import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import io
import base64

text = "<h1 style='text-align: center; color: blue; font-size: 30px;'>TCO Comparison Calculator"
text1 = "<h1 style='text-align: center; color: blue; font-size: 20px;'>First solution"
text2 = "<h1 style='text-align: center; color: blue; font-size: 20px;'>Second solution"
text3 = "<h1 style='text-align: center; color: blue; font-size: 25px;'>Comparison"
text4 = "<h1 style='text-align: center; color: blue; font-size: 25px;'>Results"

diy_value = 0
saas_value = 0

def calculate_tco(model_choice, vm_rental_choice, out_diy):
    VM_cost_per_hour=3.6730 #at Azure for the basic pay as you go option
    maxed_out = 0.8 #percentage of time the VM is maxed out
    used = 0.5 #percentage of time the VM is used 
    tokens_per_request = 64
    
    if model_choice == "Llama-2-7B":
        tokens_per_second=694.38
        
    elif model_choice == "Llama-2-13B":
        tokens_per_second=1000
    
    elif model_choice == "Llama-2-70B":
        tokens_per_second=10000
    
    if vm_rental_choice == "pay as you go":
        reduction = 0
    
    elif vm_rental_choice == "1 year reserved":
        reduction = 0.34
    
    elif vm_rental_choice == "3 years reserved":
        reduction = 0.62
        
    homemade_cost_per_token = VM_cost_per_hour * (1 - reduction) / (tokens_per_second * 3600 * maxed_out * used)
    homemade_cost_per_request = tokens_per_request * homemade_cost_per_token
    out_diy = homemade_cost_per_token
    return out_diy

def calculate_tco_2(model_provider, context, out_saas):
    tokens_per_request = 64
        
    if model_provider == "OpenAI":
        if context == "4K context":
            saas_cost_per_token = 0.00035
            saas_cost_per_request = saas_cost_per_token * tokens_per_request
        elif context == "16K context" :
            saas_cost_per_token = 0.0007
            saas_cost_per_request = saas_cost_per_token * tokens_per_request
    out_saas = saas_cost_per_token
    return out_saas

def extract_cost_from_text(text):
    try:
        cost = float(text)
        return cost
    except ValueError as e:
        raise ValueError("Invalid cost text format")
    
def compare(cost_text1, cost_text2):
    try:
        # Extract the costs from the input strings
        cost1 = extract_cost_from_text(cost_text1)
        cost2 = extract_cost_from_text(cost_text2)

        r = cost1 / cost2

        if r < 1:
            comparison_result = f"First solution is cheaper, with a ratio of {r:.2f}."
        elif r > 1:
            comparison_result = f"Second solution is cheaper, with a ratio of {r:.2f}."
        else:
            comparison_result = "Both solutions will cost the same."

        return comparison_result

    except ValueError as e:
        return f"Error: {str(e)}"

def update_plot(diy_value, saas_value):
                    data = pd.DataFrame(
                        {
                            "Solution": ["Open-source", "SaaS"],
                            "Cost/token ($)": [diy_value, saas_value],
                        }
                    )
                    return gr.BarPlot.update(data, x="Solution", y="Cost/token ($)")

def render_latex(latex_str):
    fig, ax = plt.subplots(figsize=(1, 1))
    ax.text(0.5, 0.5, f"${latex_str}$", size=12, usetex=True, va="center", ha="center")
    ax.axis("off")
    
    buf = io.BytesIO()
    plt.savefig(buf, format="png")
    plt.close(fig)
    
    base64_str = base64.b64encode(buf.getvalue()).decode("utf-8")
    return f"<img src='data:image/png;base64,{base64_str}'>"

description=f"""
<p>In this demo application, we help you compare different solutions for your AI incorporation plans, such as open-source or SaaS.</p>
<p>First, you'll have to choose the two solutions you'd like to compare. Then, follow the instructions to select your configurations for each solution and we will compute the cost/request accordingly to them. Eventually, you can compare both solutions to evaluate which one best suits your needs, in the short or long term.</p>
"""
description1="This interface provides you with the cost per token you get using the open-source solution, based on the model you choose to use and how long you're planning to use it. The selected prices for a Virtual Machine rental come from Azure's VM rental plans, which can offer reductions for long-term reserved usage."
description2="This interface provides you with the cost per token resulting from the AI model provider you choose and the number of tokens you select for context, which the model will take into account when processing input texts."
description3="This interface compares the cost per request for the two solutions you selected and gives you an insight of whether a solution is more valuable in the long term."

