Update app.py

app.py

@@ -293,7 +293,8 @@ def generate_plots():
     # Plot 1: Decision Breakdown
     try:
         plt.figure(figsize=(8, 6))
-        sns.countplot(data=global_df, x='Decision', palette='viridis', order=global_df['Decision'].value_counts().index)
+        # Add hue and set legend=False to resolve FutureWarning
+        sns.countplot(data=global_df, x='Decision', hue='Decision', palette='viridis', order=global_df['Decision'].value_counts().index, legend=False)
         plt.title('Decision Breakdown for the Fleet')
         plt.xlabel('Decision')
         plt.ylabel('Number of Trucks')
@@ -312,7 +313,8 @@ def generate_plots():
         # Filter out NaN/None ownership types if any remain for plotting robustness
         plot_df = global_df[global_df['ownership_type'].notna() & (global_df['ownership_type'] != 'NAN')]
         if not plot_df.empty:
-            sns.boxplot(data=plot_df, x='ownership_type', y='total_repairs', palette='coolwarm')
+            # Add hue and set legend=False to resolve FutureWarning
+            sns.boxplot(data=plot_df, x='ownership_type', y='total_repairs', hue='ownership_type', palette='coolwarm', legend=False)
             plt.title('Total Repairs by Ownership Type')
             plt.xlabel('Ownership Type')
             plt.ylabel('Total Repairs ($)')