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| __all__ = ['block', 'make_clickable_model', 'make_clickable_user', 'get_submissions'] | |
| import gradio as gr | |
| import pandas as pd | |
| from huggingface_hub import list_models | |
| def make_clickable_model(model_name, link=None): | |
| if link is None: | |
| link = "https://huggingface.co/" + model_name | |
| # Remove user from model name | |
| return 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>' | |
| def get_submissions(category): | |
| submissions = list_models(filter=["keras-dreambooth", 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 | |
| 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 (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 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(): | |
| 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("nature"), outputs=nature_data | |
| ) | |
| with gr.TabItem("Science Fiction & Fantasy 🧙♀️ 🧛♀️ 🤖 "): | |
| 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("scifi"), outputs=scifi_data | |
| ) | |
| with gr.TabItem("Consentful 🖼️ 🎨 "): | |
| 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("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() |