rlasseri commited on
Commit
fc4f1f5
β€’
1 Parent(s): 28366c6

Corrections

Browse files
Files changed (1) hide show
  1. app.py +7 -9
app.py CHANGED
@@ -3,16 +3,14 @@ import models
3
  import results
4
  import theme
5
 
6
- text = "<h1 style='text-align: center; color: #333333; font-size: 40px;'>AI TCO Comparison Calculator"
7
- text2 = "<h1 style='color: #333333; font-size: 20px;'>πŸ™Œ Want to contribute?"
8
  text3 = "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."
9
  intro = f"""
10
  <p>Discover and compare various AI model services, including SaaS and "Deploy Yourself" solutions, based on the Total Cost of Ownership for their deployment. 😊</p>
11
  <p>Please keep in mind that our focus is on getting the AI model service up and running, not accounting for additional maintenance costs.πŸš€</p>
12
  """
13
- contribution = f"""
14
- <p>If you want to contribute to the calculator by <strong>adding your own AI service option</strong>, follow this <a href="https://huggingface.co/spaces/mithril-security/TCO_calculator/blob/main/How_to_contribute.md">tutorial</a> πŸ‘ˆ. </p>
15
- """
16
  formula = r"""
17
  $CR = \frac{CIT_{1K} \times IT + COT_{1K} \times OT}{1000}$ <br>
18
  with: <br>
@@ -24,8 +22,8 @@ $OT$ = Output Tokens
24
  """
25
 
26
  def on_use_case_change(use_case):
27
- if use_case == "Summarize":
28
- return gr.update(value=500), gr.update(value=200)
29
  elif use_case == "Question-Answering":
30
  return gr.update(value=300), gr.update(value=300)
31
  else:
@@ -43,13 +41,13 @@ with gr.Blocks(theme=style) as demo:
43
  with gr.Row():
44
  with gr.Column():
45
  with gr.Row():
46
- use_case = gr.Dropdown(["Summarize", "Question-Answering", "Classification"], value="Question-Answering", label=" Describe your use case ")
47
  with gr.Accordion("Click here if you want to customize the number of input and output tokens per request", open=False):
48
  with gr.Row():
49
  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)
50
  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)
51
  with gr.Row(visible=False):
52
- num_users = gr.Number(value="1000", interactive = True, label=" Number of users for your service ")
53
 
54
  use_case.change(on_use_case_change, inputs=use_case, outputs=[input_tokens, output_tokens])
55
 
 
3
  import results
4
  import theme
5
 
6
+ text = "<h1 style='text-align: center; color: #333333; font-size: 40px;'>AI TCO Comparison Calculator -- ML/PLD/SL"
7
+ text2 = "<h1 style='color: #333333; font-size: 20px;'>πŸ™Œ "
8
  text3 = "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."
9
  intro = f"""
10
  <p>Discover and compare various AI model services, including SaaS and "Deploy Yourself" solutions, based on the Total Cost of Ownership for their deployment. 😊</p>
11
  <p>Please keep in mind that our focus is on getting the AI model service up and running, not accounting for additional maintenance costs.πŸš€</p>
12
  """
13
+
 
 
14
  formula = r"""
15
  $CR = \frac{CIT_{1K} \times IT + COT_{1K} \times OT}{1000}$ <br>
16
  with: <br>
 
22
  """
23
 
24
  def on_use_case_change(use_case):
25
+ if use_case == "ChatBOT":
26
+ return gr.update(value=300), gr.update(value=700)
27
  elif use_case == "Question-Answering":
28
  return gr.update(value=300), gr.update(value=300)
29
  else:
 
41
  with gr.Row():
42
  with gr.Column():
43
  with gr.Row():
44
+ use_case = gr.Dropdown(["Summarize", "Question-Answering", "Classification","ChatBOT"], value="ChatBOT", label=" Describe your use case ")
45
  with gr.Accordion("Click here if you want to customize the number of input and output tokens per request", open=False):
46
  with gr.Row():
47
  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)
48
  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)
49
  with gr.Row(visible=False):
50
+ num_users = gr.Number(value="100", interactive = True, label=" Number of users for your service ")
51
 
52
  use_case.change(on_use_case_change, inputs=use_case, outputs=[input_tokens, output_tokens])
53