import gradio as gr
import os
from pathlib import Path
import argparse
import shutil
# from train_dreambooth import run_training
from textual_inversion import run_training
from convertosd import convert
from PIL import Image
from slugify import slugify
import requests
import torch
import zipfile
import tarfile
import urllib.parse
import gc
from diffusers import StableDiffusionPipeline
from huggingface_hub import snapshot_download


is_spaces = True if "SPACE_ID" in os.environ else False
#is_shared_ui = True if "IS_SHARED_UI" in os.environ else False
if(is_spaces):
    is_shared_ui = True if ("lvkaokao/textual-inversion-training" in os.environ['SPACE_ID'] or "Intel/textual-inversion-training" in os.environ['SPACE_ID']) else False
else:
    is_shared_ui = False

css = '''
    .instruction{position: absolute; top: 0;right: 0;margin-top: 0px !important}
    .arrow{position: absolute;top: 0;right: -110px;margin-top: -8px !important}
    #component-4, #component-3, #component-10{min-height: 0}
    .duplicate-button img{margin: 0}
'''
maximum_concepts = 1

#Pre download the files
'''
model_v1_4 = snapshot_download(repo_id="CompVis/stable-diffusion-v1-4")
#model_v1_5 = snapshot_download(repo_id="runwayml/stable-diffusion-v1-5")
model_v1_5 = snapshot_download(repo_id="stabilityai/stable-diffusion-2")
model_v2_512 = snapshot_download(repo_id="stabilityai/stable-diffusion-2-base", revision="fp16")
safety_checker = snapshot_download(repo_id="multimodalart/sd-sc")
'''
model_v1_4 = "CompVis/stable-diffusion-v1-4"
model_v1_5 = "stabilityai/stable-diffusion-2"
model_v2_512 = "stabilityai/stable-diffusion-2-base"

model_to_load = model_v1_4


with zipfile.ZipFile("mix.zip", 'r') as zip_ref:
    zip_ref.extractall(".")

def swap_text(option):
    mandatory_liability = "You must have the right to do so and you are liable for the images you use, example:"
    if(option == "object"):
        instance_prompt_example = "cttoy"
        freeze_for = 30
        return [f"You are going to train `object`(s), upload 5-10 images of each object you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''<img src="file/cat-toy.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for, gr.update(visible=False)]
    elif(option == "person"):
       instance_prompt_example = "julcto"
       freeze_for = 70
       return [f"You are going to train a `person`(s), upload 10-20 images of each person you are planning on training on from different angles/perspectives. {mandatory_liability}:", '''<img src="file/person.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for, gr.update(visible=True)]
    elif(option == "style"):
        instance_prompt_example = "trsldamrl"
        freeze_for = 10
        return [f"You are going to train a `style`, upload 10-20 images of the style you are planning on training on. Name the files with the words you would like  {mandatory_liability}:", '''<img src="file/trsl_style.png" />''', f"You should name your concept with a unique made up word that has low chance of the model already knowing it (e.g.: `{instance_prompt_example}` here). Images will be automatically cropped to 512x512.", freeze_for, gr.update(visible=False)]

def swap_base_model(selected_model):
    global model_to_load
    if(selected_model == "v1-4"):
        model_to_load = model_v1_4
    elif(selected_model == "v1-5"):
        model_to_load = model_v1_5
    else:
        model_to_load = model_v2_512

def count_files(*inputs):
    file_counter = 0
    concept_counter = 0
    for i, input in enumerate(inputs):
        if(i < maximum_concepts-1):
            files = inputs[i]
            if(files):
                concept_counter+=1
                file_counter+=len(files)
    uses_custom = inputs[-1] 
    type_of_thing = inputs[-4]
    if(uses_custom):
        Training_Steps = int(inputs[-3])
    else:
        Training_Steps = file_counter*200
        if(Training_Steps > 2400):
            Training_Steps=2400
        elif(Training_Steps < 1400):
            Training_Steps=1400
    if(is_spaces):
        summary_sentence = f'''The training should take around 24 hours for 1000 steps using the default free CPU.<br><br>'''
    else:
        summary_sentence = f'''You are going to train {concept_counter} {type_of_thing}(s), with {file_counter} images for {Training_Steps} steps.<br><br>'''
        
    return([gr.update(visible=True), gr.update(visible=True, value=summary_sentence)])

def update_steps(*files_list):
    file_counter = 0
    for i, files in enumerate(files_list):
        if(files):
            file_counter+=len(files)
    return(gr.update(value=file_counter*200))

