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MiniHuggingfaceDiffusion
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import gradio as gr
from random import randint
from all_models import models

from externalmod import gr_Interface_load

import asyncio
import os
from threading import RLock
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 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

default_models = models[:num_models]
inference_timeout = 600

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 gen_fn(model_str, prompt):
    if model_str == 'NA':
        return None
    noise = str('') #str(randint(0, 99999999999))
    return models_load[model_str](f'{prompt} {noise}')



with gr.Blocks() as demo:
    with gr.Tab('🤗 Huggingface Diffusion 🤗'): 
        txt_input = gr.Textbox(label='Your prompt:', lines=4)
        gen_button = gr.Button('Generate up to 6 images in up to 3 minutes total')
        #stop_button = gr.Button('Stop', variant = 'secondary', interactive = False)
        gen_button.click(lambda s: gr.update(interactive = True), None)
        gr.HTML(
        """
            <div style="text-align: center; max-width: 1200px; margin: 0 auto;">
              <div>
                <body>
                <div class="center"><p style="margin-bottom: 10px; color: #000000;">Scroll down to see more images and select models.</p>
                </div>
                </body>
              </div>
            </div>
        """
               )
        with gr.Row():
            output = [gr.Image(label = m, min_width=480) for m in default_models]
            current_models = [gr.Textbox(m, visible = False) for m in default_models]
                        
            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], outputs=[o], concurrency_limit=None, queue=False)
                #stop_button.click(lambda s: gr.update(interactive = False), None, stop_button, cancels = [gen_event])
        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 = gr.CheckboxGroup(models, label = f'Choose up to {num_models} different models from the 2 available! Untick them to only use one!', value = default_models, multiselect = True, max_choices = num_models, interactive = True, filterable = False)
            model_choice.change(update_imgbox, model_choice, output)
            model_choice.change(extend_choices, model_choice, current_models)
        with gr.Row():
            gr.HTML(
    """
        <div class="footer">
        <p> Based on the <a href="https://huggingface.co/spaces/John6666/hfd_test_nostopbutton">Huggingface NoStopButton</a> Space by John6666, <a href="https://huggingface.co/spaces/derwahnsinn/TestGen">TestGen</a> Space by derwahnsinn, the <a href="https://huggingface.co/spaces/RdnUser77/SpacIO_v1">SpacIO</a> Space by RdnUser77 and Omnibus's Maximum Multiplier! For 6 images with the same model check out the <a href="https://huggingface.co/spaces/Yntec/PrintingPress">Printing Press</a>, for the classic UI with prompt enhancer try <a href="https://huggingface.co/spaces/Yntec/blitz_diffusion">Blitz Diffusion!</a>
        </p>
    """
)

demo.queue(default_concurrency_limit=200, max_size=200)
demo.launch(show_api=False, max_threads=400)