Update app.py

app.py

@@ -8,6 +8,7 @@ import tensorflow as tf
 from tensorflow import keras
 from tensorflow.keras import layers
 from tensorflow.keras.models import Sequential
+from tensorflow.keras.applications import EfficientNetB0
 
 from PIL import Image
 import gdown
@@ -53,102 +54,75 @@ for root, dirs, files in os.walk(extracted_path):
     for f in files:
         print(f"{subindent}{f}")
 
-import pathlib
 # Path to the dataset directory
 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
 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
+    data_dir,
+    validation_split=0.2,
+    subset="training",
+    seed=123,
+    image_size=(img_height, img_width),
+    batch_size=batch_size
 )
 
 val_ds = tf.keras.preprocessing.image_dataset_from_directory(
-  data_dir,
-  validation_split=0.2,
-  subset="validation",
-  seed=123,
-  image_size=(img_height, img_width),
-  batch_size=batch_size
+    data_dir,
+    validation_split=0.2,
+    subset="validation",
+    seed=123,
+    image_size=(img_height, img_width),
+    batch_size=batch_size
 )
 
 class_names = train_ds.class_names
 print(class_names)
 
-plt.figure(figsize=(10, 10))
-for images, labels in train_ds.take(1):
-  for i in range(9):
-    ax = plt.subplot(3, 3, i + 1)
-    plt.imshow(images[i].numpy().astype("uint8"))
-    plt.title(class_names[labels[i]])
-    plt.axis("off")
-
+# Enhanced Data Augmentation
 data_augmentation = keras.Sequential(
-  [
-    layers.RandomFlip("horizontal",
-                      input_shape=(img_height,
-                                  img_width,
-                                  3)),
-    layers.RandomRotation(0.1),
-    layers.RandomZoom(0.1),
-    layers.RandomContrast(0.1),
-    layers.RandomBrightness(0.1)
-  ]
+    [
+        layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
+        layers.RandomRotation(0.2),
+        layers.RandomZoom(0.2),
+        layers.RandomContrast(0.2),
+        layers.RandomBrightness(0.2)
+    ]
 )
 
-plt.figure(figsize=(10, 10))
-for images, _ in train_ds.take(1):
-  for i in range(9):
-    augmented_images = data_augmentation(images)
-    ax = plt.subplot(3, 3, i + 1)
-    plt.imshow(augmented_images[0].numpy().astype("uint8"))
-    plt.axis("off")
+# Use a Pretrained Model
+base_model = EfficientNetB0(input_shape=(img_height, img_width, 3), include_top=False, weights='imagenet')
+base_model.trainable = False  # Freeze the base model
 
+# Define the model
 num_classes = len(class_names)
 model = Sequential([
-  data_augmentation,
-  layers.Rescaling(1./255),
-  layers.Conv2D(32, 3, padding='same', activation='relu'),
-  layers.MaxPooling2D(),
-  layers.Conv2D(64, 3, padding='same', activation='relu'),
-  layers.MaxPooling2D(),
-  layers.Conv2D(128, 3, padding='same', activation='relu'),
-  layers.MaxPooling2D(),
-  layers.Dropout(0.5),
-  layers.Flatten(),
-  layers.Dense(256, activation='relu'),
-  layers.Dropout(0.5),
-  layers.Dense(num_classes, activation='softmax', name="outputs")
+    data_augmentation,
+    layers.Rescaling(1./255),
+    base_model,
+    layers.GlobalAveragePooling2D(),
+    layers.Dropout(0.5),
+    layers.Dense(num_classes, activation='softmax', name="outputs")  # Use softmax here
 ])
 
-model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),  # Change from_logits to False
               metrics=['accuracy'])
 
 model.summary()
 
-# Learning rate scheduler
-lr_scheduler = keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch / 20))
-
-# Early stopping
+# Callbacks
 early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
+lr_scheduler = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=1e-5)
 
 epochs = 20
 history = model.fit(
-  train_ds,
-  validation_data=val_ds,
-  epochs=epochs,
-  callbacks=[lr_scheduler, early_stopping]
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs,
+    callbacks=[early_stopping, lr_scheduler]
 )
 
 # Define category descriptions
@@ -170,7 +144,7 @@ category_descriptions = {
 # Define the prediction function
 def predict_image(img):
     img = np.array(img)
-    img_resized = tf.image.resize(img, (180, 180))
+    img_resized = tf.image.resize(img, (img_height, img_width))
     img_4d = tf.expand_dims(img_resized, axis=0)
     prediction = model.predict(img_4d)[0]
     predicted_class = np.argmax(prediction)
@@ -182,22 +156,10 @@ 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,
     inputs=image,
     outputs=label,
     title="Welcome to Agricultural Pest Image Classification",
-    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
+    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"
 ).launch(debug=True)