weighted accuracy between 0 and 1. Cleaning

app.py

@@ -19,6 +19,12 @@ from tabs.tool_win import (
     plot_tool_winnings_overall,
     plot_tool_winnings_by_tool,
 )
+
+from tabs.tool_accuracy import (
+    compute_weighted_accuracy,
+    plot_tools_accuracy_graph,
+    plot_tools_weighted_accuracy_graph,
+)
 from tabs.error import (
     get_error_data,
     get_error_data_overall,
@@ -112,19 +118,6 @@ def get_all_data():
     return df1, df2, df3
 
 
-def get_weighted_accuracy(row, global_requests: int):
-    """Function to compute the weighted accuracy of a tool"""
-    return row["tool_accuracy"] * (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 prepare_data():
     """
     Prepare the data for the dashboard
@@ -267,47 +260,22 @@ with demo:
         with gr.TabItem("🎯 Tool Accuracy Dashboard"):
             with gr.Row():
                 gr.Markdown("# Tools accuracy ranking")
-                gr.Markdown("")
+            with gr.Row():
                 gr.Markdown(
                     "The data used for this metric is from the past two months. This accuracy is computed based on right answers from the total requests received."
                 )
 
             with gr.Row():
-                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",
-                )
-                gr.Plot(value=plot.get_figure())
+                plot_tools_accuracy_graph(tools_accuracy_info)
+
             with gr.Row():
                 gr.Markdown("# Weighted accuracy ranking per tool")
-                gr.Markdown("")
+            with gr.Row():
                 gr.Markdown(
                     "The data used for this metric is from the past two months. This metric is computed using both the tool accuracy and the volume of requests received by the tool"
                 )
             with gr.Row():
-                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())
+                plot_tools_weighted_accuracy_graph(tools_accuracy_info)
 
         with gr.TabItem("🏥 Tool Error Dashboard"):
             with gr.Row():

tabs/tool_accuracy.py

@@ -0,0 +1,51 @@
+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())