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' ] color_map = {"1": "red", "2": "orange", "3": "yellow", "4": "lightgreen", "5": "green"} # Keep color map 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}](https://huggingface.co/{mname})' # --- Leaderboard Table Functions (Using gr.HTML Component) --- def create_minimal_bar_html(energy_value_wh, energy_score, max_energy_value): """Generates HTML for the minimal bar chart.""" if max_energy_value <= 0: # Avoid division by zero if max energy is 0 or negative bar_percentage = 0 else: bar_percentage = min(100, (energy_value_wh / max_energy_value) * 100) # Cap at 100% bar_color = color_map.get(str(energy_score), "gray") # Default color if score is unexpected html = f"""
{energy_value_wh:.4f} Wh
""" return html def get_model_names(task): df = pd.read_csv('data/energy/' + task) if df.columns[0].startswith("Unnamed:"): df = df.iloc[:, 1:] df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000 # kWh to Wh conversion df['energy_score'] = df['energy_score'].astype(int) df = df.sort_values(by='total_gpu_energy') # Sort BEFORE creating HTML column max_energy_for_task = df['total_gpu_energy'].max() # Calculate max energy for this task # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_for_task df['GPU Energy (Wh)'] = df.apply(lambda row: gr.HTML(create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_for_task)), axis=1) df['Model'] = df['model'].apply(make_link) df['Score'] = df['energy_score'].apply(format_stars) df = df[['Model', 'GPU Energy (Wh)', 'Score']] # Keep only these columns return df def get_all_model_names(): all_df = pd.DataFrame() max_energy_overall = 0 # Initialize overall max energy for task in tasks: df = pd.read_csv('data/energy/' + task) df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000 # kWh to Wh conversion df['energy_score'] = df['energy_score'].astype(int) 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='total_gpu_energy') # Sort ALL DATA before calculating max and creating HTML max_energy_overall = all_df['total_gpu_energy'].max() # Calculate overall max AFTER sorting # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_overall all_df['GPU Energy (Wh)'] = all_df.apply(lambda row: gr.HTML(create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_overall)), axis=1) all_df['Model'] = all_df['model'].apply(make_link) all_df['Score'] = all_df['energy_score'].apply(format_stars) all_df = all_df[['Model', 'GPU Energy (Wh)', 'Score']] return all_df[['Model', 'GPU Energy (Wh)', 'Score']] def get_text_generation_model_names(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['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise') * 1000 # kWh to Wh conversion df['energy_score'] = df['energy_score'].astype(int) df = df.sort_values(by='total_gpu_energy') # Sort BEFORE creating HTML column max_energy_for_class = df['total_gpu_energy'].max() # Calculate max energy for this class # Create HTML bar chart for GPU Energy column, passing dynamic max_energy_for_class df['GPU Energy (Wh)'] = df.apply(lambda row: gr.HTML(create_minimal_bar_html(row['total_gpu_energy'], row['energy_score'], max_energy_for_class)), axis=1) df['Model'] = df['model'].apply(make_link) df['Score'] = df['energy_score'].apply(format_stars) df = df[['Model', 'GPU Energy (Wh)', 'Score']] # Keep only these columns return df def update_text_generation(model_class): table = get_text_generation_model_names(model_class) return table # --- Build the Gradio Interface (Plots Removed, Tables with Dynamic Bars using gr.HTML) --- 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; } /* CSS for minimal bar chart inside table cell */ .minimal-bar-container { display: flex; align-items: center; gap: 5px; /* space between bar and text */ } .minimal-bar { height: 10px; background-color: blue; /* default, will be overridden by dynamic color */ border-radius: 2px; } """) 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 💬"): # Dropdown moved above the leaderboard model_class_dropdown = gr.Dropdown(choices=["A", "B", "C"], label="Select Model Class", value="A") tg_table = gr.Dataframe(get_text_generation_model_names("A")) # No datatype="markdown" here # Update table when the dropdown value changes model_class_dropdown.change(fn=update_text_generation, inputs=model_class_dropdown, outputs=[tg_table]) with gr.TabItem("Image Generation 📷"): table = gr.Dataframe(get_model_names('image_generation.csv')) # No datatype="markdown" here with gr.TabItem("Text Classification 🎭"): table = gr.Dataframe(get_model_names('text_classification.csv')) # No datatype="markdown" here with gr.TabItem("Image Classification 🖼️"): table = gr.Dataframe(get_model_names('image_classification.csv')) # No datatype="markdown" here with gr.TabItem("Image Captioning 📝"): table = gr.Dataframe(get_model_names('image_captioning.csv')) # No datatype="markdown" here with gr.TabItem("Summarization 📃"): table = gr.Dataframe(get_model_names('summarization.csv')) # No datatype="markdown" here with gr.TabItem("Automatic Speech Recognition 💬"): table = gr.Dataframe(get_model_names('asr.csv')) # No datatype="markdown" here with gr.TabItem("Object Detection 🚘"): table = gr.Dataframe(get_model_names('object_detection.csv')) # No datatype="markdown" here with gr.TabItem("Sentence Similarity 📚"): table = gr.Dataframe(get_model_names('sentence_similarity.csv')) # No datatype="markdown" here with gr.TabItem("Extractive QA ❔"): table = gr.Dataframe(get_model_names('question_answering.csv')) # No datatype="markdown" here with gr.TabItem("All Tasks 💡"): table = gr.Dataframe(get_all_model_names()) # No datatype="markdown" here 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(share=True) # Added share=True here