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import gradio as gr
import pandas as pd
import plotly.express as px
merged_df = pd.read_csv("data/merged_cloud_data.csv")
tdp_fig = px.scatter(
merged_df,
x="Total TDP (W)",
y="$/Hour",
color="provider",
log_x=True,
log_y=True,
trendline="ols",
trendline_options=dict(log_y=True, log_x=True),
trendline_scope="overall",
)
cost_fig = px.scatter(
merged_df,
x="GPU Total Cost",
y="$/Hour",
color="GPU Type",
log_y=True,
log_x=True,
trendline="ols",
trendline_options=dict(log_x=True, log_y=True),
trendline_scope="overall",
)
def generate_figure(org_name):
org_data = data[data["Organization"] == org_name]
model_counts = (
org_data.groupby("Year")[["Model", "Environmental Transparency"]]
.value_counts()
.reset_index()
)
model_counts.columns = ["Year", "Model", "Environmental Transparency", "Count"]
fig = px.bar(
model_counts,
x="Year",
y="Count",
color="Environmental Transparency",
color_discrete_map=color_discrete_map,
hover_data=["Model"],
)
fig.update_layout(xaxis_type="category")
fig.update_xaxes(categoryorder="category ascending")
return fig
with gr.Blocks() as demo:
gr.Markdown("# Cloud Compute ☁️, Energy ⚡ and Cost 💲 - Explorer Tool")
gr.Markdown(
"## Explore the data from 'When we pay for cloud compute, what are we really paying for?'"
)
with gr.Accordion("Methodology", open=False):
gr.Markdown("TODO")
with gr.Row():
with gr.Column():
gr.Markdown("### TDP Data")
plt1 = gr.Plot(tdp_fig)
with gr.Row():
with gr.Column(scale=1):
org_choice = gr.Dropdown(
organizations,
value="",
label="Organizations",
info="Pick a characteristic to regenerate plot",
interactive=True,
)
with gr.Column(scale=4):
gr.Markdown("### Cost Data")
fig = generate_figure(org_choice)
plt = gr.Plot(cost_fig)
# org_choice.select(generate_figure, inputs=[org_choice], outputs=[plt])
demo.launch()
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