Update app.py

app.py

@@ -132,10 +132,10 @@ LOS_PATH = "images_LoS"
 # Define the percentage values
 percentage_values_los = np.linspace(0.001, 1, 20) * 100  # 20 percentage values
 
-# Function to compute confusion matrix and plot it
 from sklearn.metrics import f1_score
 import seaborn as sns
 
+# Function to compute confusion matrix, F1-score and plot it with dark mode style
 def plot_confusion_matrix_from_csv(csv_file_path, title, save_path):
     # Load CSV file
     data = pd.read_csv(csv_file_path)
@@ -147,30 +147,29 @@ def plot_confusion_matrix_from_csv(csv_file_path, title, save_path):
     # Compute confusion matrix
     cm = confusion_matrix(y_true, y_pred)
     
-    # Calculate F1 Score
-    f1 = f1_score(y_true, y_pred, average='weighted')
-
-    # Plot the confusion matrix with dark mode colors
+    # Compute F1-score
+    f1 = f1_score(y_true, y_pred, average='macro')  # Macro-average F1-score
+    
+    # Set dark mode styling
+    plt.style.use('dark_background')
     plt.figure(figsize=(5, 5))
-    plt.imshow(cm, interpolation='nearest', cmap='coolwarm')  # Dark mode color scheme
-    plt.title(f"{title}\nF1-Score: {f1:.2f}", color='white', pad=20)  # Display F1-Score in title, add padding for better visibility
-    plt.colorbar()
-    plt.xticks([0, 1], labels=['Class 0', 'Class 1'], color='white')  # White text for dark mode
-    plt.yticks([0, 1], labels=['Class 0', 'Class 1'], color='white')  # White text for dark mode
     
-    # Annotate the confusion matrix
-    thresh = cm.max() / 2
-    for i in range(cm.shape[0]):
-        for j in range(cm.shape[1]):
-            plt.text(j, i, format(cm[i, j], 'd'), ha="center", va="center", 
-                     color="white" if cm[i, j] > thresh else "black")
+    # 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')
     
-    plt.ylabel('True label', color='white')  # White text for dark mode
-    plt.xlabel('Predicted label', color='white')  # White text for dark mode
-    plt.tight_layout(pad=2.0)  # Add padding to prevent clipping of labels
+    # Add F1-score to the title
+    plt.title(f"{title} (F1 Score: {f1:.3f})", color="white", 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.ylabel('True label', color="white", fontsize=12)
+    plt.xlabel('Predicted label', color="white", fontsize=12)
+    plt.tight_layout()
     
     # Save the plot as an image
-    plt.savefig(save_path, facecolor='black')  # Set background to black for dark mode
+    plt.savefig(save_path, transparent=True)  # Use transparent to blend with the dark mode website
     plt.close()
     
     # Return the saved image