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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 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() |