Update app.py

app.py

@@ -10,7 +10,16 @@ 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)
@@ -20,6 +29,15 @@ def on_use_case_change(use_case):
         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]
@@ -29,15 +47,11 @@ def compare_info(tco1, tco2, dropdown, dropdown2):
     plt.xlabel('AI option services', fontsize=10)
     plt.ylabel('($) Cost/Request', fontsize=10)
     plt.title('Comparison of Cost/Request', fontsize=14)
-    
-    # Customize x-axis labels
-    #plt.xticks(rotation=30, ha='right')  # Rotate by 30 degrees and align to the right
 
-    # Save the plot to a file or display it
     plt.tight_layout()
     plt.savefig('cost_comparison.png')  # Save to a file
 
-    return gr.update(value='cost_comparison.png')
+    return gr.update(value='cost_comparison.png'), comparison_result
 
 def create_table(tco1, tco2, labor_cost1, labor_cost2, dropdown, dropdown2, latency, latency2):
     list_values = []
@@ -120,23 +134,21 @@ with gr.Blocks(theme=style) as demo:
     
     with gr.Row():
         with gr.Accordion("Click here to see the cost/request computation formula", open=False):
-            with gr.Row():
-                with gr.Column():
-                    tco_formula = gr.Markdown()
-                with gr.Column():
-                    tco_formula2 = gr.Markdown()
+            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()
-                    info = gr.Markdown(text2)
+                    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, tco_formula, latency, labor_cost1]).then(page2.compute_cost_per_token, inputs=page2.get_all_components_for_cost_computing() + [dropdown2, input_tokens, output_tokens], outputs=[tco2, tco_formula2, 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).then(update_plot, inputs=[tco1, tco2, dropdown, dropdown2, labor_cost1, labor_cost2], outputs=plot)
+    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)