app.py

__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 make_clickable_space(name, repo_type):
    if repo_type == "spaces":
        link = "https://huggingface.co/" + "spaces/" + name
    elif repo_type == "models":
        link = "https://huggingface.co/"  + name
    else:
        link = "https://huggingface.co/" + "datasets/" + name
    return f'<a target="_blank" href="{link}">{name.split("/")[-1]}</a>'

def get_repo_ids(repo_type):
    api = HfApi()
    if repo_type == "spaces":
        repos = api.list_spaces(author="hackathon-somos-nlp-2023")
        repos = [s for s in repos if s.id not in ["hackathon-somos-nlp-2023/README", "hackathon-somos-nlp-2023/leaderboard"]]
    elif repo_type == "models":
        repos = api.list_models(author="hackathon-somos-nlp-2023")
    else:
        repos = api.list_datasets(author="hackathon-somos-nlp-2023")
    return repos

def get_submissions(repo_type):
    submissions = get_repo_ids(repo_type)
    leaderboard = []

    for submission in submissions:
        leaderboard.append(
            (
                make_clickable_model(submission.id),
                submission.likes,
            )
        )

    df = pd.DataFrame(data=leaderboard, columns=["Repo", "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(
        """# Hackathon Somos NLP 2023 Leaderboard
    """
    )
    with gr.Tabs():
        with gr.TabItem("Spaces (ML apps)"):
            with gr.Row():
                spaces_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions, inputs=gr.Variable("spaces"), outputs=spaces_data
                )
        with gr.TabItem("Models"):
            with gr.Row():
                models_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions, inputs=gr.Variable("models"), outputs=models_data
                )
        with gr.TabItem("Datasets"):
            with gr.Row():
                datasets_data = gr.components.Dataframe(
                    type="pandas", datatype=["number", "markdown", "number"]
                )
            with gr.Row():
                data_run = gr.Button("Refresh")
                data_run.click(
                    get_submissions, inputs=gr.Variable("datasets"), outputs=datasets_data
                )


    block.load(get_submissions, inputs=gr.Variable("spaces"), outputs=spaces_data)
    block.load(get_submissions, inputs=gr.Variable("models"), outputs=models_data)
    block.load(get_submissions, inputs=gr.Variable("datasets"), outputs=datasets_data)


block.launch()