cinematic. f1.4"
# preSetPrompt = "cute tall slender athletic 20+ nude caucasian woman. gorgeous face. perky tits. gaping outie pussy. pussy juice. sly smile. explicit pose. artistic. photorealistic. cinematic. f1.4"

# H. R. Giger prompt:
# preSetPrompt = "a tall slender athletic caucasian nude 18+ female cyborg. gorgeous face. perky tits. wet skin. sensual expression. she is entangled in rusty chains, rusty barbed wire and electric cables. old dark dusty decaying spaceship designed by h.r. giger. rusty metal dildos. wet tubes and wet plastic hoses. dark, gloomy teal cinematic light. photorealistic."

negPreSetPrompt = "[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry, text, fuzziness"

lock = RLock()
HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.

def get_current_time():
    now = datetime.now()
    now2 = now
    current_time = now2.strftime("%y-%m-%d %H:%M:%S")
    return current_time

def load_fn(models):
    global models_load
    models_load = {}
    for model in models:
        if model not in models_load.keys():
            try:
                m = gr_Interface_load(f'models/{model}', hf_token=HF_TOKEN)
            except Exception as error:
                print(error)
                m = gr.Interface(lambda: None, ['text'], ['image'])
            models_load.update({model: m})


load_fn(models)


num_models = 6
max_images = 6
inference_timeout = 400
default_models = models[:num_models]
MAX_SEED = 2**32-1


def extend_choices(choices):
    return choices[:num_models] + (num_models - len(choices[:num_models])) * ['NA']


def update_imgbox(choices):
    choices_plus = extend_choices(choices[:num_models])
    return [gr.Image(None, label=m, visible=(m!='NA')) for m in choices_plus]


def random_choices():
    import random
    random.seed()
    return random.choices(models, k=num_models)


# https://huggingface.co/docs/api-inference/detailed_parameters
# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
async def infer(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1, timeout=inference_timeout):
    kwargs = {}
    if height > 0: kwargs["height"] = height
    if width > 0: kwargs["width"] = width
    if steps > 0: kwargs["num_inference_steps"] = steps
    if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
        
    if seed == -1:
        theSeed = randomize_seed()
        kwargs["seed"] = theSeed
    else: 
        kwargs["seed"] = seed
        theSeed = seed
        
    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
    await asyncio.sleep(0)
    try:
        result = await asyncio.wait_for(task, timeout=timeout)
    except asyncio.TimeoutError as e:
        print(e)
        print(f"Task timed out: {model_str}")
        if not task.done(): task.cancel()
        result = None
        raise Exception(f"Task timed out: {model_str}") from e
    except Exception as e:
        print(e)
        if not task.done(): task.cancel()
        result = None
        raise Exception() from e
    if task.done() and result is not None and not isinstance(result, tuple):
        with lock:
            # png_path = "img.png"
            # png_path = get_current_time() + "_" + model_str.replace("/", "_") + ".png"
            # png_path =  model_str.replace("/", "_") + " - " +  prompt + " - " + get_current_time() + ".png"
            png_path =  model_str.replace("/", "_") + " - " + get_current_time() + "_" + str(theSeed) + ".png"
            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, seed)
        return image
    return None


def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
    try:
        loop = asyncio.new_event_loop()
        result = loop.run_until_complete(infer(model_str, prompt, nprompt,
                                         height, width, steps, cfg, seed, inference_timeout))
    except (Exception, asyncio.CancelledError) as e:
        print(e)
        print(f"Task aborted: {model_str}")
        result = None
        raise gr.Error(f"Task aborted: {model_str}, Error: {e}")
    finally:
        loop.close()
    return result


def add_gallery(image, model_str, gallery):
    if gallery is None: gallery = []
    with lock:
        if image is not None: gallery.insert(0, (image, model_str))
    return gallery




js_func = """
function refresh() {
    const url = new URL(window.location);
    if (url.searchParams.get('__theme') !== 'dark') {
        url.searchParams.set('__theme', 'dark');
        window.location.href = url.href;
    }
}
"""

js_AutoSave="""
        console.log("Yo");
        
        var img1 = document.querySelector("div#component-355 .svelte-1kpcxni button.svelte-1kpcxni .svelte-1kpcxni img"),
        observer = new MutationObserver((changes) => {
            changes.forEach(change => {
                    if(change.attributeName.includes('src')){
                        console.log(img1.src);
                        document.querySelector("div#component-355 .svelte-1kpcxni .svelte-sr71km a.svelte-1s8vnbx button").click();
                    }
            });
        });
        observer.observe(img1, {attributes : true});
        
