Adjusting tools accuracy graphs

app.py

@@ -381,7 +381,7 @@ with demo:
                 )
             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"
+                    "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. The minimum value of this custom metric is 0 and the maximum value is 1. The higher the better is the tool."
                 )
             with gr.Row():
                 _ = plot_tools_weighted_accuracy_graph(tools_accuracy_info)

tabs/tool_accuracy.py

@@ -8,6 +8,21 @@ VOLUME_FACTOR_REGULARIZATION = 0.5
 UNSCALED_WEIGHTED_ACCURACY_INTERVAL = (-0.5, 100.5)
 SCALED_WEIGHTED_ACCURACY_INTERVAL = (0, 1)
 
+# tools palette as dictionary
+tools_palette = {
+    "prediction-request-reasoning": "darkorchid",
+    "claude-prediction-offline": "rebeccapurple",
+    "prediction-request-reasoning-claude": "slateblue",
+    "prediction-request-rag-claude": "steelblue",
+    "prediction-online": "darkcyan",
+    "prediction-offline": "mediumaquamarine",
+    "claude-prediction-online": "mediumseagreen",
+    "prediction-online-sme": "yellowgreen",
+    "prediction-url-cot-claude": "gold",
+    "prediction-offline-sme": "orange",
+    "prediction-request-rag": "chocolate",
+}
+
 
 def scale_value(
     value: float,
@@ -56,8 +71,11 @@ def plot_tools_accuracy_graph(tools_accuracy_info: pd.DataFrame):
         y="tool",
         hue="tool",
         dodge=False,
-        palette="viridis",
+        palette=tools_palette,
     )
+    plt.xlabel("Mech tool_accuracy (%)", fontsize=20)
+    plt.ylabel("tool", fontsize=20)
+    plt.tick_params(axis="y", labelsize=12)
     return gr.Plot(value=plot.get_figure())
 
 
@@ -66,7 +84,7 @@ def plot_tools_weighted_accuracy_graph(tools_accuracy_info: pd.DataFrame):
         by="weighted_accuracy", ascending=False
     )
     # Create the Seaborn bar plot
-    sns.set_theme(palette="viridis")
+    # sns.set_theme(palette="viridis")
     plt.figure(figsize=(25, 10))
     plot = sns.barplot(
         tools_accuracy_info,
@@ -74,6 +92,9 @@ def plot_tools_weighted_accuracy_graph(tools_accuracy_info: pd.DataFrame):
         y="tool",
         hue="tool",
         dodge=False,
+        palette=tools_palette,
     )
-    # Display the plot using gr.Plot
+    plt.xlabel("Weighted accuracy metric", fontsize=20)
+    plt.ylabel("tool", fontsize=20)
+    plt.tick_params(axis="y", labelsize=12)
     return gr.Plot(value=plot.get_figure())

tabs/trades.py

@@ -73,7 +73,6 @@ def get_overall_winning_by_market_trades(trades_df: pd.DataFrame) -> pd.DataFram
     winning_trades = winning_trades.reset_index()
     winning_trades.columns = winning_trades.columns.astype(str)
     winning_trades.columns = ["month_year_week", "market_creator", "winning_trade"]
-    print(winning_trades.head())
     return winning_trades