import gradio as gr
import pandas as pd
CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""@misc{aienergyscore-leaderboard,
author = {Sasha Luccioni and Boris Gamazaychikov and Emma Strubell and Sara Hooker and Yacine Jernite and Carole-Jean Wu and Margaret Mitchell},
title = {AI Energy Score Leaderboard - February 2025},
year = {2025},
publisher = {Hugging Face},
howpublished = "\url{https://huggingface.co/spaces/AIEnergyScore/Leaderboard}",
}"""
# List of tasks (CSV filenames)
tasks = [
'asr.csv',
'object_detection.csv',
'text_classification.csv',
'image_captioning.csv',
'question_answering.csv',
'text_generation.csv',
'image_classification.csv',
'sentence_similarity.csv',
'image_generation.csv',
'summarization.csv'
]
def format_stars(score):
try:
score_int = int(score)
except Exception:
score_int = 0
# Render stars in black with a slightly larger font
return f'{"★" * score_int}'
def make_link(mname):
parts = str(mname).split('/')
display_name = parts[1] if len(parts) > 1 else mname
return f'{display_name}'
def generate_html_table_from_df(df):
"""
Given a dataframe that already includes:
- 'gpu_energy_numeric': numeric energy (in Wh)
- 'Model': the model link HTML,
- 'Score': the HTML stars,
- and 'energy_score' as an integer,
generate an HTML table that shows the energy value plus a horizontal bar whose width is computed
relative to the maximum energy.
"""
max_energy = df['gpu_energy_numeric'].max() if not df.empty else 1
color_map = {"1": "red", "2": "orange", "3": "yellow", "4": "lightgreen", "5": "green"}
html = ''
html += ''
html += '| Model | '
html += 'GPU Energy (Wh) | '
html += 'Score | '
html += '
'
html += ''
for _, row in df.iterrows():
energy_numeric = row['gpu_energy_numeric']
energy_str = f"{energy_numeric:.4f}"
# Compute the relative width (as a percentage) for the horizontal bar
bar_width = (energy_numeric / max_energy) * 100
score_val = row['energy_score']
bar_color = color_map.get(str(score_val), "gray")
html += ''
html += f'| {row["Model"]} | '
html += (
f'{energy_str}
'
f' | '
)
html += f'{row["Score"]} | '
html += '
'
html += '
'
return html
def get_model_names_html(task):
df = pd.read_csv('data/energy/' + task)
if df.columns[0].startswith("Unnamed:"):
df = df.iloc[:, 1:]
# Convert energy_score to integer and total_gpu_energy from kWh to Wh
df['energy_score'] = df['energy_score'].astype(int)
df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
df['Model'] = df['model'].apply(make_link)
df['Score'] = df['energy_score'].apply(format_stars)
df = df.sort_values(by='gpu_energy_numeric')
return generate_html_table_from_df(df)
def get_all_model_names_html():
all_df = pd.DataFrame()
for task in tasks:
df = pd.read_csv('data/energy/' + task)
if df.columns[0].startswith("Unnamed:"):
df = df.iloc[:, 1:]
df['energy_score'] = df['energy_score'].astype(int)
df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
df['Model'] = df['model'].apply(make_link)
df['Score'] = df['energy_score'].apply(format_stars)
all_df = pd.concat([all_df, df], ignore_index=True)
all_df = all_df.drop_duplicates(subset=['model'])
all_df = all_df.sort_values(by='gpu_energy_numeric')
return generate_html_table_from_df(all_df)
def get_text_generation_model_names_html(model_class):
df = pd.read_csv('data/energy/text_generation.csv')
if df.columns[0].startswith("Unnamed:"):
df = df.iloc[:, 1:]
if 'class' in df.columns:
df = df[df['class'] == model_class]
df['energy_score'] = df['energy_score'].astype(int)
df['gpu_energy_numeric'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000
df['Model'] = df['model'].apply(make_link)
df['Score'] = df['energy_score'].apply(format_stars)
df = df.sort_values(by='gpu_energy_numeric')
return generate_html_table_from_df(df)
def update_text_generation(model_class):
table_html = get_text_generation_model_names_html(model_class)
return table_html
# --- Build the Gradio Interface ---
demo = gr.Blocks(css="""
.gr-dataframe table {
table-layout: fixed;
width: 100%;
}
.gr-dataframe th, .gr-dataframe td {
max-width: 150px;
white-space: nowrap;
overflow: hidden;
text-overflow: ellipsis;
}
""")
with demo:
gr.Markdown(
"""# AI Energy Score Leaderboard
### Welcome to the leaderboard for the [AI Energy Score Project!](https://huggingface.co/AIEnergyScore)
Select different tasks to see scored models. Submit open models for testing and learn about testing proprietary models via the [submission portal](https://huggingface.co/spaces/AIEnergyScore/submission_portal)"""
)
with gr.Tabs():
# --- Text Generation Tab with Dropdown for Model Class ---
with gr.TabItem("Text Generation 💬"):
model_class_dropdown = gr.Dropdown(choices=["A", "B", "C"],
label="Select Model Class",
value="A")
tg_table = gr.HTML(get_text_generation_model_names_html("A"))
model_class_dropdown.change(fn=update_text_generation,
inputs=model_class_dropdown,
outputs=tg_table)
with gr.TabItem("Image Generation 📷"):
gr.HTML(get_model_names_html('image_generation.csv'))
with gr.TabItem("Text Classification 🎭"):
gr.HTML(get_model_names_html('text_classification.csv'))
with gr.TabItem("Image Classification 🖼️"):
gr.HTML(get_model_names_html('image_classification.csv'))
with gr.TabItem("Image Captioning 📝"):
gr.HTML(get_model_names_html('image_captioning.csv'))
with gr.TabItem("Summarization 📃"):
gr.HTML(get_model_names_html('summarization.csv'))
with gr.TabItem("Automatic Speech Recognition 💬"):
gr.HTML(get_model_names_html('asr.csv'))
with gr.TabItem("Object Detection 🚘"):
gr.HTML(get_model_names_html('object_detection.csv'))
with gr.TabItem("Sentence Similarity 📚"):
gr.HTML(get_model_names_html('sentence_similarity.csv'))
with gr.TabItem("Extractive QA ❔"):
gr.HTML(get_model_names_html('question_answering.csv'))
with gr.TabItem("All Tasks 💡"):
gr.HTML(get_all_model_names_html())
with gr.Accordion("📙 Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
elem_id="citation-button",
lines=10,
show_copy_button=True,
)
gr.Markdown(
"""Last updated: February 2025"""
)
demo.launch()