File size: 3,583 Bytes
e0e93c4 50f19fa ea19e17 52a72f5 3f6473e ea19e17 3f6473e 9de3425 ea19e17 e0e93c4 ea19e17 52a72f5 ea19e17 52a72f5 ea19e17 3f6473e 50f19fa ea19e17 73d3fc4 2d9906b ea19e17 50f19fa 3f6473e 50f19fa b3b6d77 2d9906b ea19e17 50f19fa 3f6473e 50f19fa 73d3fc4 50f19fa ea19e17 3f6473e ea19e17 3f6473e ea19e17 3f6473e ea19e17 50f19fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import gradio as gr
import models
import time
text = "<h1 style='text-align: center; color: midnightblue; font-size: 30px;'>TCO Comparison Calculator"
text1 = "<h1 style='text-align: center; color: midnightblue; font-size: 20px;'>First solution"
text2 = "<h1 style='text-align: center; color: midnightblue; font-size: 20px;'>Second solution"
text3 = "<h1 style='text-align: center; color: midnightblue; font-size: 20px;'>Compute and compare cost/token for both solutions"
description=f"""
<p>In this demo application, we help you compare different solutions for your AI integration plans, such as Open source or SaaS.</p>
<p>First, you'll have to choose the two solutions you'd like to compare. Then, follow the instructions to select your configurations for each solution and we will compute the cost/token accordingly to them. Eventually, both solutions are compared to evaluate which one best suits your needs.</p>
"""
def compute_ratio(tco1, tco2):
time.sleep(2)
try:
r = tco1 / tco2
if r < 1:
comparison_result = f"Second solution is {1/r:.5f} times more expensive than the first"
elif r > 1:
comparison_result = f"Second solution is {r:.5f} times cheaper than the first"
else:
comparison_result = "Both solutions will cost the same."
return comparison_result
except ValueError as e:
return f"Error: {str(e)}"
with gr.Blocks(theme=gr.themes.Soft()) as demo:
Models: list[models.BaseTCOModel] = [models.OpenAIModel, models.CohereModel, models.OpenSourceLlama2Model,models.OpenSourceDIY]
model_names = [Model().get_name() for Model in Models]
gr.Markdown(value=text)
gr.Markdown(value=description)
with gr.Row():
with gr.Column():
gr.Markdown(value=text1)
page1 = models.ModelPage(Models)
dropdown = gr.Dropdown(model_names, interactive=True, label="AI service options")
page1.render()
with gr.Column():
gr.Markdown(value=text2)
page2 = models.ModelPage(Models)
dropdown2 = gr.Dropdown(model_names, interactive=True, label="AI service options")
page2.render()
dropdown.change(page1.make_model_visible, inputs=dropdown, outputs=page1.get_all_components())
dropdown2.change(page2.make_model_visible, inputs=dropdown2, outputs=page2.get_all_components())
gr.Markdown(value=text3)
compute_tco_btn = gr.Button("Compute cost/token", size="lg")
tco1 = gr.State()
tco2 = gr.State()
with gr.Row():
with gr.Column():
tco_output = gr.Text("Output 1: ", label="Cost/token for the first solution")
with gr.Accordion("Open to see the formula", open=False):
tco_formula = gr.Markdown()
with gr.Column():
tco_output2 = gr.Text("Output 2: ", label="Cost/token for the second solution")
with gr.Accordion("Open to see the formula", open=False):
tco_formula2 = gr.Markdown()
ratio = gr.Text("Ratio: ", label="Ratio of cost/token for both solutions")
compute_tco_btn.click(page1.compute_cost_per_token, inputs=page1.get_all_components_for_cost_computing() + [dropdown], outputs=[tco_output, tco1, tco_formula]).then(page2.compute_cost_per_token, inputs=page2.get_all_components_for_cost_computing() + [dropdown2], outputs=[tco_output2, tco2, tco_formula2]).then(compute_ratio, inputs=[tco1, tco2], outputs=ratio)
demo.launch(debug=True) |