app.py

import os
import gradio as gr
from random import randint
from operator import itemgetter
from all_models import tags_plus_models,models,models_plus_tags
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
inference_timeout = 300
MAX_SEED = 2**32-1


nb_rep=2
nb_mod_dif=20
nb_models=nb_mod_dif*nb_rep

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 

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

def construct_list_models(tags_plus_models,nb_rep,nb_mod_dif):
    list_temp=[]
    output=[]
    for tag_plus_models in tags_plus_models:
        list_temp=split_models_axb(tag_plus_models[2],nb_rep,nb_mod_dif)
        list_temp2=[]
        i=0
        for elem in list_temp:
            list_temp2.append([tag_plus_models[0]+"_"+str(i)+" : "+elem[0]+" - "+elem[len(elem)-1] ,elem])
            i+=1
        output.append([tag_plus_models[0] + " (" + str(tag_plus_models[1]) + ")",list_temp2])
    return output

models_test = []
models_test = construct_list_models(tags_plus_models,nb_rep,nb_mod_dif)

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)
    i=0
    if num_models!=0:
        default_models = models[:num_models]
    else:
        default_models = {}
    for model in models:
        i+=1
        if i%50==0:
            print("\n\n\n-------"+str(i)+'/'+str(len(models))+"-------\n\n\n")
        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,interactive=False, visible=(m != 'NA')) for m in choices_plus]

def choice_group_a(group_model_choice):
    return group_model_choice

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

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


def cutStrg(longStrg,start,end):
    shortStrg=''
    for i in range(end-start):
        shortStrg+=longStrg[start+i]
    return shortStrg
    
def aff_models_perso(txt_list_perso,nb_models=nb_models,models=models):
    list_perso=[]
    t1=True
    start=txt_list_perso.find('\"')
    if start!=-1:
        while t1:
            start+=1
            end=txt_list_perso.find('\"',start)
            if end != -1:
                txtTemp=cutStrg(txt_list_perso,start,end)
                if txtTemp in models:
                    list_perso.append(cutStrg(txt_list_perso,start,end))
            else :
                t1=False
            start=txt_list_perso.find('\"',end+1)
            if start==-1:
                t1=False
            if len(list_perso)>=nb_models:
                t1=False
    return list_perso

def aff_models_perso_b(txt_list_perso):
    return choice_group_b(aff_models_perso(txt_list_perso))

def aff_models_perso_c(txt_list_perso):
    return choice_group_c(aff_models_perso(txt_list_perso))
            
            
def tag_choice(group_tag_choice):
    return gr.Dropdown(label="List of Models with the chosen Tag", show_label=True, choices=list(group_tag_choice) , interactive = True , filterable = False)

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

def test_pass_aff(test):
    if test==os.getenv('p'):
        return gr.Accordion( open=True, visible=True)
    else:
        return gr.Accordion( open=True, visible=False)


# 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 add_gallery(image, model_str, gallery):
    if gallery is None: gallery = []
    #with lock:
    if image is not None: gallery.append((image, model_str))
    return gallery

def reset_gallery(gallery):
    return add_gallery(None,"",[])

def load_gallery(gallery):
    gallery = reset_gallery(gallery)
    for c in cache_image:
        gallery=add_gallery(c[0],c[1],gallery)
    return gallery
def load_gallery_actu(gallery):
    gallery = reset_gallery(gallery)
    #for c in cache_image_actu:
    for c in sorted(cache_image_actu, key=itemgetter(1)):
        gallery=add_gallery(c[0],c[1],gallery)
    return gallery

def add_cache_image(o,m):
    cache_image.append((o,m))
    #cache_image=sorted(cache_image, key=itemgetter(1))
    return 
def add_cache_image_actu(o,m):
    cache_image_actu.append((o,m))
    #cache_image_actu=sorted(cache_image_actu, key=itemgetter(1))
    return
def reset_cache_image():
    cache_image.clear()
    return 
def reset_cache_image_actu():
    cache_image_actu.clear()
    return 

def disp_models(group_model_choice,nb_rep=nb_rep):
    listTemp=[]
    strTemp='\n'
    i=0
    for m in group_model_choice:
        if m not in listTemp:
            listTemp.append(m)
    for m in listTemp:
        i+=1
        strTemp+="\"" + m + "\",\n"
        if i%(8/nb_rep)==0:
            strTemp+="\n"
    return gr.Textbox(label="models",value=strTemp)