def pad_image(image):
    w, h = image.size
    if w == h:
        return image
    elif w > h:
        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
        new_image.paste(image, (0, (w - h) // 2))
        return new_image
    else:
        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
        new_image.paste(image, ((h - w) // 2, 0))
        return new_image

def train(*inputs):
    if is_shared_ui:
        raise gr.Error("This Space only works in duplicated instances")
    
    torch.cuda.empty_cache()
    if 'pipe' in globals():
        global pipe, pipe_is_set
        del pipe
        pipe_is_set = False
        gc.collect()
        
    if os.path.exists("output_model"): shutil.rmtree('output_model')
    if os.path.exists("concept_images"): shutil.rmtree('concept_images')
    if os.path.exists("diffusers_model.tar"): os.remove("diffusers_model.tar")
    if os.path.exists("model.ckpt"): os.remove("model.ckpt")
    if os.path.exists("hastrained.success"): os.remove("hastrained.success")
    file_counter = 0
    print(inputs)

    os.makedirs('concept_images', exist_ok=True)
    files = inputs[maximum_concepts*3]
    init_word = inputs[maximum_concepts*2]
    prompt = inputs[maximum_concepts]
    if(prompt == "" or prompt == None):
        raise gr.Error("You forgot to define your concept prompt")

    for j, file_temp in enumerate(files):
        file = Image.open(file_temp.name)
        image = pad_image(file)
        image = image.resize((512, 512))
        extension = file_temp.name.split(".")[1]
        image = image.convert('RGB')
        image.save(f'concept_images/{j+1}.jpg', format="JPEG", quality = 100)
        file_counter += 1


    os.makedirs('output_model',exist_ok=True)
    uses_custom = inputs[-1] 
    type_of_thing = inputs[-4]
    remove_attribution_after = inputs[-6]
    experimental_face_improvement = inputs[-9]
    which_model = inputs[-10]
    if(uses_custom):
        Training_Steps = int(inputs[-3])
    else:
        Training_Steps = 1000

    print(os.listdir("concept_images"))

    args_general = argparse.Namespace(
        pretrained_model_name_or_path = model_to_load,
        train_data_dir="concept_images",
        learnable_property=type_of_thing,
        placeholder_token=prompt,
        initializer_token=init_word,
        resolution=512,
        train_batch_size=1,
        gradient_accumulation_steps=2,
        use_bf16=True,
        max_train_steps=Training_Steps,
        learning_rate=5.0e-4,
        scale_lr=True,
        lr_scheduler="constant",
        lr_warmup_steps=0,
        output_dir="output_model",
    )
    print("Starting single training...")
    lock_file = open("intraining.lock", "w")
    lock_file.close()
    run_training(args_general)

    gc.collect()
    torch.cuda.empty_cache()
    if(which_model in ["v1-5"]):
        print("Adding Safety Checker to the model...")
        shutil.copytree(f"{safety_checker}/feature_extractor", "output_model/feature_extractor")
        shutil.copytree(f"{safety_checker}/safety_checker", "output_model/safety_checker")
        shutil.copy(f"model_index.json", "output_model/model_index.json")
    
    if(not remove_attribution_after):
        print("Archiving model file...")
        with tarfile.open("diffusers_model.tar", "w") as tar:
            tar.add("output_model", arcname=os.path.basename("output_model"))
        if os.path.exists("intraining.lock"): os.remove("intraining.lock")
        trained_file = open("hastrained.success", "w")
        trained_file.close()
        print(os.listdir("output_model"))
        print("Training completed!")
        return [
            gr.update(visible=True, value=["diffusers_model.tar"]), #result
            gr.update(visible=True), #try_your_model
            gr.update(visible=True), #push_to_hub
            gr.update(visible=True), #convert_button
            gr.update(visible=False), #training_ongoing
            gr.update(visible=True) #completed_training
        ]
    else:
        hf_token = inputs[-5]
        model_name = inputs[-7]
        where_to_upload = inputs[-8]
        push(model_name, where_to_upload, hf_token, which_model, True)
        hardware_url = f"https://huggingface.co./spaces/{os.environ['SPACE_ID']}/hardware"
        headers = { "authorization" : f"Bearer {hf_token}"}
        body = {'flavor': 'cpu-basic'}
        requests.post(hardware_url, json = body, headers=headers)

