tabs/tool_accuracy.py

import pandas as pd
import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns


def get_weighted_accuracy(row, global_requests: int):
    """Function to compute the weighted accuracy of a tool"""
    return (row["tool_accuracy"] / 100.0) * (row["total_requests"] / global_requests)


def compute_weighted_accuracy(tools_accuracy: pd.DataFrame):
    global_requests = tools_accuracy.total_requests.sum()
    tools_accuracy["weighted_accuracy"] = tools_accuracy.apply(
        lambda x: get_weighted_accuracy(x, global_requests), axis=1
    )
    return tools_accuracy


def plot_tools_accuracy_graph(tools_accuracy_info: pd.DataFrame):
    tools_accuracy_info = tools_accuracy_info.sort_values(
        by="tool_accuracy", ascending=False
    )
    plt.figure(figsize=(25, 10))
    plot = sns.barplot(
        tools_accuracy_info,
        x="tool_accuracy",
        y="tool",
        hue="tool",
        dodge=False,
        palette="viridis",
    )
    return gr.Plot(value=plot.get_figure())


def plot_tools_weighted_accuracy_graph(tools_accuracy_info: pd.DataFrame):
    tools_accuracy_info = tools_accuracy_info.sort_values(
        by="weighted_accuracy", ascending=False
    )
    # Create the Seaborn bar plot
    sns.set_theme(palette="viridis")
    plt.figure(figsize=(25, 10))
    plot = sns.barplot(
        tools_accuracy_info,
        x="weighted_accuracy",
        y="tool",
        hue="tool",
        dodge=False,
    )
    # Display the plot using gr.Plot
    gr.Plot(value=plot.get_figure())