app.py

import gradio as gr
import pandas as pd
import plotly.express as px
import numpy as np  # Import numpy

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'<span style="color: black !important; font-size:1.5em !important;">{"★" * score_int}</span>'

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})'

# --- Plot Functions (Bar Chart - Modified with explicit tickvals) ---

def get_plots(task):
    df = pd.read_csv('data/energy/' + task)
    if df.columns[0].startswith("Unnamed:"):
        df = df.iloc[:, 1:]
    # Use the raw numeric value from the CSV for GPU Energy
    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
    df['energy_score'] = df['energy_score'].astype(int).astype(str)
    # Create a display model column for labeling
    df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])

    # Use the energy score to control color
    color_map = {"1": "red", "2": "orange", "3": "yellow", "4": "lightgreen", "5": "green"}

    # Now plot as a bar chart
    fig = px.bar(
        df,
        x="Display Model",
        y="total_gpu_energy",
        color="energy_score",
        custom_data=['energy_score'],
        height=500,
        width=800,
        color_discrete_map=color_map
    )
    # Update hover text to show the model and GPU Energy (with 4 decimals)
    fig.update_traces(
        hovertemplate="<br>".join([
            "Model: %{x}",
            "GPU Energy (Wh): %{y:.4f}",
            "Energy Score: %{customdata[0]}"
        ])
    )
    fig.update_layout(
        xaxis_title="Model",
        yaxis_title="GPU Energy (Wh)",
        yaxis = dict(
            tickformat=".4f",
            tickvals = list(np.arange(0, df['total_gpu_energy'].max() + 1, 0.5)) # Ticks every 0.5 units
        )
    )
    return fig

def get_all_plots():
    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['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
        df['energy_score'] = df['energy_score'].astype(int).astype(str)
        df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])
        all_df = pd.concat([all_df, df], ignore_index=True)
    all_df = all_df.drop_duplicates(subset=['model'])

    color_map = {"1": "red", "2": "orange", "3": "yellow", "4": "lightgreen", "5": "green"}

    fig = px.bar(
        all_df,
        x="Display Model",
        y="total_gpu_energy",
        color="energy_score",
        custom_data=['energy_score'],
        height=500,
        width=800,
        color_discrete_map=color_map
    )
    fig.update_traces(
        hovertemplate="<br>".join([
            "Model: %{x}",
            "GPU Energy (Wh): %{y:.4f}",
            "Energy Score: %{customdata[0]}"
        ])
    )
    fig.update_layout(
        xaxis_title="Model",
        yaxis_title="GPU Energy (Wh)",
        yaxis = dict(
            tickformat=".4f",
            tickvals = list(np.arange(0, all_df['total_gpu_energy'].max() + 1, 0.5)) # Ticks every 0.5 units
        )
    )
    return fig

# --- New functions for Text Generation filtering by model class (with Bar Chart - Modified explicit tickvals) ---

def get_text_generation_plots(model_class):
    df = pd.read_csv('data/energy/text_generation.csv')
    if df.columns[0].startswith("Unnamed:"):
        df = df.iloc[:, 1:]
    # Filter by the selected model class if the "class" column exists
    if 'class' in df.columns:
        df = df[df['class'] == model_class]
    df['total_gpu_energy'] = pd.to_numeric(df['total_gpu_energy'], errors='raise')
    df['energy_score'] = df['energy_score'].astype(int).astype(str)
    df['Display Model'] = df['model'].apply(lambda m: m.split('/')[-1])


    color_map = {"1": "red", "2": "orange", "3": "yellow", "4": "lightgreen", "5": "green"}

    fig = px.bar(
        df,
        x="Display Model",
        y="total_gpu_energy",
        color="energy_score",
        custom_data=['energy_score'],
        height=500,
        width=800,
        color_discrete_map=color_map
    )
    fig.update_traces(
        hovertemplate="<br>".join([
            "Model: %{x}",
            "GPU Energy (Wh): %{y:.4f}",
            "Energy Score: %{customdata[0]}"
        ])
    )
    fig.update_layout(
        xaxis_title="Model",
        yaxis_title="GPU Energy (Wh)",
        yaxis = dict(
            tickformat=".4f",
            tickvals = list(np.arange(0, df['total_gpu_energy'].max() + 1, 0.5)) # Ticks every 0.5 units
        )
    )
    return fig


