app.py

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



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


load_fn(models)


num_models = 6
default_models = models[:num_models]



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



with gr.Blocks() as demo:
    with gr.Tab('The Dream'): 
        txt_input = gr.Textbox(label = 'Prompt text')
        gen_button = gr.Button('Generate')
        stop_button = gr.Button('Stop', variant = 'secondary', interactive = False)
        gen_button.click(lambda s: gr.update(interactive = True), None, stop_button)
           
        with gr.Row():
            output = [gr.Image(label = m) for m in default_models]
            current_models = [gr.Textbox(m, visible = False) for m in default_models]
            
            model_choice.change(update_imgbox, model_choice, output)
            model_choice.change(extend_choices, model_choice, current_models)

            for m, o in zip(current_models, output):
                gen_event = gen_button.click(gen_fn, [m, txt_input], o)
                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 {num_models} different models', value = default_models, multiselect = True, max_choices = num_models, interactive = True, filterable = False)

demo.queue(concurrency_count = 200)                        
demo.launch()