File size: 2,718 Bytes
de1fc5c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8b2b9b5
 
 
de1fc5c
8b2b9b5
de1fc5c
8b2b9b5
 
de1fc5c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
__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():
    api = HfApi()
    spaces = api.list_spaces(filter="jax-diffusers-event")
    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():
    submissions = get_space_ids()
    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(
        """# JAX Diffusers Event Leaderboard
    
    Welcome to the leaderboard for the JAX Diffusers Event 💗🏆 
    
    This is a community event where participants are creating applications based on Stable Diffusion using 🧨 diffusers and JAX with (v4) TPUs generously provided for free by Google Cloud.
    
    To attend the event, simply follow the instructions in [this guide](https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint).
    
    To submit your Space and add it to the leaderboard, simply add `jax-diffusers-event` under tags section in your Space's README.
    
    At the end of the event, the demos with the most likes will be evaluated by the jury for 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()