app_test.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 = 4
default_models = models[:num_models]
#current_models = default_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 = True) 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}')


def gen_fn2(choices, prompt):
    choices_plus = extend_choices(choices)
    return [gen_fn(m, prompt) for m in choices_plus]



with gr.Blocks() as demo:
    with gr.Tab('Multiple models'): 
        with gr.Accordion('Model selection'):
            model_choice = gr.Dropdown(models, label = f'Choose up to {num_models} different models', value = default_models, multiselect = True, max_choices = num_models, interactive = True)

        txt_input = gr.Textbox(label = 'Prompt text')
        gen_button = gr.Button('Generate')
           
        with gr.Row():
            output = [gr.Image(label = m) for m in default_models]
            current_models = default_models
            
            model_choice.change(update_imgbox, model_choice, output)
            model_choice.change(extend_choices, model_choice, current_models, preprocess = False, postprocess = False)

            for m, o in zip(current_models, output):
                gen_button.click(lambda txt: gen_fn(m, txt), txt_input, o)
                    
            #gen_button.click(gen_fn2, [model_choice, txt_input], output, batch = True, max_batch_size = num_models)
           
            '''
            output = gr.Gallery()
            gen_button.click(lambda models, txt: [gen_fn(m, txt) for m in models], [model_choice, txt_input], output)
            '''
            
    with gr.Tab('Single model'):
        model_choice2 = gr.Dropdown(models, label = 'Choose model', value = models[0], filterable = False)
        txt_input2 = gr.Textbox(label = 'Prompt text')
        gen_button2 = gr.Button('Generate')

        with gr.Row():
            num_images = 6
            output2 = [gr.Image(label = '') for _ in range(num_images)]

        for o in output2:
            gen_button2.click(gen_fn, [model_choice2, txt_input2], o)
    

demo.queue(concurrency_count = 36)                        
demo.launch()