import time
pipe_is_set = False
def generate(prompt, steps):

    print("prompt: ", prompt)
    print("steps: ", steps)

    torch.cuda.empty_cache()
    from diffusers import StableDiffusionPipeline
    global pipe_is_set
    if(not pipe_is_set):
        global pipe
        if torch.cuda.is_available():
            pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float16)
            pipe = pipe.to("cuda")
        else:
            pipe = StableDiffusionPipeline.from_pretrained("./output_model", torch_dtype=torch.float)
        pipe_is_set = True
    
    start_time = time.time()
    image = pipe(prompt, num_inference_steps=steps, guidance_scale=7.5).images[0]
    print("cost: ", time.time() - start_time)
    return(image)
    
def push(model_name, where_to_upload, hf_token, which_model, comes_from_automated=False):

    if(not os.path.exists("model.ckpt")):
        convert("output_model", "model.ckpt")
    from huggingface_hub import HfApi, HfFolder, CommitOperationAdd
    from huggingface_hub import create_repo
    model_name_slug = slugify(model_name)
    api = HfApi()
    your_username = api.whoami(token=hf_token)["name"]
    if(where_to_upload == "My personal profile"):    
        model_id = f"{your_username}/{model_name_slug}"
    else:
        model_id = f"sd-dreambooth-library/{model_name_slug}"
        headers = {"Authorization" : f"Bearer: {hf_token}", "Content-Type": "application/json"}
        response = requests.post("https://huggingface.co./organizations/sd-dreambooth-library/share/SSeOwppVCscfTEzFGQaqpfcjukVeNrKNHX", headers=headers)
    
    images_upload = os.listdir("concept_images")
    image_string = ""
    instance_prompt_list = []
    previous_instance_prompt = ''
    for i, image in enumerate(images_upload):
        instance_prompt = image.split("_")[0]
        if(instance_prompt != previous_instance_prompt):
            title_instance_prompt_string = instance_prompt
            instance_prompt_list.append(instance_prompt)
        else:
            title_instance_prompt_string = ''
        previous_instance_prompt = instance_prompt
        image_string = f'''{title_instance_prompt_string} {"(use that on your prompt)" if title_instance_prompt_string != "" else ""} 
{image_string}![{instance_prompt} {i}](https://huggingface.co./{model_id}/resolve/main/concept_images/{urllib.parse.quote(image)})'''
    readme_text = f'''---
license: creativeml-openrail-m
tags:
- text-to-image
---
### {model_name} Dreambooth model trained by {api.whoami(token=hf_token)["name"]} with [Hugging Face Dreambooth Training Space](https://huggingface.co./spaces/multimodalart/dreambooth-training) with the {which_model} base model

You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts! 

Sample pictures of:
{image_string}
'''
    #Save the readme to a file
    readme_file = open("model.README.md", "w")
    readme_file.write(readme_text)
    readme_file.close()
    #Save the token identifier to a file
    text_file = open("token_identifier.txt", "w")
    text_file.write(', '.join(instance_prompt_list))
    text_file.close()
    try:
        create_repo(model_id,private=True, token=hf_token)
    except:
        import time
        epoch_time = str(int(time.time()))
        create_repo(f"{model_id}-{epoch_time}", private=True,token=hf_token)
    operations = [
        CommitOperationAdd(path_in_repo="token_identifier.txt", path_or_fileobj="token_identifier.txt"),
        CommitOperationAdd(path_in_repo="README.md", path_or_fileobj="model.README.md"),
        CommitOperationAdd(path_in_repo=f"model.ckpt",path_or_fileobj="model.ckpt")
    ]
    api.create_commit(
    repo_id=model_id,
    operations=operations,
    commit_message=f"Upload the model {model_name}",
    token=hf_token
    )
    api.upload_folder(
    folder_path="output_model",
    repo_id=model_id,
    token=hf_token
    )
    api.upload_folder(
    folder_path="concept_images",
    path_in_repo="concept_images",
    repo_id=model_id,
    token=hf_token
    )
    if is_spaces:
        if(not comes_from_automated):
            extra_message = "Don't forget to remove the GPU attribution after you play with it."
        else:
            extra_message = "The GPU has been removed automatically as requested, and you can try the model via the model page"
        api.create_discussion(repo_id=os.environ['SPACE_ID'], title=f"Your model {model_name} has finished trained from the Dreambooth Train Spaces!", description=f"Your model has been successfully uploaded to: https://huggingface.co./{model_id}. {extra_message}",repo_type="space", token=hf_token)

