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import gradio as gr |
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import pandas as pd |
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from huggingface_hub import list_models |
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import plotly.express as px |
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def get_plots(task_df): |
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grouped_df = task_df[['total_gpu_energy', 'model']].groupby('model').mean().sort_values('total_gpu_energy',ascending = False) |
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grouped_df = grouped_df.reset_index() |
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grouped_df['model'] = grouped_df['model'].str.split('/').str[-1] |
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grouped_df['task'] = 'text_classification' |
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grouped_df['total_gpu_energy (Wh)'] = grouped_df['total_gpu_energy']*1000 |
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grouped_df['energy_star'] = pd.cut(grouped_df['total_gpu_energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"]) |
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grouped_df = px.scatter(grouped_df, x="model", y="total_gpu_energy (Wh)", height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"}) |
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return grouped_df |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown( |
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"""# Energy Star Leaderboard |
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TODO """ |
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) |
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with gr.Tabs(): |
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with gr.TabItem("Text Generation 💬"): |
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with gr.Row(): |
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animal_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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with gr.TabItem("Image Generation 📷"): |
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with gr.Row(): |
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science_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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with gr.TabItem("Text Classification 🎭"): |
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with gr.Row(): |
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plot = gr.Plot(get_plots('data/text_classification.csv')) |
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with gr.TabItem("Image Classification 🖼️"): |
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with gr.Row(): |
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landscape_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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with gr.TabItem("Extractive QA ❔"): |
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with gr.Row(): |
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wildcard_data = gr.components.Dataframe( |
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type="pandas", datatype=["number", "markdown", "markdown", "number"] |
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) |
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demo.launch() |
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