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 = "High fashion studio foto shoot. tall slender 18+ caucasian woman. gorgeous face. high waist sexy bodysuit. photorealistic. f1.4"
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()
    current_time = now.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 = 12
max_images = 12
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)


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()
    else: 
        theSeed = seed
    kwargs["seed"] = theSeed
        
    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"infer: 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)
        print(f"infer: exception: {model_str}")
        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 =  model_str.replace("/", "_") + " - " + get_current_time() + "_" + str(theSeed) + ".png"
            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, theSeed)
        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"gen_fn: 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="""
<script>

/*
function simulateButtonPress_() {
    const button = document.getElementById('simulate-button');
    if (button) {
        button.click();  // Simulate the button press
        console.log('Button Pressed!');
    }
}
*/

function simulateButtonPress() {
    console.log('Button Pressed!');
}

// Function to observe image changes
function observeImageChanges() {
    // Select all images with the 'image-monitor' class
    const images = document.querySelectorAll('.svelte-1pijsyv');
    
    // Create a MutationObserver to watch for changes in the image src
    const observer = new MutationObserver((mutationsList, observer) => {
        mutationsList.forEach(mutation => {
            if (mutation.type === 'attributes' && mutation.attributeName === 'src') {
                // If the image src changes, simulate button press
                console.log('Image changed!');
                simulateButtonPress();
            }
        });
    });
    
    // Observer options: observe changes to attributes (like src)
    const config = { attributes: true };

    // Start observing each image
    images.forEach(image => {
        observer.observe(image, config);
    });
}

// Start observing


window.addEventListener('load', () => {
    observeImageChanges();
    console.log("Yo");
});
    
</script>
"""

CSS="""
<style>
    .image-monitor {
        border:1px solid red;
    }

    /*
    .svelte-1pijsyv{
        border:1px solid green;
    }
    */
    
    .gallery-container{
        max-height: 512px;
    }

    .butt{
        background-color:#2b4764 !important
    }
    .butt:hover{
        background-color:#3a6c9f !important;
    }
    
</style>
"""

# with gr.Blocks(fill_width=True, head=js) as demo:
with gr.Blocks(head=CSS + JS) as demo:
    with gr.Tab(str(num_models) + ' Models'):
        with gr.Column(scale=2):
            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", open=False, visible=True):
                    with gr.Row():
                        width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                        height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                    with gr.Row():
                        steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                        cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                        seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                        seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
                        seed_rand.click(randomize_seed, None, [seed], queue=False)
            with gr.Row():
                gen_button = gr.Button(f'Generate up to {int(num_models)} images', variant='primary', scale=3, elem_classes=["butt"])
                random_button = gr.Button(f'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=["image-monitor"],
                              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="Output", show_download_button=True,
                                interactive=False, show_share_button=False, container=True, format="png",
                                preview=True, object_fit="cover", columns=2, rows=2) 

        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])

        with gr.Column(scale=4):
            with gr.Accordion('Model selection'):
                model_choice = gr.CheckboxGroup(models, label = f'Choose up to {int(num_models)} different models from the {len(models)} available!', value=default_models, interactive=True)
                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)

    with gr.Tab('Single model'):
        with gr.Column(scale=2):
            model_choice2 = gr.Dropdown(models, label='Choose model', value=models[0])
            with gr.Group():
                txt_input2 = gr.Textbox(label='Your prompt:', value = preSetPrompt, lines=3, autofocus=1)
                neg_input2 = gr.Textbox(label='Negative prompt:', value=negPreSetPrompt, lines=1)
                with gr.Accordion("Advanced", open=False, visible=True):
                    with gr.Row():
                        width2 = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                        height2 = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                    with gr.Row():
                        steps2 = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                        cfg2 = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
                        seed2 = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                        seed_rand2 = gr.Button("Randomize Seed", size="sm", variant="secondary")
                        seed_rand2.click(randomize_seed, None, [seed2], queue=False)
            num_images = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')
            with gr.Row():
                gen_button2 = gr.Button('Let the machine halucinate', variant='primary', scale=2, elem_classes=["butt"])

        with gr.Column(scale=1):
            with gr.Group():
                with gr.Row():
                    output2 = [gr.Image(label='', show_download_button=True,
                               interactive=False, width=112, height=112, visible=True, format="png",
                               show_share_button=False, show_label=False) for _ in range(max_images)]

        with gr.Column(scale=2):
            gallery2 = gr.Gallery(label="Output", show_download_button=True,
                                interactive=False, show_share_button=True, container=True, format="png",
                                preview=True, object_fit="cover", columns=2, rows=2) 

        for i, o in enumerate(output2):
            img_i = gr.Number(i, visible=False)
            num_images.change(lambda i, n: gr.update(visible = (i < n)), [img_i, num_images], o, queue=False)
            gen_event2 = gr.on(triggers=[gen_button2.click, txt_input2.submit],
                               fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5: gen_fn(m, t1, t2, n1, n2, n3, n4, n5) if (i < n) else None,
                               inputs=[img_i, num_images, model_choice2, txt_input2, neg_input2,
                                       height2, width2, steps2, cfg2, seed2], outputs=[o],
                                       concurrency_limit=None, queue=False)
            o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])

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