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__all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions'] |
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import gradio as gr |
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import pandas as pd |
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from huggingface_hub import HfApi, repocard |
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def is_duplicated(space_id:str)->None: |
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card = repocard.RepoCard.load(space_id, repo_type="space") |
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return getattr(card.data, "duplicated_from", None) is not None |
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def make_clickable_model(model_name, link=None): |
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if link is None: |
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link = "https://huggingface.co/" + "spaces/" + model_name |
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return f'<a target="_blank" href="{link}">{model_name.split("/")[-1]}</a>' |
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def get_space_ids(category): |
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api = HfApi() |
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spaces = api.list_spaces(filter=["keras-dreambooth", category]) |
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print(spaces) |
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space_ids = [x for x in spaces] |
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return space_ids |
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def make_clickable_user(user_id): |
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link = "https://huggingface.co/" + user_id |
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return f'<a target="_blank" href="{link}">{user_id}</a>' |
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def get_submissions(category): |
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submissions = get_space_ids(category) |
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leaderboard_models = [] |
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for submission in submissions: |
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if not is_duplicated(submission.id): |
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user_id = submission.id.split("/")[0] |
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leaderboard_models.append( |
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( |
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make_clickable_user(user_id), |
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make_clickable_model(submission.id), |
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submission.likes, |
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) |
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) |
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df = pd.DataFrame(data=leaderboard_models, columns=["User", "Space", "Likes"]) |
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df.sort_values(by=["Likes"], ascending=False, inplace=True) |
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df.insert(0, "Rank", list(range(1, len(df) + 1))) |
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return df |
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block = gr.Blocks() |
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with block: |
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gr.Markdown( |
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"""# Keras DreamBooth Leaderboard |
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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. |
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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! |
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""" |
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) |
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with gr.Tabs(): |
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with gr.TabItem("Nature 🐨 🌳 "): |
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with gr.Row(): |
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nature_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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data_run.click( |
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get_submissions, inputs=gr.Variable("nature"), outputs=nature_data |
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) |
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with gr.TabItem("Science Fiction & Fantasy 🧙♀️ 🧛♀️ 🤖 "): |
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with gr.Row(): |
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scifi_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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data_run.click( |
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get_submissions, inputs=gr.Variable("scifi"), outputs=scifi_data |
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) |
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with gr.TabItem("Consentful 🖼️ 🎨 "): |
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with gr.Row(): |
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consentful_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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data_run.click( |
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get_submissions, inputs=gr.Variable("consentful"), outputs=consentful_data |
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) |
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with gr.TabItem("Wild Card 🃏"): |
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with gr.Row(): |
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wildcard_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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with gr.Row(): |
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data_run = gr.Button("Refresh") |
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data_run.click( |
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get_submissions, |
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inputs=gr.Variable("wildcard"), |
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outputs=wildcard_data, |
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) |
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block.load(get_submissions, inputs=gr.Variable("nature"), outputs=nature_data) |
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block.load(get_submissions, inputs=gr.Variable("scifi"), outputs=scifi_data) |
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block.load(get_submissions, inputs=gr.Variable("consentful"), outputs=consentful_data) |
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block.load(get_submissions, inputs=gr.Variable("wildcard"), outputs=wildcard_data) |
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block.launch() |