    return [gr.update(visible=True, value=f"Successfully uploaded your model. Access it [here](https://huggingface.co./{model_id})"), gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"])]

def convert_to_ckpt():
    convert("output_model", "model.ckpt")
    return gr.update(visible=True, value=["diffusers_model.tar", "model.ckpt"])

def check_status(top_description):
    print('=='*20)
    print(os.listdir("./"))

    if os.path.exists("hastrained.success"):
        if is_spaces:
            update_top_tag = gr.update(value=f'''
            <div class="gr-prose" style="max-width: 80%">
                <h2>Your model has finished training ✅</h2>
                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub). Once you are done, your model is safe, and you don't want to train a new one, go to the <a href="https://huggingface.co./spaces/{os.environ['SPACE_ID']}">settings page</a> and downgrade your Space to a CPU Basic</p> 
            </div>
            ''')
        else:
            update_top_tag = gr.update(value=f'''
            <div class="gr-prose" style="max-width: 80%">
                <h2>Your model has finished training ✅</h2>
                <p>Yay, congratulations on training your model. Scroll down to play with with it, save it (either downloading it or on the Hugging Face Hub).</p> 
            </div>
            ''')
        show_outputs = True
    elif os.path.exists("intraining.lock"):
        update_top_tag = gr.update(value='''
        <div class="gr-prose" style="max-width: 80%">
            <h2>Don't worry, your model is still training! ⌛</h2>
            <p>You closed the tab while your model was training, but it's all good! It is still training right now. You can click the "Open logs" button above here to check the training status. Once training is done, reload this tab to interact with your model</p> 
        </div>
        ''')
        show_outputs = False
    else:
        update_top_tag = gr.update(value=top_description)
        show_outputs = False
    if os.path.exists("diffusers_model.tar"):
       update_files_tag = gr.update(visible=show_outputs, value=["diffusers_model.tar"])
    else:
       update_files_tag = gr.update(visible=show_outputs)
    return [
        update_top_tag, #top_description
        gr.update(visible=show_outputs), #try_your_model
        gr.update(visible=show_outputs), #push_to_hub
        update_files_tag, #result
        gr.update(visible=show_outputs), #convert_button
    ]

def checkbox_swap(checkbox):
    return [gr.update(visible=checkbox), gr.update(visible=checkbox), gr.update(visible=checkbox), gr.update(visible=checkbox)]

with gr.Blocks(css=css) as demo:
    with gr.Box():
        if is_shared_ui:
            top_description = gr.HTML(f'''
                <div class="gr-prose" style="max-width: 80%">
                <h2>Attention - This Space doesn't work in this shared UI</h2>
                <p>For it to work, you can either run locally or duplicate the Space and run it on your own profile using the free CPU or a (paid) private T4 GPU for training. CPU training takes a long time while each T4 costs US$0.60/h which should cost < US$1 to train most models using default settings!&nbsp;&nbsp;<a class="duplicate-button" style="display:inline-block" href="https://huggingface.co./spaces/{os.environ['SPACE_ID']}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
                <img class="instruction" src="file/duplicate.png"> 
                <img class="arrow" src="file/arrow.png" />
                </div>
            ''')
        elif(is_spaces):
            top_description = gr.HTML(f'''
                    <div class="gr-prose" style="max-width: 80%">
                    <h2>You have successfully duplicated the Textual Inversion Training Space 🎉</h2>
                    <p>If you want to use CPU, it will take a long time to run the training below. If you want to use GPU, please get this ready: <a href="https://huggingface.co./spaces/{os.environ['SPACE_ID']}/settings">attribute a T4 GPU to it (via the Settings tab)</a> and run the training below. You will be billed by the minute from when you activate the GPU until when it is turned it off.</p> 
                    </div>
            ''')
        else:
            top_description = gr.HTML(f'''
                    <div class="gr-prose" style="max-width: 80%">
                    <h2>You have successfully cloned the Dreambooth Training Space locally 🎉</h2>
                    <p>Do a <code>pip install requirements-local.txt</code></p> 
                    </div>
                ''')
    gr.Markdown("# Textual Inversion Training UI 💭")
    gr.Markdown("Customize Stable Diffusion by training it on a new concept. This Space is based on [Intel® Neural Compressor](https://github.com/intel/neural-compressor/tree/master/examples/pytorch/diffusion_model/diffusers/textual_inversion) with [🧨 diffusers](https://github.com/huggingface/diffusers)")
    
    with gr.Row() as what_are_you_training:
        type_of_thing = gr.Dropdown(label="What would you like to train?", choices=["object", "person", "style"], value="object", interactive=True)
        base_model_to_use = gr.Dropdown(label="Which base model would you like to use?", choices=["v1-4", "v1-5", "v2-512"], value="v1-4", interactive=True)
    
