Update app.py

app.py

@@ -7,8 +7,9 @@ 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>
+<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>
+<p>If you want to <strong>contribute to the calculator</strong> by adding your own AI service option, follow this <a href="https://huggingface.co/spaces/mithril-security/TCO_calculator/blob/main/How_to_contribute.md">tutorial</a> 👈. </p>
 """
 formula = r"""
 $CR = \frac{CIT_{1K} \times IT + COT_{1K} \times OT}{1000}$  <br>
@@ -19,7 +20,7 @@ $COT_{1K}$ = 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)
@@ -73,6 +74,27 @@ def create_table(tco1, tco2, labor_cost1, labor_cost2, dropdown, dropdown2, late
     
     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))
@@ -90,7 +112,7 @@ def update_plot(tco1, tco2, dropdown, dropdown2, labour_cost1, labour_cost2):
 style = theme.Style()
 
 with gr.Blocks(theme=style) as demo:
-    Models: list[models.BaseTCOModel] = [models.OpenAIModelGPT4, models.OpenAIModelGPT3_5, models.CohereModel, models.OpenSourceLlama2Model]
+    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)
@@ -149,6 +171,6 @@ with gr.Blocks(theme=style) as demo:
                 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)
+    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)