Spaces:
Running
Running
File size: 2,419 Bytes
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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
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 get_space_ids():
api = HfApi()
spaces = api.list_spaces(filter="making-demos")
print(spaces)
space_ids = [x.id 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():
submissions = get_space_ids()
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(
"""# Making Demos Leaderboard
Welcome to the leaderboard for the ever-running Making Demos Event! 🏆
This is a community event where participants create demos of recently released machine learning models, or combine multiple demos to make cool apps!
To attend the event, simply join us in [Discord](https://huggingface.co/join/discord), take the role #collaborate and write under one of the paper posts under #making-demos forum.
You can add `making-demos` to tags in your Spaces' README.md's metadata section to add your demo to this leaderboard.
At the end of every week, winners of this leaderboard will earn special prizes! 🎁
"""
)
with gr.Row():
data = gr.components.Dataframe(
type="pandas", datatype=["number", "markdown", "markdown", "number"]
)
with gr.Row():
data_run = gr.Button("Refresh")
data_run.click(
get_submissions, outputs=data
)
block.load(get_submissions, outputs=data)
block.launch()
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