    #Very hacky approach to emulate dynamically created Gradio components   
    with gr.Row() as upload_your_concept:
        with gr.Column():
            thing_description = gr.Markdown("You are going to train an `object`, please upload 1-5 images of the object to teach new concepts to Stable Diffusion, example")
            thing_experimental = gr.Checkbox(label="Improve faces (prior preservation) - can take longer training but can improve faces", visible=False, value=False)
            thing_image_example = gr.HTML('''<img src="file/dicoo-toy.png" class="aligncenter" height="128" width="128" />''')
            things_naming = gr.Markdown("You should name your concept with a unique made up word that never appears in the model vocab (e.g.: `dicoo*` here). **The meaning of the initial word** is to initialize the concept word embedding which will make training easy (e.g.: `toy` here). Images will be automatically cropped to 512x512.")
            
        with gr.Column():
            file_collection = []
            concept_collection = []
            init_collection = []
            buttons_collection = []
            delete_collection = []
            is_visible = []

            row = [None] * maximum_concepts
            for x in range(maximum_concepts):
                ordinal = lambda n: "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4])
                if(x == 0):
                    visible = True
                    is_visible.append(gr.State(value=True))
                else:
                    visible = False
                    is_visible.append(gr.State(value=False))

                file_collection.append(gr.File(label=f'''Upload the images for your {ordinal(x+1) if (x>0) else ""} concept''', file_count="multiple", interactive=True, visible=visible))
                with gr.Column(visible=visible) as row[x]:
                    concept_collection.append(gr.Textbox(label=f'''{ordinal(x+1) if (x>0) else ""} concept word - use a unique, made up word to avoid collisions'''))
                    init_collection.append(gr.Textbox(label=f'''{ordinal(x+1) if (x>0) else ""} initial word - to init the concept embedding'''))
                    with gr.Row():
                        if(x < maximum_concepts-1):
                            buttons_collection.append(gr.Button(value="Add +1 concept", visible=visible))
                        if(x > 0):
                            delete_collection.append(gr.Button(value=f"Delete {ordinal(x+1)} concept"))
            
            counter_add = 1
            for button in buttons_collection:
                if(counter_add < len(buttons_collection)):
                    button.click(lambda:
                    [gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), True, None],
                    None, 
                    [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], buttons_collection[counter_add], is_visible[counter_add], file_collection[counter_add]], queue=False)
                else:
                    button.click(lambda:[gr.update(visible=True),gr.update(visible=True), gr.update(visible=False), True], None, [row[counter_add], file_collection[counter_add], buttons_collection[counter_add-1], is_visible[counter_add]], queue=False)
                counter_add += 1
            
            counter_delete = 1
            for delete_button in delete_collection:
                if(counter_delete < len(delete_collection)+1):
                    delete_button.click(lambda:[gr.update(visible=False),gr.update(visible=False), gr.update(visible=True), False], None, [file_collection[counter_delete], row[counter_delete], buttons_collection[counter_delete-1], is_visible[counter_delete]], queue=False)
                counter_delete += 1
            
    with gr.Accordion("Custom Settings", open=False):
        swap_auto_calculated = gr.Checkbox(label="Use custom settings")
        gr.Markdown("The default steps is 1000. If your results aren't really what you wanted, it may be underfitting and you need more steps.")
        steps = gr.Number(label="How many steps", value=1000)
        # need to remove
        perc_txt_encoder = gr.Number(label="Percentage of the training steps the text-encoder should be trained as well", value=30, visible=False)
        # perc_txt_encoder = 30
        
    with gr.Box(visible=False) as training_summary:
        training_summary_text = gr.HTML("", visible=False, label="Training Summary")
        is_advanced_visible = True if is_spaces else False
        training_summary_checkbox = gr.Checkbox(label="Automatically remove paid GPU attribution and upload model to the Hugging Face Hub after training", value=False, visible=is_advanced_visible)
        training_summary_model_name = gr.Textbox(label="Name of your model", visible=False)
        training_summary_where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to", visible=False)
        training_summary_token_message = gr.Markdown("[A Hugging Face write access token](https://huggingface.co./settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.", visible=False)            
        training_summary_token = gr.Textbox(label="Hugging Face Write Token", type="password", visible=False)
        
    train_btn = gr.Button("Start Training")
    
    training_ongoing = gr.Markdown("## Training is ongoing ⌛... You can close this tab if you like or just wait. If you did not check the `Remove GPU After training`, you can come back here to try your model and upload it after training. Don't forget to remove the GPU attribution after you are done. ", visible=False)
    
