__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions']

import gradio as gr
import pandas as pd
from huggingface_hub import HfApi, repocard

def is_duplicated(space_id:str)->None:
    card = repocard.RepoCard.load(space_id, repo_type="space")
    return getattr(card.data, "duplicated_from", None) is not None



def make_clickable_model(model_name, link=None):
    if link is None:
        link = "https://huggingface.co/" + "spaces/" + model_name
    return f'<a target="_blank" href="{link}">{model_name.split("/")[-1]}</a>'

def get_space_ids(category):
    api = HfApi()
    spaces = api.list_spaces(filter=["keras-dreambooth", category])
    print(spaces)
    space_ids = [x for x in spaces]
    return space_ids


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

def get_submissions(category):
    submissions = get_space_ids(category)
    leaderboard_models = []

    for submission in submissions:
        # user, model, likes
        if not is_duplicated(submission.id):
            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", "Space", "Likes"])
    df.sort_values(by=["Likes"], ascending=False, inplace=True)
    df.insert(0, "Rank", list(range(1, len(df) + 1)))
    return df

block = gr.Blocks()

with block:
    gr.Markdown(
        """# Keras DreamBooth Leaderboard
    
    Welcome to the leaderboard for the Keras DreamBooth Event! This is a community event where participants **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 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 4 _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!
    
    """
    )
    with gr.Tabs():
        with gr.TabItem("Nature 🐨 🌳 "):
            with gr.Row():
                nature_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("nature"), outputs=nature_data
                )
        with gr.TabItem("Science Fiction & Fantasy 🧙‍♀️ 🧛‍♀️ 🤖  "):
            with gr.Row():
                scifi_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("scifi"), outputs=scifi_data
                )
        with gr.TabItem("Consentful 🖼️ 🎨 "):
            with gr.Row():
                consentful_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("consentful"), outputs=consentful_data
                )
        with gr.TabItem("Wild Card 🃏"):
            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,
                )

    block.load(get_submissions, inputs=gr.Variable("nature"), outputs=nature_data)
    block.load(get_submissions, inputs=gr.Variable("scifi"), outputs=scifi_data)
    block.load(get_submissions, inputs=gr.Variable("consentful"), outputs=consentful_data)
    block.load(get_submissions, inputs=gr.Variable("wildcard"), outputs=wildcard_data)


block.launch()