# --- Leaderboard Table Functions and Gradio Interface are unchanged ---
# (Keep the rest of the code same as previous response)

def get_model_names(task):
    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)
    # For leaderboard display, format GPU Energy to 4 decimals
    df['GPU Energy (Wh)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
    df['Model'] = df['model'].apply(make_link)
    df['Score'] = df['energy_score'].apply(format_stars)
    # Remove any Class column if it exists
    df = df[['Model', 'GPU Energy (Wh)', 'Score']]
    df = df.sort_values(by='GPU Energy (Wh)')
    return df

def get_all_model_names():
    all_df = pd.DataFrame()
    for task in tasks:
        df = pd.read_csv('data/energy/' + task)
        df['energy_score'] = df['energy_score'].astype(int)
        df['GPU Energy (Wh)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
        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 (Wh)')
    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['energy_score'] = df['energy_score'].astype(int)
    df['GPU Energy (Wh)'] = pd.to_numeric(df['total_gpu_energy'], errors='raise').apply(lambda x: f"{x:.4f}")
    df['Model'] = df['model'].apply(make_link)
    df['Score'] = df['energy_score'].apply(format_stars)
    # Remove the Class column if it exists
    df = df[['Model', 'GPU Energy (Wh)', 'Score']]
    df = df.sort_values(by='GPU Energy (Wh)')
    return df

def update_text_generation(model_class):
    plot = get_text_generation_plots(model_class)
    table = get_text_generation_model_names(model_class)
    return plot, table

# --- 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 💬"):
            # Dropdown moved above the plot and leaderboard
            model_class_dropdown = gr.Dropdown(choices=["A", "B", "C"],
                                                 label="Select Model Class",
                                                 value="A")
            with gr.Row():
                with gr.Column(scale=1.3):
                    tg_plot = gr.Plot(get_text_generation_plots("A"))
                with gr.Column(scale=1):
                    tg_table = gr.Dataframe(get_text_generation_model_names("A"), datatype="markdown")
            # Update plot and table when the dropdown value changes
            model_class_dropdown.change(fn=update_text_generation,
                                        inputs=model_class_dropdown,
                                        outputs=[tg_plot, tg_table])

        with gr.TabItem("Image Generation 📷"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('image_generation.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('image_generation.csv'), datatype="markdown")

        with gr.TabItem("Text Classification 🎭"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('text_classification.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('text_classification.csv'), datatype="markdown")

        with gr.TabItem("Image Classification 🖼️"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('image_classification.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('image_classification.csv'), datatype="markdown")

        with gr.TabItem("Image Captioning 📝"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('image_captioning.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('image_captioning.csv'), datatype="markdown")

        with gr.TabItem("Summarization 📃"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('summarization.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('summarization.csv'), datatype="markdown")

        with gr.TabItem("Automatic Speech Recognition 💬"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('asr.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('asr.csv'), datatype="markdown")

        with gr.TabItem("Object Detection 🚘"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('object_detection.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('object_detection.csv'), datatype="markdown")

        with gr.TabItem("Sentence Similarity 📚"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('sentence_similarity.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('sentence_similarity.csv'), datatype="markdown")

        with gr.TabItem("Extractive QA ❔"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('question_answering.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('question_answering.csv'), datatype="markdown")

        with gr.TabItem("All Tasks 💡"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_all_plots())
                with gr.Column():
                    table = gr.Dataframe(get_all_model_names(), datatype="markdown")

    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()