models = ["Llama-2-7B", "Llama-2-13B", "Llama-2-70B"]
vm_rental_choice = ["pay as you go", "1 year reserved", "3 years reserved"]
model_provider = ["OpenAI"]
context = ["4K context", "16K context"]
error_box = gr.Textbox(label="Error", visible=False)

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(value=text)
    gr.Markdown(value=description)
    
    out_diy = gr.State(value=0)
    out_saas = gr.State(value=0)
    out_diy2 = gr.State(value=0)
    out_saas2 = gr.State(value=0)
    
    with gr.Row():
        with gr.Column():
        
            solution_selection = gr.Dropdown(["SaaS", "Open-source"], label="Select a Solution", value="SaaS")
            
            with gr.Row(visible=False) as title_column:            
                gr.Markdown(value=text1)

            with gr.Row(visible=False) as text_diy_column:
                gr.Markdown(description1)
            
            with gr.Accordion("Open to see the formula", visible=False, open=False) as formula_diy:
                gr.Markdown(
                    r"$ opensource\_cost\_per\_request = \frac{tokens\_per\_request \times VM\_cost\_per\_hour \times (1 - reduction)}{tokens\_per\_second \times 3600 \times maxed\_out \times used}$"
                )
            
            with gr.Row(visible=False) as input_diy_column:
                model_inp = gr.Dropdown(models, label="Select an AI Model", value="Llama-2-7B", info="Open-source AI model used for your application")
                rental_plan_inp = gr.Dropdown(vm_rental_choice, label="Select a VM Rental Plan", value="pay as you go", info="These options are from Azure's VM rental plans")
                rental_plan_inp.change(fn=calculate_tco, inputs=[model_inp, rental_plan_inp, out_diy], outputs=out_diy)

            with gr.Row(visible=False) as text_saas_column:
                gr.Markdown(description2)
            
            with gr.Accordion("Open to see the formula", visible=False, open=False) as formula_saas:
                gr.Markdown(
                    r"$ saas\_cost\_per\_request = saas\_cost\_per\_token \times tokens\_per\_request$"
                )
            
            with gr.Row(visible=False) as input_saas_column:
                model_provider_inp = gr.Dropdown(model_provider, label="Model Provider", vallue="OpenAI", info="Choose an AI model provider you want to work with")
                context_inp = gr.Dropdown(context, label="Context", value="4K context", info="Number of tokens the model considers when processing text")
                context_inp.change(fn=calculate_tco_2, inputs=[model_provider_inp, context_inp, out_saas], outputs=out_saas)
    
            def submit(solution_selection):
                if solution_selection == "Open-source":
                    return {
                        formula_diy: gr.update(visible=True),
                        title_column: gr.update(visible=True),
                        text_diy_column: gr.update(visible=True),
                        input_diy_column: gr.update(visible=True),
                        formula_saas: gr.update(visible=False),
                        text_saas_column: gr.update(visible=False),                       
                        input_saas_column: gr.update(visible=False),
                    }     
                else: 
                    return {
                        formula_saas: gr.update(visible=True),
                        formula_diy: gr.update(visible=False),
                        text_diy_column: gr.update(visible=False),
                        input_diy_column: gr.update(visible=False),
                        title_column: gr.update(visible=True),                        
                        text_saas_column: gr.update(visible=True),                       
                        input_saas_column: gr.update(visible=True),
                    }
            
            solution_selection.change(
                submit,
                solution_selection,
                [out_saas, text_diy_column, formula_diy, formula_saas, title_column, text_saas_column, model_inp, rental_plan_inp, model_provider_inp, context_inp, input_diy_column, input_saas_column],
            )
            
