Update app.py

app.py

@@ -9,17 +9,11 @@ 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'
 
@@ -64,27 +58,20 @@ 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]))
 
-
-bees = list(data_dir.glob('bees/*'))
-print(bees[0])
-PIL.Image.open(str(bees[0]))
-
-
-img_height,img_width=180,180
-batch_size=32
+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,
@@ -92,15 +79,12 @@ 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):
@@ -109,7 +93,6 @@ for images, labels in train_ds.take(1):
     plt.title(class_names[labels[i]])
     plt.axis("off")
 
-
 data_augmentation = keras.Sequential(
   [
     layers.RandomFlip("horizontal",
@@ -118,10 +101,11 @@ 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):
@@ -130,39 +114,43 @@ 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,
   layers.Rescaling(1./255),
-  layers.Conv2D(16, 3, padding='same', activation='relu'),
-  layers.MaxPooling2D(),
   layers.Conv2D(32, 3, padding='same', activation='relu'),
   layers.MaxPooling2D(),
   layers.Conv2D(64, 3, padding='same', activation='relu'),
   layers.MaxPooling2D(),
-  layers.Dropout(0.2),
+  layers.Conv2D(128, 3, padding='same', activation='relu'),
+  layers.MaxPooling2D(),
+  layers.Dropout(0.5),
   layers.Flatten(),
-  layers.Dense(128, activation='relu'),
-  layers.Dense(num_classes, activation='softmax', name="outputs")  # Use softmax here
+  layers.Dense(256, activation='relu'),
+  layers.Dropout(0.5),
+  layers.Dense(num_classes, activation='softmax', name="outputs")
 ])
 
 model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),  # Change from_logits to False
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
               metrics=['accuracy'])
 
 model.summary()
 
+# Learning rate scheduler
+lr_scheduler = keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch / 20))
 
-epochs = 15
+# Early stopping
+early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
+
+epochs = 20
 history = model.fit(
   train_ds,
   validation_data=val_ds,
-  epochs=epochs
+  epochs=epochs,
+  callbacks=[lr_scheduler, early_stopping]
 )
 
-
-
 # Define category descriptions
 category_descriptions = {
     "Ants": "Ants are small insects known for their complex social structures and teamwork.",
@@ -194,6 +182,16 @@ def predict_image(img):
 image = gr.Image()
 label = gr.Label(num_top_classes=1)
 
+# Define custom CSS for background image
+custom_css = """
+body {
+    background-image: url('extracted_files/Pest_Dataset/bees/bees (444).jpg');
+    background-size: cover;
+    background-repeat: no-repeat;
+    background-attachment: fixed;
+    color: white;
+}
+"""
 
 gr.Interface(
     fn=predict_image,
@@ -203,4 +201,3 @@ 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)
-