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