diff --git a/gui.js b/gui.js
index 664a527a21..06050afdc8 100644
--- a/gui.js
+++ b/gui.js
@@ -6191,7 +6191,7 @@ function _get_tensorflow_save_model_code () {
 model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
 model.fit(train_generator, validation_data=validation_generator, epochs=${_epochs})
 
-# Speichere das trainierte Modell
+# Save the model to model.h5 for future usage.
 model.save('model.h5')
 
 `
@@ -6205,31 +6205,31 @@ function _get_tensorflow_data_loader_code () {
 return `
 from tensorflow.keras.preprocessing.image import ImageDataGenerator
 
-# Definiere den Pfad zum Ordner mit den Bildern
+# Define path to data
 data_dir = 'data/'
 
-# Definiere die Größe, zu der die Bilder resized werden sollen
+# Define size of images
 target_size = (${height}, ${width})
 
-# Erstelle einen ImageDataGenerator, um die Bilder einzulesen und zu resizen
-datagen = ImageDataGenerator(rescale=1./divide_by, # Normalisiere die Bildpixel
-                             validation_split=${_validation_split}, # Splitte die Daten automatisch in Trainings- und Validierungssets
-                             preprocessing_function=lambda x: tf.image.resize(x, target_size)) # Resize die Bilder
+# Create ImageDataGenerator to read images and resize them
+datagen = ImageDataGenerator(rescale=1./divide_by, # Normalize (from 0-255 to 0-1)
+                             validation_split=${_validation_split}, # Split into validation and training datasets
+                             preprocessing_function=lambda x: tf.image.resize(x, target_size)) # Resize images
 
-# Lese die Bilder aus dem Ordner ein und teile sie automatisch in Trainings- und Validierungssets auf
+# Read images and split them into training and validation dataset automatically
 train_generator = datagen.flow_from_directory(
     data_dir,
     target_size=target_size,
     batch_size=${_batch_size},
     class_mode='categorical',
-    subset='training') # 'training' für das Trainingsset
+    subset='training')
 
 validation_generator = datagen.flow_from_directory(
     data_dir,
     target_size=target_size,
     batch_size=32,
     class_mode='categorical',
-    subset='validation') # 'validation' für das Validierungsset
+    subset='validation')
 
 `;
 }