        # gr.Divider(style="vertical", thickness=2, color="blue")
         
        with gr.Column():
            
            solution_selection2 = gr.Dropdown(["SaaS", "Open-source"], value="Open-source", label="Select a Solution")
            
            with gr.Row(visible=False) as title_column2:            
                gr.Markdown(value=text2)
            
            with gr.Row(visible=False) as text_diy_column2:            
                gr.Markdown(description1)
            
            with gr.Accordion("Open to see the formula", visible=False, open=False) as formula_diy2:
                gr.Markdown(
                    r"$ homemade\_cost\_per\_request = \frac{tokens\_per\_request \times VM\_cost\_per\_hour \times (1 - reduction)}{tokens\_per\_second \times 3600 \times maxed\_out \times used}$"
                )
            
            with gr.Row(visible=False) as input_diy_column2:
                model_inp2 = gr.Dropdown(models, label="Select an AI Model", value="Llama-2-7B", info="Open-source AI model used for your application")
                rental_plan_inp2 = gr.Dropdown(vm_rental_choice, label="Select a VM Rental Plan", value="pay as you go", info="These options are from Azure's VM rental plans")
                rental_plan_inp2.change(fn=calculate_tco, inputs=[model_inp2, rental_plan_inp2, out_diy2], outputs=out_diy2)
                      
            with gr.Row(visible=False) as text_saas_column2:            
                gr.Markdown(description2)
            
            with gr.Accordion("Open to see the formula", visible=False, open=False) as formula_saas2:
                gr.Markdown(
                    r"$ saas\_cost\_per\_request = saas\_cost\_per\_token \times tokens\_per\_request$"
                )
            
            with gr.Row(visible=False) as input_saas_column2:
                model_provider_inp2 = gr.Dropdown(['OpenAI'], label="Model Provider", value="OpenAI", info="Choose an AI model provider you want to work with")
                context_inp2 = gr.Dropdown(['4K context', '16K context'], label="Context", value="4K context", info="Number of tokens the model considers when processing text")
                context_inp2.change(fn=calculate_tco_2, inputs=[model_provider_inp2, context_inp2, out_saas2], outputs=out_saas2)
            
            def submit2(solution_selection2):
                if solution_selection2 == "Open-source":
                    return {
                        formula_diy2: gr.update(visible=True),
                        title_column2: gr.update(visible=True),
                        text_diy_column2: gr.update(visible=True),
                        input_diy_column2: gr.update(visible=True),
                        formula_saas2: gr.update(visible=False),
                        text_saas_column2: gr.update(visible=False),                       
                        input_saas_column2: gr.update(visible=False),
                    }     
                else: 
                    return {
                        formula_diy2: gr.update(visible=False),
                        text_diy_column2: gr.update(visible=False),
                        input_diy_column2: gr.update(visible=False),
                        title_column2: gr.update(visible=True),
                        formula_saas2: gr.update(visible=True),
                        text_saas_column2: gr.update(visible=True),                        
                        input_saas_column2: gr.update(visible=True),
                    }
            
            solution_selection2.change(
                submit2,
                solution_selection2,
                [out_diy2, out_saas2, formula_diy2, formula_saas2, title_column2, text_diy_column2, text_saas_column2, model_inp2, rental_plan_inp2, model_provider_inp2, context_inp2, input_diy_column2, input_saas_column2],
            )
    
    with gr.Row():
        with gr.Column():
            
            with gr.Row():
                gr.Markdown(text3)
                
            with gr.Row():
                plot = gr.BarPlot(vertical=False, title="Comparison", y_title="Cost/token ($)", width=500, interactive=True)
                
            context_inp.change(fn=update_plot, inputs=[out_diy2, out_saas], outputs=plot)
            model_provider_inp.change(fn=update_plot, inputs=[out_diy2, out_saas], outputs=plot)
            rental_plan_inp2.change(fn=update_plot, inputs=[out_diy2, out_saas], outputs=plot)
            model_inp2.change(fn=update_plot, inputs=[out_diy2, out_saas], outputs=plot)
            
            context_inp2.change(fn=update_plot, inputs=[out_diy, out_saas2], outputs=plot)
            model_provider_inp2.change(fn=update_plot, inputs=[out_diy, out_saas2], outputs=plot)
            rental_plan_inp.change(fn=update_plot, inputs=[out_diy, out_saas2], outputs=plot)
            model_inp.change(fn=update_plot, inputs=[out_diy, out_saas2], outputs=plot)
       
demo.launch()