    #Post-training UI
    completed_training = gr.Markdown('''# ✅ Training completed. 
    ### Don't forget to remove the GPU attribution after you are done trying and uploading your model''', visible=False)
    
    with gr.Row():
        with gr.Box(visible=True) as try_your_model:
            gr.Markdown("## Try your model")
            prompt = gr.Textbox(label="Type your prompt")
            result_image = gr.Image()
            inference_steps = gr.Slider(minimum=1, maximum=150, value=50, step=1)
            generate_button = gr.Button("Generate Image")
        
        with gr.Box(visible=False) as push_to_hub:
            gr.Markdown("## Push to Hugging Face Hub")
            model_name = gr.Textbox(label="Name of your model", placeholder="Tarsila do Amaral Style")
            where_to_upload = gr.Dropdown(["My personal profile", "Public Library"], label="Upload to")
            gr.Markdown("[A Hugging Face write access token](https://huggingface.co./settings/tokens), go to \"New token\" -> Role : Write. A regular read token won't work here.")
            hf_token = gr.Textbox(label="Hugging Face Write Token", type="password")
            
            push_button = gr.Button("Push to the Hub")
    
    result = gr.File(label="Download the uploaded models in the diffusers format", visible=True)
    success_message_upload = gr.Markdown(visible=False)
    convert_button = gr.Button("Convert to CKPT", visible=False)
    
    #Swap the examples and the % of text encoder trained depending if it is an object, person or style
    type_of_thing.change(fn=swap_text, inputs=[type_of_thing], outputs=[thing_description, thing_image_example, things_naming, perc_txt_encoder, thing_experimental], queue=False, show_progress=False)
    
    #Swap the base model
    base_model_to_use.change(fn=swap_base_model, inputs=base_model_to_use, outputs=[])

    #Update the summary box below the UI according to how many images are uploaded and whether users are using custom settings or not 
    for file in file_collection:
        #file.change(fn=update_steps,inputs=file_collection, outputs=steps)
        file.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
        
    steps.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
    perc_txt_encoder.change(fn=count_files, inputs=file_collection+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[training_summary, training_summary_text], queue=False)
    
    #Give more options if the user wants to finish everything after training
    if(is_spaces):
        training_summary_checkbox.change(fn=checkbox_swap, inputs=training_summary_checkbox, outputs=[training_summary_token_message, training_summary_token, training_summary_model_name, training_summary_where_to_upload],queue=False, show_progress=False)
    #Add a message for while it is in training
    train_btn.click(lambda:gr.update(visible=True), inputs=None, outputs=training_ongoing)

    #The main train function
    train_btn.click(fn=train, inputs=is_visible+concept_collection+init_collection+file_collection+[base_model_to_use]+[thing_experimental]+[training_summary_where_to_upload]+[training_summary_model_name]+[training_summary_checkbox]+[training_summary_token]+[type_of_thing]+[steps]+[perc_txt_encoder]+[swap_auto_calculated], outputs=[result, try_your_model, push_to_hub, convert_button, training_ongoing, completed_training], queue=False)
    
    #Button to generate an image from your trained model after training
    print('=='*20)
    print(prompt)
    print(inference_steps)
    generate_button.click(fn=generate, inputs=[prompt, inference_steps], outputs=result_image, queue=False)

    #Button to push the model to the Hugging Face Hub
    push_button.click(fn=push, inputs=[model_name, where_to_upload, hf_token, base_model_to_use], outputs=[success_message_upload, result], queue=False)
    #Button to convert the model to ckpt format 
    convert_button.click(fn=convert_to_ckpt, inputs=[], outputs=result, queue=False)
    
    #Checks if the training is running
    demo.load(fn=check_status, inputs=top_description, outputs=[top_description, try_your_model, push_to_hub, result, convert_button], queue=False, show_progress=False)

demo.queue(default_enabled=False).launch(debug=True)