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import pandas as pd |
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import gradio as gr |
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import matplotlib.pyplot as plt |
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shared_page1 = None |
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shared_page2 = None |
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def set_shared_pages(page1, page2): |
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global shared_page1, shared_page2 |
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shared_page1 = page1 |
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shared_page2 = page2 |
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def compare_info(tco1, tco2, dropdown, dropdown2): |
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if error_occurred == False : |
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r = tco1 / tco2 |
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if r < 1: |
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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.""" |
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elif r > 1: |
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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.""" |
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else: |
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comparison_result = f"""Both solutions have the <b>same cost/request</b>.""" |
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services = [dropdown, dropdown2] |
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costs_to_compare = [tco1, tco2] |
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plt.figure(figsize=(6, 4)) |
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plt.bar(services, costs_to_compare, color=['red', 'green']) |
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plt.xlabel('AI option services', fontsize=10) |
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plt.ylabel('($) Cost/Request', fontsize=10) |
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plt.title('Comparison of Cost/Request', fontsize=14) |
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plt.tight_layout() |
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plt.savefig('cost_comparison.png') |
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return gr.update(value='cost_comparison.png', visible=True), comparison_result |
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else: |
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return None, "" |
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def create_table(tco1, tco2, labor_cost1, labor_cost2, dropdown, dropdown2, latency, latency2): |
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if error_occurred == False: |
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if shared_page1 is None or shared_page2 is None: |
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raise ValueError("Shared instances not set.") |
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list_values = [] |
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first_sol = [tco1, labor_cost1, latency] |
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second_sol = [tco2, labor_cost2, latency2] |
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list_values.append(first_sol) |
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list_values.append(second_sol) |
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data = pd.DataFrame(list_values, index=[dropdown, dropdown2], columns=["Cost/request ($) ", "Labor Cost ($/month)", "Average latency (s)"]) |
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formatted_data = data.copy() |
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formatted_data["Cost/request ($) "] = formatted_data["Cost/request ($) "].apply('{:.5f}'.format) |
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formatted_data["Labor Cost ($/month)"] = formatted_data["Labor Cost ($/month)"].apply('{:.0f}'.format) |
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styled_data = formatted_data.style\ |
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.set_properties(**{'background-color': '#ffffff', 'color': '#000000', 'border-color': '#e0e0e0', 'border-width': '1px', 'border-style': 'solid'})\ |
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.to_html() |
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centered_styled_data = f"<center>{styled_data}</center>" |
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return gr.update(value=centered_styled_data) |
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else: |
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return "" |
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def compute_cost_per_request(*args): |
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dropdown_id = args[-4] |
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dropdown_id2 = args[-3] |
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input_tokens = args[-2] |
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output_tokens = args[-1] |
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global error_occurred |
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if dropdown_id!="" and dropdown_id2!="": |
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error_occurred = False |
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page1 = shared_page1 |
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page2 = shared_page2 |
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args_page1 = list(args) + [dropdown_id, input_tokens, output_tokens] |
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args_page2 = list(args) + [dropdown_id2, input_tokens, output_tokens] |
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result_page1 = page1.compute_cost_per_token(*args_page1) |
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result_page2 = page2.compute_cost_per_token(*args_page2) |
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tco1, latency, labor_cost1 = result_page1 |
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tco2, latency2, labor_cost2 = result_page2 |
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return tco1, latency, labor_cost1, tco2, latency2, labor_cost2 |
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else: |
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error_occurred = True |
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raise gr.Error("Please select two AI service options.") |
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def update_plot(tco1, tco2, dropdown, dropdown2, labour_cost1, labour_cost2): |
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if error_occurred == False: |
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request_ranges = list(range(0, 1001, 100)) + list(range(1000, 10001, 500)) + list(range(10000, 100001, 1000)) + list(range(100000, 1600001, 100000)) |
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costs_tco1 = [(tco1 * req + labour_cost1) for req in request_ranges] |
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costs_tco2 = [(tco2 * req + labour_cost2) for req in request_ranges] |
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data = pd.DataFrame({ |
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"Number of requests": request_ranges * 2, |
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"Cost ($)": costs_tco1 + costs_tco2, |
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"AI model service": ["1)" + " " + dropdown] * len(request_ranges) + ["2)" + " " + dropdown2] * len(request_ranges) |
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} |
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) |
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return gr.LinePlot.update(data, visible=True, x="Number of requests", y="Cost ($)", color="AI model service", color_legend_title=" ", color_legend_position="right", title="TCO for one month", height=300, width=500, tooltip=["Number of requests", "Cost ($)", "AI model service"]) |
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else: |
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return "" |
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error_occurred = False |
