Update app.py

app.py

@@ -9,11 +9,17 @@ from tensorflow import keras
 from tensorflow.keras import layers
 from tensorflow.keras.models import Sequential
 
+
 from PIL import Image
 import gdown
 import zipfile
+
 import pathlib
 
+
+
+
+
 # Define the Google Drive shareable link
 gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'
 
@@ -58,20 +64,27 @@ import pathlib
 data_dir = pathlib.Path('extracted_files/Pest_Dataset')
 data_dir = pathlib.Path(data_dir)
 
+
 bees = list(data_dir.glob('bees/*'))
 print(bees[0])
 PIL.Image.open(str(bees[0]))
 
-img_height, img_width = 180, 180
-batch_size = 32
+
+bees = list(data_dir.glob('bees/*'))
+print(bees[0])
+PIL.Image.open(str(bees[0]))
+
+
+img_height,img_width=180,180
+batch_size=32
 train_ds = tf.keras.preprocessing.image_dataset_from_directory(
   data_dir,
   validation_split=0.2,
   subset="training",
   seed=123,
   image_size=(img_height, img_width),
-  batch_size=batch_size
-)
+  batch_size=batch_size)
+
 
 val_ds = tf.keras.preprocessing.image_dataset_from_directory(
   data_dir,
@@ -79,12 +92,15 @@ val_ds = tf.keras.preprocessing.image_dataset_from_directory(
   subset="validation",
   seed=123,
   image_size=(img_height, img_width),
-  batch_size=batch_size
-)
+  batch_size=batch_size)
+
 
 class_names = train_ds.class_names
 print(class_names)
 
+
+import matplotlib.pyplot as plt
+
 plt.figure(figsize=(10, 10))
 for images, labels in train_ds.take(1):
   for i in range(9):
@@ -93,6 +109,7 @@ for images, labels in train_ds.take(1):
     plt.title(class_names[labels[i]])
     plt.axis("off")
 
+
 data_augmentation = keras.Sequential(
   [
     layers.RandomFlip("horizontal",
@@ -101,11 +118,10 @@ data_augmentation = keras.Sequential(
                                   3)),
     layers.RandomRotation(0.1),
     layers.RandomZoom(0.1),
-    layers.RandomContrast(0.1),
-    layers.RandomBrightness(0.1)
   ]
 )
 
+
 plt.figure(figsize=(10, 10))
 for images, _ in train_ds.take(1):
   for i in range(9):
@@ -114,6 +130,7 @@ for images, _ in train_ds.take(1):
     plt.imshow(augmented_images[0].numpy().astype("uint8"))
     plt.axis("off")
 
+
 num_classes = len(class_names)
 model = Sequential([
   data_augmentation,
@@ -124,11 +141,11 @@ model = Sequential([
   layers.MaxPooling2D(),
   layers.Conv2D(128, 3, padding='same', activation='relu'),
   layers.MaxPooling2D(),
-  layers.Dropout(0.5),
+  layers.Dropout(0.5),  # Adding dropout regularization
   layers.Flatten(),
   layers.Dense(256, activation='relu'),
-  layers.Dropout(0.5),
-  layers.Dense(num_classes, activation='softmax', name="outputs")
+  layers.Dropout(0.5),  # Adding dropout regularization
+  layers.Dense(num_classes, activation='softmax', name="outputs")  
 ])
 
 model.compile(optimizer='adam',
@@ -137,20 +154,16 @@ model.compile(optimizer='adam',
 
 model.summary()
 
-# Learning rate scheduler
-lr_scheduler = keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch / 20))
-
-# Early stopping
-early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
 
-epochs = 20
+epochs = 15
 history = model.fit(
   train_ds,
   validation_data=val_ds,
-  epochs=epochs,
-  callbacks=[lr_scheduler, early_stopping]
+  epochs=epochs
 )
 
+
+
 # Define category descriptions
 category_descriptions = {
     "Ants": "Ants are small insects known for their complex social structures and teamwork.",
@@ -201,3 +214,4 @@ gr.Interface(
     description="The image data set used was obtained from Kaggle and has a collection of 12 different types of agricultural pests: Ants, Bees, Beetles, Caterpillars, Earthworms, Earwigs, Grasshoppers, Moths, Slugs, Snails, Wasps, and Weevils",
     css=custom_css
 ).launch(debug=True)
+