import gradio as gr import models import pandas as pd import theme import matplotlib.pyplot as plt text = "

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"""

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. 😊

Please note that we focus on getting the service up and running, but not the maintenance that follows.🚀

If you want to contribute to the calculator by adding your own AI service option, follow this tutorial 👈.

""" formula = r""" $CR = \frac{CIT_{1K} \times IT + COT_{1K} \times OT}{1000}$
with:
$CR$ = Cost per Request
$CIT_{1K}$ = Cost per 1000 Input Tokens
$COT_{1K}$ = Cost per 1000 Output Tokens
$IT$ = Input Tokens
$OT$ = 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 {1/r:.5f} times more expensive than the one of the first {dropdown} service.""" elif r > 1: comparison_result = f"""The cost/request of the second {dropdown2} service is {r:.5f} times cheaper than the one of the first {dropdown} service.""" else: comparison_result = f"""Both solutions have the same cost/request.""" # 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('($) Cost/Request', fontsize=10) plt.title('Comparison of Cost/Request', fontsize=14) plt.tight_layout() plt.savefig('cost_comparison.png') # Save to a file return gr.update(value='cost_comparison.png', visible=True), comparison_result def create_table(tco1, tco2, labor_cost1, labor_cost2, dropdown, dropdown2, latency, latency2): list_values = [] first_sol = [tco1, labor_cost1, latency] second_sol = [tco2, labor_cost2, latency2] list_values.append(first_sol) list_values.append(second_sol) data = pd.DataFrame(list_values, index=[dropdown, dropdown2], columns=["Cost/request ($) ", "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 ($/month)"].apply('{:.0f}'.format) styled_data = formatted_data.style\ .set_properties(**{'background-color': '#ffffff', 'color': '#000000', 'border-color': '#e0e0e0', 'border-width': '1px', 'border-style': 'solid'})\ .to_html() centered_styled_data = f"
{styled_data}
" return gr.update(value=centered_styled_data) def compute_cost_per_request(*args): dropdown_id = args[-2] dropdown_id2 = args[-1] if dropdown_id!=None and dropdown_id2!=None: # Separate the arguments for page1 and page2 args_list = list(args) args_page1 = args_list[:len(page1.get_all_components_for_cost_computing())] + [dropdown_id, input_tokens, output_tokens] args_page2 = args_list[len(page1.get_all_components_for_cost_computing()):] + [dropdown_id2, input_tokens, output_tokens] # Compute and compare using both pages result_page1 = page1.compute_cost_per_token(*args_page1) result_page2 = page2.compute_cost_per_token(*args_page2) # Unpack the results from the functions tco1, latency, labor_cost1 = result_page1 tco2, latency2, labor_cost2 = result_page2 return tco1, latency, labor_cost1, tco2, latency2, labor_cost2 else: raise gr.Error("Please select two AI service options.") def update_plot(tco1, tco2, dropdown, dropdown2, labour_cost1, labour_cost2): request_ranges = list(range(0, 1001, 100)) + list(range(1000, 10001, 500)) + list(range(10000, 100001, 1000)) + list(range(100000, 2000001, 100000)) costs_tco1 = [(tco1 * req + labour_cost1) for req in request_ranges] costs_tco2 = [(tco2 * req + labour_cost2) for req in request_ranges] data = pd.DataFrame({ "Number of requests": request_ranges * 2, "Cost ($)": 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.OpenAIModelGPT4, models.OpenAIModelGPT3_5, models.CohereModel, models.DIYLlama2Model] 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(visible=False) ratio = gr.Markdown() with gr.Column(scale=2): plot = gr.LinePlot(visible=False) compute_tco_btn.click(compute_cost_per_request, inputs=page1.get_all_components_for_cost_computing() + page2.get_all_components_for_cost_computing() + [dropdown, dropdown2], outputs=[tco1, latency, labor_cost1, 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)