Update app.py

app.py

@@ -330,21 +330,32 @@ def plot_confusion_matrix(y_true, y_pred, title):
     # Calculate F1 Score
     f1 = f1_score(y_true, y_pred, average='weighted')
 
-    plt.style.use('dark_background')
+    #plt.style.use('dark_background')
+    
+    # Set styling based on light or dark mode
+    if light_mode:
+        plt.style.use('default')  # Light mode styling
+        text_color = 'black'
+        cmap = 'Blues'  # Light-mode-friendly colormap
+    else:
+        plt.style.use('dark_background')  # Dark mode styling
+        text_color = 'white'
+        cmap = 'magma'  # Dark-mode-friendly colormap
+        
     plt.figure(figsize=(5, 5))
     
     # Plot the confusion matrix with a dark-mode compatible colormap
-    sns.heatmap(cm, annot=True, fmt="d", cmap="magma", cbar=False, annot_kws={"size": 12}, linewidths=0.5, linecolor='white')
+    sns.heatmap(cm, annot=True, fmt="d", cmap=cmap, cbar=False, annot_kws={"size": 12}, linewidths=0.5, linecolor='white')
     
     # Add F1-score to the title
-    plt.title(f"{title}\n(F1 Score: {f1:.3f})", color="white", fontsize=14)
+    plt.title(f"{title}\n(F1 Score: {f1:.3f})", color=text_color, fontsize=14)
     
     # Customize tick labels for dark mode
-    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color="white", fontsize=10)
-    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color="white", fontsize=10)
+    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=10)
+    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=10)
     
-    plt.ylabel('True label', color="white", fontsize=12)
-    plt.xlabel('Predicted label', color="white", fontsize=12)
+    plt.ylabel('True label', color=text_color, fontsize=12)
+    plt.xlabel('Predicted label', color=text_color, fontsize=12)
     plt.tight_layout()
     
     # Save the plot as an image