def search_models(str_search,tags_plus_models=tags_plus_models):
    output1="\n"
    output2=""
    for m in tags_plus_models[0][2]:
        if m.find(str_search)!=-1:
            output1+="\"" + m + "\",\n"
    outputPlus="\n From tags : \n\n"
    for tag_plus_models in tags_plus_models:
        if str_search.lower() == tag_plus_models[0].lower() and str_search!="":
            for m in tag_plus_models[2]:
                output2+="\"" + m + "\",\n"
    if output2 != "":
        output=output1+outputPlus+output2
    else :
        output=output1
    return gr.Textbox(label="out",value=output)

def search_info(txt_search_info,models_plus_tags=models_plus_tags):
    outputList=[]
    if txt_search_info.find("\"")!=-1:
        start=txt_search_info.find("\"")+1
        end=txt_search_info.find("\"",start)
        m_name=cutStrg(txt_search_info,start,end)
    else :
        m_name = txt_search_info
    for m in models_plus_tags:
        if m_name == m[0]:
            outputList=m[1]
    if len(outputList)==0:
        outputList.append("Model Not Find")
    return gr.Textbox(label="out",value=outputList)


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.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)
                #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][1][0][1]
            """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]"""
            global output_g
            global current_models_g
            output_g = update_imgbox([choices[0]])
            current_models_g = extend_choices_b([choices[0]])

            
            for m, o in zip(current_models_g, output_g):
                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.Row():
            txt_input_p = gr.Textbox(label="Pass", lines=1)
            test_button = gr.Button(' ')    

            
        with gr.Accordion( open=True, visible=False) as stuffs:
            with gr.Accordion("Gallery",open=False):
                with gr.Row():
                    global cache_image
                    global cache_image_actu
                    cache_image=[]
                    cache_image_actu=[]
                    with gr.Column():
                        b11 = gr.Button('Load Galerry Actu')
                        b12 = gr.Button('Load Galerry All')
                    gallery = 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=4,rows=4) 
                    with gr.Column():
                        b21 = gr.Button('Reset Gallery')
                        b22 = gr.Button('Reset Gallery All')
                    b11.click(load_gallery_actu,gallery,gallery)
                    b12.click(load_gallery,gallery,gallery)
                    b21.click(reset_gallery,gallery,gallery)
                    b22.click(reset_cache_image,[],gallery)
                    for m, o in zip(current_models_g, output_g):
                        #o.change(add_gallery, [o, m, gallery], [gallery])
                        o.change(add_cache_image,[o,m],[])
                        o.change(add_cache_image_actu,[o,m],[])
                gen_button.click(reset_cache_image_actu, [], [])
                
            with gr.Group():
                with gr.Row():
                    group_tag_choice = gr.Dropdown(label="Lists Tags", show_label=True, choices=list([]) , interactive = True)
                    
                with gr.Row():
                    group_model_choice = gr.Dropdown(label="List of Models with the chosen Tag", show_label=True, choices=list([]) , interactive = True)
                    group_model_choice.change(choice_group_b,group_model_choice,output_g)
                    group_model_choice.change(choice_group_c,group_model_choice,current_models_g)
                    group_tag_choice.change(tag_choice,group_tag_choice,group_model_choice)
                
            with gr.Row():
                txt_list_models=gr.Textbox(label="Models Actu",value="")
                group_model_choice.change(disp_models,group_model_choice,txt_list_models)
                
            with gr.Row():
                txt_list_perso = gr.Textbox(label='List Models Perso')
            
                button_list_perso = gr.Button('Load')
                button_list_perso.click(aff_models_perso_b,txt_list_perso,output_g)
                button_list_perso.click(aff_models_perso_c,txt_list_perso,current_models_g)
    
            with gr.Row():
                txt_search = gr.Textbox(label='Search in')
                txt_output_search = gr.Textbox(label='Search out')
                button_search = gr.Button('Research')
                button_search.click(search_models,txt_search,txt_output_search)

            with gr.Row():
                txt_search_info = gr.Textbox(label='Search info in')
                txt_output_search_info = gr.Textbox(label='Search info out')
                button_search_info = gr.Button('Research info')
                button_search_info.click(search_info,txt_search_info,txt_output_search_info)
        
            
        with gr.Row():
            test_button.click(test_pass_aff,txt_input_p,stuffs)
            test_button.click(test_pass,txt_input_p,group_tag_choice)
            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(default_concurrency_limit=200, max_size=200)
demo.launch(max_threads=400)