Create results.py

results.py

@@ -0,0 +1,104 @@
+import pandas as pd
+import gradio as gr 
+
+shared_page1 = None
+shared_page2 = None
+
+def set_shared_pages(page1, page2):
+    global shared_page1, shared_page2
+    shared_page1 = page1
+    shared_page2 = page2
+
+def compare_info(tco1, tco2, dropdown, dropdown2):
+    if error_occurred == False :
+        #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]
+        
+        plt.figure(figsize=(6, 4))
+        plt.bar(services, costs_to_compare, color=['red', 'green'])
+        plt.xlabel('AI option services', fontsize=10)
+        plt.ylabel('($) Cost/Request', fontsize=10)
+        plt.title('Comparison of Cost/Request', fontsize=14)
+
+        plt.tight_layout()
+        plt.savefig('cost_comparison.png')  # Save to a file
+
+        return gr.update(value='cost_comparison.png', visible=True), comparison_result
+    else:
+        return None, ""
+
+def create_table(tco1, tco2, labor_cost1, labor_cost2, dropdown, dropdown2, latency, latency2):
+    if error_occurred == False:
+        if shared_page1 is None or shared_page2 is None:
+            raise ValueError("Shared instances not set.")
+        list_values = []
+        first_sol = [tco1, labor_cost1, latency]
+        second_sol = [tco2, labor_cost2, latency2]
+        list_values.append(first_sol)
+        list_values.append(second_sol)
+
+        data = pd.DataFrame(list_values, index=[dropdown, dropdown2], columns=["Cost/request ($) ", "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 ($/month)"].apply('{:.0f}'.format)
+
+        styled_data = formatted_data.style\
+            .set_properties(**{'background-color': '#ffffff', 'color': '#000000', 'border-color': '#e0e0e0', 'border-width': '1px', 'border-style': 'solid'})\
+            .to_html()
+        centered_styled_data = f"<center>{styled_data}</center>"
+        
+        return gr.update(value=centered_styled_data)
+    else:
+        return ""
+
+def compute_cost_per_request(*args):
+    dropdown_id = args[-4]
+    dropdown_id2 = args[-3]
+    input_tokens = args[-2]
+    output_tokens = args[-1]
+    global error_occurred 
+
+    if dropdown_id!="" and dropdown_id2!="":
+        error_occurred = False
+        page1 = shared_page1
+        page2 = shared_page2
+
+        args_page1 = list(args) + [dropdown_id, input_tokens, output_tokens]
+        args_page2 = list(args) + [dropdown_id2, input_tokens, output_tokens]
+        result_page1 = page1.compute_cost_per_token(*args_page1)
+        result_page2 = page2.compute_cost_per_token(*args_page2)
+        tco1, latency, labor_cost1 = result_page1
+        tco2, latency2, labor_cost2 = result_page2   
+        return tco1, latency, labor_cost1, tco2, latency2, labor_cost2
+    else: 
+        error_occurred = True
+        raise gr.Error("Please select two AI service options.")
+
+def update_plot(tco1, tco2, dropdown, dropdown2, labour_cost1, labour_cost2):
+    if error_occurred == False:
+        request_ranges = list(range(0, 1001, 100)) + list(range(1000, 10001, 500)) + list(range(10000, 100001, 1000)) + list(range(100000, 2000001, 100000))
+        costs_tco1 = [(tco1 * req + labour_cost1) for req in request_ranges]
+        costs_tco2 = [(tco2 * req + labour_cost2) for req in request_ranges]
+
+        data = pd.DataFrame({
+            "Number of requests": request_ranges * 2,
+            "Cost ($)": 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"])
+    else:
+        return ""
+
+error_occurred = False