app.py

import os
import gradio as gr
from random import randint
from all_models import models
from datetime import datetime
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.

now2 = 0
nb_models=24
inference_timeout = 300
MAX_SEED = 2**32-1

def split_models(models,nb_models):
    models_temp=[]
    models_lis_temp=[]
    i=0
    for m in models:
        models_temp.append(m)
        i=i+1
        if i%nb_models==0:
            models_lis_temp.append(models_temp)
            models_temp=[]
    if len(models_temp)>1:
        models_lis_temp.append(models_temp)
    return models_lis_temp

def split_models_axb(models,a,b):
    models_temp=[]
    models_lis_temp=[]
    i=0
    nb_models=b
    for m in models:
        for j in range(a):
            models_temp.append(m)
        i=i+1
        if i%nb_models==0:
            models_lis_temp.append(models_temp)
            models_temp=[]
    if len(models_temp)>1:
        models_lis_temp.append(models_temp)
    return models_lis_temp , a*b

def split_models_8x3(models,nb_models):
    models_temp=[]
    models_lis_temp=[]
    i=0
    nb_models_x3=8
    for m in models:
        models_temp.append(m)
        i=i+1
        if i%nb_models_x3==0:
            models_lis_temp.append(models_temp+models_temp+models_temp)
            models_temp=[]
    if len(models_temp)>1:
        models_lis_temp.append(models_temp+models_temp+models_temp)
    return models_lis_temp

"""models_test=split_models_x3(models,nb_models)"""
"""models_test=split_models(models,nb_models)"""
models_test , nb_models =split_models_axb(models,2,20)

def get_current_time():
    now = datetime.now()
    now2 = now
    current_time = now2.strftime("%Y-%m-%d %H:%M:%S")
    kii = "" # ?
    ki = f'{kii} {current_time}'
    return ki

def load_fn_original(models):
    global models_load
    global num_models
    global default_models
    models_load = {}
    num_models = len(models)
    if num_models!=0:
        default_models = models[:num_models]
    else:
        default_models = {}
    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'])
                print(error)
            models_load.update({model: m})

def load_fn(models):
    global models_load
    global num_models
    global default_models
    models_load = {}
    num_models = len(models)
    if num_models!=0:
        default_models = models[:num_models]
    else:
        default_models = {}
    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:
                m = gr.Interface(lambda txt: None, ['text'], ['image'])
                print(error)
            models_load.update({model: m})


"""models = models_test[1]"""
#load_fn_original
load_fn(models)
"""models = {}
load_fn(models)"""
    

def extend_choices(choices):
    return choices + (nb_models - len(choices)) * ['NA']
    """return choices + (num_models - len(choices)) * ['NA']"""

def extend_choices_b(choices):
    choices_plus = extend_choices(choices)
    return [gr.Textbox(m, visible=False) for m in choices_plus]

def update_imgbox(choices):
    choices_plus = extend_choices(choices)
    return [gr.Image(None, label=m, visible=(m != 'NA')) for m in choices_plus]

def choice_group_a(group_model_choice):
    for m in models_test:
        if group_model_choice==m[1]:
            choice=m
    print(choice)
    return choice

def choice_group_b(group_model_choice):
    choice=choice_group_a(group_model_choice)
    choice = extend_choices(choice)
    """return [gr.Image(label=m, min_width=170, height=170) for m in choice]"""
    return [gr.Image(None, label=m, visible=(m != 'NA')) for m in choice]

def choice_group_c(group_model_choice):
    choice=choice_group_a(group_model_choice)
    choice = extend_choices(choice)
    return [gr.Textbox(m, visible=False) for m in choice]

def choice_group_d(var_Test):
    (gen_button,stop_button,output,current_models, txt_input)=var_Test
    for m, o in zip(current_models, output):
        gen_event = gen_button.click(gen_fn, [m, txt_input], o)
        stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
    return gen_event

def test_pass(test):
    if test==os.getenv('p'):
        print("ok")
        return gr.Dropdown(label="test Model", show_label=False, choices=list(models_test) , allow_custom_value=True)
    else:
        print("nop")
        return gr.Dropdown(label="test Model", show_label=False, choices=list([]) , allow_custom_value=True)

# https://huggingface.co/docs/api-inference/detailed_parameters
# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
async def infer(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1, timeout=inference_timeout):
    from pathlib import Path
    kwargs = {}
    if height is not None and height >= 256: kwargs["height"] = height
    if width is not None and width >= 256: kwargs["width"] = width
    if steps is not None and steps >= 1: kwargs["num_inference_steps"] = steps
    if cfg is not None and cfg > 0: cfg = kwargs["guidance_scale"] = cfg
    noise = ""
    if seed >= 0: kwargs["seed"] = seed
    else:
        rand = randint(1, 500)
        for i in range(rand):
            noise += " "
    task = asyncio.create_task(asyncio.to_thread(models_load[model_str].fn,
                               prompt=f'{prompt} {noise}', negative_prompt=nprompt, **kwargs, token=HF_TOKEN))
    await asyncio.sleep(0)
    try:
        result = await asyncio.wait_for(task, timeout=timeout)
    except (Exception, asyncio.TimeoutError) as e:
        print(e)
        print(f"Task timed out: {model_str}")
        if not task.done(): task.cancel()
        result = None
    if task.done() and result is not None:
        with lock:
            png_path = "image.png"
            result.save(png_path)
            image = str(Path(png_path).resolve())
        return image
    return None

