app.py

import gradio as gr
import pandas as pd
from huggingface_hub import list_models
import plotly.express as px

def get_plots(task):
    #TO DO : hover text with energy efficiency number, parameters
    task_df= pd.read_csv('data/energy/'+task)
    params_df = pd.read_csv('data/params/'+task)
    params_df= params_df.rename(columns={"Link": "model"})
    all_df = pd.merge(task_df, params_df, on='model')
    all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
    all_df = all_df.sort_values(by=['Total GPU Energy (Wh)'])
    all_df['parameters'] = all_df['parameters'].apply(format_params)
    all_df['energy_star'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
    fig = px.scatter(all_df, x="model", y='Total GPU Energy (Wh)', custom_data=['parameters'], height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"})
    fig.update_traces(
    hovertemplate="<br>".join([
        "Total Energy: %{y}",
        "Parameters: %{customdata[0]}"])
    )
    return fig

def get_model_names(task_data):
    #TODO: add link to results in model card of each model
    task_df= pd.read_csv('data/energy/'+task_data)
    task_df=task_df.drop_duplicates(subset=['model'])
    model_names = task_df[['model']]
    return model_names

def format_params(num):
    if num > 1000000000:
        if not num % 1000000000:
            return f'{num // 1000000000}B'
        return f'{round(num / 1000000000, 1)}B'
    return f'{num // 1000000}M'


demo = gr.Blocks()

with demo:
    gr.Markdown(
        """# Energy Star Leaderboard

    TODO """
    )
    with gr.Tabs():
        with gr.TabItem("Text Generation 💬"):
            with gr.Row():
                with gr.Column():
                    plot = gr.Plot(get_plots('text_generation.csv'))
                with gr.Column():
                    table = gr.Dataframe(get_model_names('text_generation.csv'))

        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'))

        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'))

        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'))

        with gr.TabItem("Image Captioning 📝"):
            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'))
        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'))

        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'))

        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'))

        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'))

        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'))

demo.launch()