# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.

# %% auto 0
__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']

# %% app.ipynb 0
import gradio as gr
import pandas as pd
from huggingface_hub import list_models

from diffusers import StableDiffusionPipeline

submissions_list = list_models(filter=["dreambooth-hackathon", category], full=True)
spaces_pipeline_load = [submission.id for submission in submissions_list ]
for ids in spaces_pipeline_load:
    mydict[ids] = StableDiffusionPipeline.from_pretrained(ids) #('ashiqabdulkhader/shiba-dog')
    #f"pipeline{ids.split('//')[-1]}" = StableDiffusionPipeline.from_pretrained(ids) #('ashiqabdulkhader/shiba-dog')

#pipeline = StableDiffusionPipeline.from_pretrained("ashiqabdulkhader/shiba-dog") #('pharma/sugar-glider')
#image = pipeline().images[0]
#image
#https://huggingface.co/ashiqabdulkhader/shiba-dog
    def filter_species(species):
        return gr.Dropdown.update(choices=species_map[species], value=species_map[species][1]), gr.update(visible=True)

    # %% app.ipynb 1
def make_clickable_demo(model_name, prompt): #link=None):
    #if link is None:
    #    link = "https://huggingface.co/" + model_name
    # Remove user from model name
    prompt = "a photo of " + ' '.join(model_name.split('/')[-1].split['-']) + str(prompt)
        
    return gr.Button.update()
    
def make_clickable_model(model_name, link=None):
    if link is None:
        link = "https://huggingface.co/" + model_name

    #adding functionality for demo
    prompt = "a photo of " + ' '.join(model_name.split('/')[-1].split['-']) + str(prompt)
    pipeline = StableDiffusionPipeline.from_pretrained(model_name) #("ashiqabdulkhader/shiba-dog") #('pharma/sugar-glider')
    image_demo = pipeline().images[0]
    
    # Remove user from model name
    return image_out.Update(value=image_demo, label=model_name.split("/")[-1])  #f'<a target="_blank" href="{link}">{model_name.split("/")[-1]}</a>'

def make_clickable_user(user_id):
    link = "https://huggingface.co/" + user_id
    return f'<a  target="_blank" href="{link}">{user_id}</a>'

# %% app.ipynb 2
def get_submissions(category):
    submissions = list_models(filter=["dreambooth-hackathon", category], full=True)
    leaderboard_models = []

    for submission in submissions:
        # user, model, likes
        user_id = submission.id.split("/")[0]
        leaderboard_models.append(
            (
                make_clickable_user(user_id),
                make_clickable_model(submission.id),
                submission.likes,
            )
        )

    df = pd.DataFrame(data=leaderboard_models, columns=["User", "Model", "Likes"])
    df.sort_values(by=["Likes"], ascending=False, inplace=True)
    df.insert(0, "Rank", list(range(1, len(df) + 1)))
    return df

# %% app.ipynb 3
block = gr.Blocks()

with block:
    gr.Markdown(
        """# The DreamBooth Hackathon Leaderboard
    
    Welcome to the leaderboard for the DreamBooth Hackathon! This is a community event where particpants **personalise a Stable Diffusion model** by fine-tuning it with a powerful technique called [_DreamBooth_](https://arxiv.org/abs/2208.12242). This technique allows one to implant a subject (e.g. your pet or favourite dish) into the output domain of the model such that it can be synthesized with a _unique identifier_ in the prompt. 
    
    This competition is composed of 5 _themes_, where each theme will collect models belong to one of the categories shown in the tabs below. We'll be **giving out prizes to the top 3 most liked models per theme**, and you're encouraged to submit as many models as you want!
    
    For details on how to participate, check out the hackathon's guide [here](https://github.com/huggingface/diffusion-models-class/blob/main/hackathon/README.md).
    """
    )
    with gr.Row():
        prompt_in = gr.Textbox(label="Type in a Prompt. This will be suffixed to 'a photo of <model name>', so prompt accordingly -")
        #button_in = gr.Button(label = "Generate Image using this model")
    with gr.Tabs():
        with gr.TabItem("Animal 🐨"):
            with gr.Row():
                animal_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions, inputs=gr.Variable("animal"), outputs=animal_data
                )

                
        with gr.TabItem("Science 🔬"):
            with gr.Row():
                science_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions, inputs=gr.Variable("science"), outputs=science_data
                )
        with gr.TabItem("Food 🍔"):
            with gr.Row():
                food_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions, inputs=gr.Variable("food"), outputs=food_data
                )
        with gr.TabItem("Landscape 🏔"):
            with gr.Row():
                landscape_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions,
                    inputs=gr.Variable("landscape"),
                    outputs=landscape_data,
                )
        with gr.TabItem("Wilcard 🔥"):
            with gr.Row():
                wildcard_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions,
                    inputs=gr.Variable("wildcard"),
                    outputs=wildcard_data,
                )
    
    with gr.Row() as your_model_demo :
        image_out = gr.Image()
        
    button_in.click(make_clickable_demo, prompt_in, your_model_demo)

    block.load(get_submissions, inputs=gr.Variable("animal"), outputs=animal_data)
    block.load(get_submissions, inputs=gr.Variable("science"), outputs=science_data)
    block.load(get_submissions, inputs=gr.Variable("food"), outputs=food_data)
    block.load(get_submissions, inputs=gr.Variable("landscape"), outputs=landscape_data)
    block.load(get_submissions, inputs=gr.Variable("wildcard"), outputs=wildcard_data)


block.launch()