TCO_calculator / app.py
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import gradio as gr
import models
import pandas as pd
import theme
import matplotlib.pyplot as plt
text = "<h1 style='text-align: center; color: #333333; font-size: 40px;'>TCO Comparison Calculator"
text2 = "Please note that the cost/request only defines the infrastructure cost for deployment. The labor cost must be added for the whole AI model service deployment TCO."
description=f"""
<p>In this demo application, we help you compare different AI model services, such as Open source or SaaS solutions, based on the Total Cost of Ownership for their deployment.</p>
<p>Please note that we focus on getting the service up and running, but not the maintenance that follows.</p>
"""
formula = r"""
$CR = \frac{CIT\_1K \times IT + COT\_1K \times OT}{1000}$ <br>
with: <br>
CR = Cost per Request <br>
CIT_1K = Cost per 1000 Input Tokens <br>
COT_1K = Cost per 1000 Output Tokens <br>
IT = Input Tokens <br>
OT = Output Tokens
"""
def on_use_case_change(use_case):
if use_case == "Summarize":
return gr.update(value=500), gr.update(value=200)
elif use_case == "Question-Answering":
return gr.update(value=300), gr.update(value=300)
else:
return gr.update(value=50), gr.update(value=10)
def compare_info(tco1, tco2, dropdown, dropdown2):
#Compute the cost/request ratio
r = tco1 / tco2
if r < 1:
comparison_result = f"""The cost/request of the second {dropdown2} service is <b>{1/r:.5f} times more expensive</b> than the one of the first {dropdown} service."""
elif r > 1:
comparison_result = f"""The cost/request of the second {dropdown2} service is <b>{r:.5f} times cheaper</b> than the one of the first {dropdown} service."""
else:
comparison_result = f"""Both solutions have the <b>same cost/request</b>."""
# Create a bar chart
services = [dropdown, dropdown2]
costs_to_compare = [tco1, tco2]
plt.figure(figsize=(6, 4))
plt.bar(services, costs_to_compare, color=['red', 'green'])
plt.xlabel('AI option services', fontsize=10)
plt.ylabel('($) Cost/Request', fontsize=10)
plt.title('Comparison of Cost/Request', fontsize=14)
plt.tight_layout()
plt.savefig('cost_comparison.png') # Save to a file
return gr.update(value='cost_comparison.png'), comparison_result
def create_table(tco1, tco2, labor_cost1, labor_cost2, dropdown, dropdown2, latency, latency2):
list_values = []
first_sol = [tco1, labor_cost1, latency]
second_sol = [tco2, labor_cost2, latency2]
list_values.append(first_sol)
list_values.append(second_sol)
data = pd.DataFrame(list_values, index=[dropdown, dropdown2], columns=["Cost/request ($) ", "Labor Cost ($/month)", "Average latency (s)"])
formatted_data = data.copy()
formatted_data["Cost/request ($) "] = formatted_data["Cost/request ($) "].apply('{:.5f}'.format)
formatted_data["Labor Cost ($/month)"] = formatted_data["Labor Cost ($/month)"].apply('{:.0f}'.format)
styled_data = formatted_data.style\
.set_properties(**{'background-color': '#ffffff', 'color': '#000000', 'border-color': '#e0e0e0', 'border-width': '1px', 'border-style': 'solid'})\
.to_html()
centered_styled_data = f"<center>{styled_data}</center>"
return gr.update(value=centered_styled_data)
def update_plot(tco1, tco2, dropdown, dropdown2, labour_cost1, labour_cost2):
request_ranges = list(range(0, 1001, 100)) + list(range(1000, 10001, 500)) + list(range(10000, 100001, 1000)) + list(range(100000, 2000001, 100000))
costs_tco1 = [(tco1 * req + labour_cost1) for req in request_ranges]
costs_tco2 = [(tco2 * req + labour_cost2) for req in request_ranges]
data = pd.DataFrame({
"Number of requests": request_ranges * 2,
"Cost ($)": costs_tco1 + costs_tco2,
"AI model service": ["1)" + " " + dropdown] * len(request_ranges) + ["2)" + " " + dropdown2] * len(request_ranges)