"""


CSS="""
.gradio-container { max-width: 1200px; margin: 0 auto; background: linear-gradient(to bottom, #1a1a1a, #2d2d2d); !important; }
.output { 
    width: 112px; 
    height: 112px; 
    border-radius: 10px;
    box-shadow: 0 4px 8px rgba(0,0,0,0.2);
    transition: transform 0.2s;
    !important; 
}
.output:hover {
    transform: scale(1.05);
}
.gallery { 
    min-width: 512px;
    min-height: 512px;
    max-height: 512px;
    border-radius: 15px;
    box-shadow: 0 6px 12px rgba(0,0,0,0.3);
    !important; 
}
.guide { text-align: center; color: #e0e0e0; !important; }
.primary-btn {
    background: linear-gradient(45deg, #4a90e2, #357abd);
    border-radius: 8px;
    transition: all 0.3s ease;
}
.primary-btn:hover {
    transform: translateY(-2px);
    box-shadow: 0 5px 15px rgba(74,144,226,0.3);
}
"""

with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', fill_width=True, css=CSS) as demo:
    gr.HTML("""<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fgunship999-SexyImages.hf.space">
               <img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fgunship999-SexyImages.hf.space&countColor=%23263759" />
               </a>""")
    
    with gr.Column(scale=2):
        # 모델 선택 부분 추가
        with gr.Accordion("Model Selection", open=True):
            model_choice = gr.CheckboxGroup(
                models,
                label=f'Choose up to {int(num_models)} models',
                value=default_models,
                interactive=True
            )

        with gr.Group():
            txt_input = gr.Textbox(
                label='Your prompt:', 
                value=preSetPrompt, 
                lines=3, 
                autofocus=1
            )
            neg_input = gr.Textbox(
                label='Negative prompt:', 
                value=negPreSetPrompt, 
                lines=1
            )
            with gr.Accordion("Advanced Settings", open=False):
                with gr.Row():
                    width = gr.Slider(label="Width", maximum=1216, step=32, value=0)
                    height = gr.Slider(label="Height", maximum=1216, step=32, value=0)
                with gr.Row():
                    steps = gr.Slider(label="Steps", maximum=100, step=1, value=0)
                    cfg = gr.Slider(label="Guidance Scale", maximum=30.0, step=0.1, value=0)
                    seed = gr.Slider(label="Seed", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                    seed_rand = gr.Button("🎲", size="sm", elem_classes="primary-btn")
                    seed_rand.click(randomize_seed, None, [seed], queue=False)

        with gr.Row():
            gen_button = gr.Button(
                f'Generate {int(num_models)} Images', 
                variant='primary', 
                scale=3,
                elem_classes="primary-btn"
            )
            random_button = gr.Button(
                'Randomize Models', 
                variant='secondary', 
                scale=1
            )

    with gr.Column(scale=1):
        with gr.Group():
            with gr.Row():
                output = [gr.Image(label=m, show_download_button=True, 
                          elem_classes="output",
                          interactive=False, width=112, height=112, 
                          show_share_button=False, format="png",
                          visible=True) for m in default_models]
                current_models = [gr.Textbox(m, visible=False) 
                                for m in default_models]

    with gr.Column(scale=2):
        gallery = gr.Gallery(
            label="Generated Images", 
            show_download_button=True, 
            elem_classes="gallery",
            interactive=False, 
            show_share_button=False, 
            container=True, 
            format="png",
            preview=True, 
            object_fit="cover", 
            columns=2, 
            rows=2
        ) 

    # 이벤트 핸들러 추가
    model_choice.change(update_imgbox, model_choice, output)
    model_choice.change(extend_choices, model_choice, current_models)
    random_button.click(random_choices, None, model_choice)

    for m, o in zip(current_models, output):
        gen_event = gr.on(
            triggers=[gen_button.click, txt_input.submit],
            fn=gen_fn,
            inputs=[m, txt_input, neg_input, height, width, steps, cfg, seed],
            outputs=[o],
            concurrency_limit=None,
            queue=False
        )
        o.change(add_gallery, [o, m, gallery], [gallery])

demo.launch(show_api=False, max_threads=400)