def gen_fn(model_str, prompt, nprompt="", height=None, width=None, steps=None, cfg=None, seed=-1):
    if model_str == 'NA':
        return None
    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"Task aborted: {model_str}")
        result = None
    finally:
        loop.close()
    return result

def gen_fn_original(model_str, prompt):
    if model_str == 'NA':
        return None
    noise = str(randint(0, 9999))
    try :
        m=models_load[model_str](f'{prompt} {noise}')
    except Exception as error : 
        print("error : " + model_str)
        print(error)
        m=False
    
    return m

def make_me():
   # with gr.Tab('The Dream'): 
        with gr.Row():
            #txt_input = gr.Textbox(lines=3, width=300, max_height=100)
            #txt_input = gr.Textbox(label='Your prompt:', lines=3, width=300, max_height=100)
            with gr.Column(scale=4):
                with gr.Group():
                    txt_input = gr.Textbox(label='Your prompt:', lines=3)
                    with gr.Accordion("Advanced", open=False, visible=True):
                        neg_input = gr.Textbox(label='Negative prompt:', lines=1)
                        with gr.Row():
                            width = gr.Number(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                            height = gr.Number(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
                        with gr.Row():
                            steps = gr.Number(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
                            cfg = gr.Number(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)
                #gen_button = gr.Button('Generate images', width=150, height=30)
            #stop_button = gr.Button('Stop', variant='secondary', interactive=False, width=150, height=30)
            gen_button = gr.Button('Generate images', scale=3)
            stop_button = gr.Button('Stop', variant='secondary', interactive=False, scale=1)

            gen_button.click(lambda: gr.update(interactive=True), None, stop_button)
            #gr.HTML("""
            #<div style="text-align: center; max-width: 100%; margin: 0 auto;">
            #    <body>
            #    </body>
            #</div>
            #""")
        with gr.Row():
            """output = [gr.Image(label=m, min_width=170, height=170) for m in default_models]
            current_models = [gr.Textbox(m, visible=False) for m in default_models]"""
            """choices=[models_test[0][0]]"""
            choices=models_test[0]
            """output = [gr.Image(label=m, min_width=170, height=170) for m in choices]
            current_models = [gr.Textbox(m, visible=False) for m in choices]"""
            output = update_imgbox([choices[0]])
            current_models = extend_choices_b([choices[0]])
            
            for m, o in zip(current_models, output):
                #gen_event = gen_button.click(gen_fn_original, [m, txt_input], o)
                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])
                stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event])
        """with gr.Accordion('Model selection'):
            model_choice = gr.CheckboxGroup(models, label=f' {num_models} different models selected', value=default_models, multiselect=True, max_choices=num_models, interactive=True, filterable=False)
            model_choice.change(update_imgbox, (gen_button,stop_button,group_model_choice), output)
            model_choice.change(extend_choices, model_choice, current_models)
        """     
        
        with gr.Accordion("test", open=True):
            """group_model_choice = gr.Dropdown(label="test Model", show_label=False, choices=list(models_test) , allow_custom_value=True)"""
            group_model_choice = gr.Dropdown(label="test Model", show_label=False, choices=list([]) , allow_custom_value=True)
            group_model_choice.change(choice_group_b,group_model_choice,output)
            group_model_choice.change(choice_group_c,group_model_choice,current_models)
            """group_model_choice.change(choice_group_d,(gen_button,stop_button,output,current_models,txt_input),gen_event)"""
            with gr.Row():
                #txt_input_p = gr.Textbox(label='test', lines=1, width=300, max_height=100)
                txt_input_p = gr.Textbox(label='test', lines=1)
            
                #test_button = gr.Button('test', width=30, height=10)
                test_button = gr.Button('test')
                test_button.click(test_pass,txt_input_p,group_model_choice)
        with gr.Row():
            gr.HTML("""
                <div class="footer">
                <p> Based on the <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!
                </p>
            """)


js_code = """

    
    console.log('ghgh');

"""


with gr.Blocks(theme="Nymbo/Nymbo_Theme", fill_width=True, css="div.float.svelte-1mwvhlq {    position: absolute;    top: var(--block-label-margin);    left: var(--block-label-margin);    background: none;    border: none;}") as demo: 
    gr.Markdown("<script>" + js_code + "</script>")
    make_me()


# https://www.gradio.app/guides/setting-up-a-demo-for-maximum-performance
#demo.queue(concurrency_count=999) # concurrency_count is deprecated in 4.x
demo.queue()
demo.launch()