Update app.py

app.py

@@ -191,14 +191,14 @@ def plot_confusion_matrix_from_csv(csv_file_path, title, save_path, light_mode=F
     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=text_color, fontsize=24)
+    plt.title(f"{title}\n(F1 Score: {f1:.3f})", color=text_color, fontsize=18)
 
     # Customize tick labels for light/dark mode
-    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
-    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
+    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=12)
+    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=12)
 
-    plt.ylabel('True label', color=text_color, fontsize=18)
-    plt.xlabel('Predicted label', color=text_color, fontsize=18)
+    plt.ylabel('True label', color=text_color, fontsize=14)
+    plt.xlabel('Predicted label', color=text_color, fontsize=14)
     plt.tight_layout()
 
     # Save the plot as an image
@@ -362,14 +362,14 @@ def plot_confusion_matrix(y_true, y_pred, title, light_mode=False):
     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}\nF1 Score: {f1:.3f}", color=text_color, fontsize=23)
+    plt.title(f"{title}\nF1 Score: {f1:.3f}", color=text_color, fontsize=18)
     
     # Customize tick labels for dark mode
-    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
-    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
+    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=12)
+    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=12)
     
-    plt.ylabel('True label', color=text_color, fontsize=18)
-    plt.xlabel('Predicted label', color=text_color, fontsize=18)
+    plt.ylabel('True label', color=text_color, fontsize=14)
+    plt.xlabel('Predicted label', color=text_color, fontsize=14)
     plt.tight_layout()
     
     # Save the plot as an image