import os, glob
import pandas as pd
import gradio as gr
from datasets import load_dataset
from huggingface_hub import HfApi
OWNER = "AIEnergyScore"
TOKEN = os.environ.get("DEBUG")
API = HfApi(token=TOKEN)
def get_leaderboard_models():
"""
Reads CSV files from the leaderboard directory and returns a DataFrame
containing the 'model' and 'task' columns.
If no CSV files are found, returns an empty DataFrame with those columns.
"""
path = r'leaderboard_v0_data/energy'
filenames = glob.glob(os.path.join(path, "*.csv"))
data = []
for filename in filenames:
data.append(pd.read_csv(filename))
if not data:
return pd.DataFrame(columns=['model', 'task'])
leaderboard_data = pd.concat(data, ignore_index=True)
return leaderboard_data[['model', 'task']]
def print_existing_models():
"""
Loads a dataset of requests and returns the models that have been benchmarked.
"""
requests = load_dataset("AIEnergyScore/requests_debug", split="test", token=TOKEN)
requests_dset = requests.to_pandas()
model_df = requests_dset[['model', 'status']]
model_df = model_df[model_df['status'] == 'COMPLETED']
return model_df
def highlight_cols(x):
df = x.copy()
df[df['status'] == 'COMPLETED'] = 'color: green'
df[df['status'] == 'PENDING'] = 'color: orange'
df[df['status'] == 'FAILED'] = 'color: red'
return df
# Apply styling to the recently benchmarked models table.
existing_models = print_existing_models()
formatted_df = existing_models.style.apply(highlight_cols, axis=None)
def get_zip_data_link():
"""
Returns an HTML link for downloading logs.
"""
return (
'Download Logs'
)
with gr.Blocks() as demo:
# --- Custom CSS for layout and styling ---
gr.HTML('''
''')
# --- Header Links ---
with gr.Row(elem_classes="header-links"):
leaderboard_link = gr.HTML(
'Leaderboard'
)
submission_link = gr.HTML(
'Submission Portal'
)
label_link = gr.HTML(
'Label Generator'
)
faq_link = gr.HTML(
'FAQ'
)
documentation_link = gr.HTML(
'Documentation'
)
download_link = gr.HTML(get_zip_data_link())
community_link = gr.HTML(
'Community'
)
# --- Logo (Centered) ---
gr.HTML('''
''')
# --- Centered Subtitle ---
gr.Markdown('Welcome to the AI Energy Score Leaderboard. Explore the top-performing models below.
')
# --- Leaderboard Tables ---
with gr.Column(elem_classes="full-width"):
with gr.Accordion("Latest Leaderboard", open=True):
gr.Dataframe(get_leaderboard_models(), elem_classes="full-width")
with gr.Accordion("Recently Benchmarked Models", open=False):
gr.Dataframe(formatted_df, elem_classes="full-width")
demo.launch()
