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import gradio as gr
import pandas as pd
import os
import plotly.express as px
import numpy as np
datadir = 'data/emissions/complete'
model_param_df = pd.read_csv('data/model_parameters.csv', header=0)
model_performance_df = pd.read_csv('data/performance.csv', header=0)
emissions_df = pd.read_csv('data/co2_data.csv',header=0)
modalities_df = pd.read_csv('data/modalities_data.csv',header=0)
finetuned_df = emissions_df[~emissions_df['task'].str.contains('zero')]
fig0 = px.scatter(finetuned_df, x="dataset", y="query emissions (g)", color="model", log_y=True)
fig0.update_layout(xaxis={'categoryorder':'mean ascending'})
fig0.update_layout(yaxis_title='Total carbon emitted (g)')
fig0.update_layout(xaxis_title='Dataset')
fig1 = px.box(finetuned_df, x="task", y="query_energy (kWh)", color="task", log_y=True)
fig1.update_layout(xaxis={'categoryorder':'mean ascending'})
fig1.update_layout(yaxis_title='Total energy used (Wh)')
fig1.update_layout(xaxis_title='Task')
fig2 = px.scatter(modalities_df, x="num_params", y="query emissions (g)", color="modality",
log_x=True, log_y=True, custom_data=['model','task'])
fig2.update_traces(
hovertemplate="<br>".join([
"Model: %{customdata[0]}",
"Task: %{customdata[1]}",
])
)
fig2.update_layout(xaxis_title='Model size (number of parameters)')
fig2.update_layout(yaxis_title='Model emissions (g of CO<sub>2</sub>)')
demo = gr.Blocks()
with demo:
gr.Markdown("# CO2 Inference Demo")
gr.Markdown("## Explore the plots below to get more insights about the different models and tasks from our study.")
with gr.Row():
with gr.Column():
gr.Plot(fig0)
with gr.Row():
with gr.Column():
gr.Plot(fig1)
with gr.Row():
with gr.Column():
gr.Plot(fig2)
demo.launch()
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