Update app.py

app.py

@@ -67,7 +67,7 @@ def compute_f1_score(cm):
     f1 = np.nan_to_num(f1)  # Replace NaN with 0
     return np.mean(f1)  # Return the mean F1-score across all classes
 
-def plot_confusion_matrix_beamPred(cm, classes, title, save_path, theme=None):
+def plot_confusion_matrix_beamPred(cm, classes, title, save_path, theme='Light'):
     # Compute the average F1-score
     avg_f1 = compute_f1_score(cm)
 
@@ -86,10 +86,10 @@ def plot_confusion_matrix_beamPred(cm, classes, title, save_path, theme=None):
     plt.figure(figsize=(10, 10))
 
     # Plot the confusion matrix with a colormap compatible for the mode
-    sns.heatmap(cm, cmap=cmap, cbar=True, linecolor='white', vmin=0, vmax=cm.max(), alpha=0.85)
+    sns.heatmap(cm, cmap=cmap, cbar=True, linecolor=text_color, vmin=0, vmax=cm.max(), alpha=0.85)
 
     # Add F1-score to the title
-    plt.title(f"{title}\n(F1 Score: {avg_f1:.3f})", color=text_color, fontsize=14)
+    plt.title(f"{title}\n(F1 Score: {avg_f1:.3f})", color=text_color, fontsize=20)
 
     tick_marks = np.arange(len(classes))
     plt.xticks(tick_marks, classes, color=text_color, fontsize=14)  # Adjust text color based on the mode