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. 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

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

def load_fn(models):
    global models_load
    models_load = {}
    for model in models:
        if model not in models_load:
            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[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)) * ['NA']

def update_imgbox(choices):
    choices_plus = extend_choices(choices)
    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 = {"height": height if height > 0 else None, "width": width if width > 0 else None, 
              "num_inference_steps": steps if steps > 0 else None, "guidance_scale": cfg if cfg > 0 else None}
    
    if seed == -1:
        kwargs["seed"] = randomize_seed()
    else:
        kwargs["seed"] = seed

    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn, prompt=prompt, negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
    
    try:
        result = await asyncio.wait_for(task, timeout=timeout)
    except asyncio.TimeoutError as e:
        print(f"Task timed out: {model_str}")
        task.cancel()
        result = None
    except Exception as e:
        print(f"Error generating image: {model_str} - {e}")
        task.cancel()
        result = None
    
    if result and not isinstance(result, tuple):
        png_path = f"{model_str.replace('/', '_')}_{get_current_time()}_{kwargs['seed']}.png"
        with lock:
            image = save_image(result, png_path, model_str, prompt, nprompt, height, width, steps, cfg, kwargs["seed"])
        return image
    return None

def gen_fn(model_str, prompt, nprompt="", height=0, width=0, steps=0, cfg=0, seed=-1):
    loop = asyncio.new_event_loop()
    try:
        result = loop.run_until_complete(infer(model_str, prompt, nprompt, height, width, steps, cfg, seed, inference_timeout))
    except (Exception, asyncio.CancelledError) as e:
        print(f"Error generating image: {e}")
        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 image is not None:
        gallery = [(image, model_str)] + gallery[:5]  # Keep only the latest 6 images
    return gallery

# Interface Layout
CSS = """
.gradio-container { max-width: 1200px; margin: 0 auto; }
.output { width: 112px; height: 112px; }
.gallery { min-width: 512px; min-height: 512px; }
"""

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;
    }
}
"""

with gr.Blocks(fill_width=True, css=CSS) as demo:
    gr.HTML("")  
    with gr.Tab('6 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)
                random_button = gr.Button(f'Randomize Models', variant='secondary', scale=1)

            gr.Markdown("", elem_classes="guide")

        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="Output", 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)

        # Inside the `gr.Blocks` context where the components are defined:
        for i, o in enumerate(output):
            # Ensure event handler is inside `gr.Blocks`
            num_images.change(
                lambda num_images, i=i: gr.update(visible=(i < num_images)),  # `i` corresponds to the index of the image
                [num_images],  # Only num_images needs to be an input
                o,  # Outputs the updated visibility of the image `o`
                queue=False
            )

            # Image generation function
            gen_event2 = gr.on(
                triggers=[gen_button.click, txt_input.submit],
                fn=gen_fn,
                inputs=[i, num_images, model_choice2, txt_input, neg_input, height, width, steps, cfg, seed],
                outputs=[o]
            )

            # Update gallery when a new image is generated
            o.change(add_gallery, [o, model_choice2, gallery], [gallery])

        # Model selection and updating outputs
        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)

    # Single model section
    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_images2 = gr.Slider(1, max_images, value=max_images, step=1, label='Number of images')

            with gr.Row():
                gen_button2 = gr.Button('Let the machine hallucinate', variant='primary', scale=2)

        with gr.Column(scale=1):
            with gr.Group():
                with gr.Row():
                    output2 = [gr.Image(label='', show_download_button=True, elem_classes="output",
                               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, elem_classes="gallery",
                                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_images2.change(lambda i, n: gr.update(visible=(i < n)), [img_i, num_images2], o, queue=False)
            gen_event2 = gr.on(
                triggers=[gen_button2.click, txt_input2.submit],
                fn=gen_fn,
                inputs=[img_i, num_images2, model_choice2, txt_input2, neg_input2, height2, width2, steps2, cfg2, seed2],
                outputs=[o]
            )

            o.change(add_gallery, [o, model_choice2, gallery2], [gallery2])

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