Create app.py

app.py

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+import os
+from keras.models import Model
+from tensorflow.keras.optimizers import Adam
+from keras.applications.vgg16 import VGG16, preprocess_input
+from keras.preprocessing.image import ImageDataGenerator
+from keras.callbacks import ModelCheckpoint, EarlyStopping
+from keras.layers import Dense, Dropout, Flatten
+from pathlib import Path
+import numpy as np
+
+
+
+BATCH_SIZE = 64
+
+train_generator = ImageDataGenerator(rotation_range=90, 
+                                     brightness_range=[0.1, 0.7],
+                                     width_shift_range=0.5, 
+                                     height_shift_range=0.5,
+                                     horizontal_flip=True, 
+                                     vertical_flip=True,
+                                     validation_split=0.15,
+                                     preprocessing_function=preprocess_input) # VGG16 preprocessing
+
+test_generator = ImageDataGenerator(preprocessing_function=preprocess_input) # VGG16 preprocessing
+
+
+
+
+
+train_data_dir = '/kaggle/input/pic-a-plant2/DBa4/‏‏DBa/train'
+test_data_dir = '/kaggle/input/pic-a-plant2/DBa4/‏‏DBa/test'
+
+class_subset = sorted(os.listdir(train_data_dir))[:] # Using only the first 10 classes
+
+traingen = train_generator.flow_from_directory(train_data_dir,
+                                               target_size=(150, 150),
+                                               class_mode='categorical',
+                                               classes=class_subset,
+                                               subset='training',
+                                               batch_size=BATCH_SIZE, 
+                                               shuffle=True,
+                                               seed=42)
+
+validgen = train_generator.flow_from_directory(train_data_dir,
+                                               target_size=(150, 150),
+                                               class_mode='categorical',
+                                               classes=class_subset,
+                                               subset='validation',
+                                               batch_size=BATCH_SIZE,
+                                               shuffle=True,
+                                               seed=42)
+
+testgen = test_generator.flow_from_directory(test_data_dir,
+                                             target_size=(150, 150),
+                                             class_mode=None,
+                                             classes=class_subset,
+                                             batch_size=1,
+                                             shuffle=False,
+                                             seed=42)