}
)
return gr.LinePlot.update(data, visible=True, x="Number of requests", y="Cost ($)",color="AI model service",color_legend_position="bottom", title="Set-up TCO for one month", height=300, width=500, tooltip=["Number of requests", "Cost ($)", "AI model service"])
style = theme.Style()
with gr.Blocks(theme=style) as demo:
Models: list[models.BaseTCOModel] = [models.OpenAIModel, models.CohereModel, models.OpenSourceLlama2Model]
model_names = [Model().get_name() for Model in Models]
gr.Markdown(value=text)
gr.Markdown(value=description)
with gr.Row():
with gr.Column():
with gr.Row():
use_case = gr.Dropdown(["Summarize", "Question-Answering", "Classification"], value="Question-Answering", label=" Describe your use case ")
with gr.Accordion("Click here if you want to customize the number of input and output tokens per request", open=False):
with gr.Row():
input_tokens = gr.Slider(minimum=1, maximum=1000, value=300, step=1, label=" Input tokens per request", info="We suggest a value that we believe best suit your use case choice but feel free to adjust", interactive=True)
output_tokens = gr.Slider(minimum=1, maximum=1000, value=300, step=1, label=" Output tokens per request", info="We suggest a value that we believe best suit your use case choice but feel free to adjust", interactive=True)
with gr.Row(visible=False):
num_users = gr.Number(value="1000", interactive = True, label=" Number of users for your service ")
use_case.change(on_use_case_change, inputs=use_case, outputs=[input_tokens, output_tokens])
with gr.Row():
with gr.Column():
page1 = models.ModelPage(Models)
dropdown = gr.Dropdown(model_names, interactive=True, label=" First AI service option ")
with gr.Accordion("Click here for more information on the computation parameters for your first AI service option", open=False):
page1.render()
with gr.Column():
page2 = models.ModelPage(Models)
dropdown2 = gr.Dropdown(model_names, interactive=True, label=" Second AI service option ")
with gr.Accordion("Click here for more information on the computation parameters for your second AI service option", open=False):
page2.render()
dropdown.change(page1.make_model_visible, inputs=[dropdown, use_case], outputs=page1.get_all_components())
dropdown2.change(page2.make_model_visible, inputs=[dropdown2, use_case], outputs=page2.get_all_components())
compute_tco_btn = gr.Button("Compute & Compare", size="lg", variant="primary", scale=1)
tco1 = gr.State()
tco2 = gr.State()
labor_cost1 = gr.State()
labor_cost2 = gr.State()
latency = gr.State()
latency2 = gr.State()
with gr.Row():
with gr.Accordion("Click here to see the cost/request computation formula", open=False):
tco_formula = gr.Markdown(formula)
with gr.Row(variant='panel'):
with gr.Column():
with gr.Row():
table = gr.Markdown()
with gr.Row():
info = gr.Markdown(text2)
with gr.Row():
with gr.Column(scale=1):
image = gr.Image()
ratio = gr.Markdown()
with gr.Column(scale=2):
plot = gr.LinePlot(visible=False)
compute_tco_btn.click(page1.compute_cost_per_token, inputs=page1.get_all_components_for_cost_computing() + [dropdown, input_tokens, output_tokens], outputs=[tco1, latency, labor_cost1]).then(page2.compute_cost_per_token, inputs=page2.get_all_components_for_cost_computing() + [dropdown2, input_tokens, output_tokens], outputs=[tco2, latency2, labor_cost2]).then(create_table, inputs=[tco1, tco2, labor_cost1, labor_cost2, dropdown, dropdown2, latency, latency2], outputs=table).then(compare_info, inputs=[tco1, tco2, dropdown, dropdown2], outputs=[image, ratio]).then(update_plot, inputs=[tco1, tco2, dropdown, dropdown2, labor_cost1, labor_cost2], outputs=plot)
demo.launch(debug=True)