Update app.py

app.py

@@ -3,10 +3,12 @@ import zipfile
 import gdown
 import pathlib
 import tensorflow as tf
-from tensorflow import keras
+from tensorflow.keras.preprocessing import image_dataset_from_directory
+
 from tensorflow.keras import layers
 from tensorflow.keras.models import Sequential
-import matplotlib.pyplot as plt
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization, Rescaling
+from tensorflow.keras.callbacks import EarlyStopping, LearningRateScheduler
 import gradio as gr
 import numpy as np
 
@@ -40,21 +42,14 @@ os.remove(local_zip_file)
 # Convert the extracted directory path to a pathlib.Path object
 data_dir = pathlib.Path('extracted_files/Pest_Dataset')
 
-# Verify the directory structure
-for root, dirs, files in os.walk(extracted_path):
-    level = root.replace(extracted_path, '').count(os.sep)
-    indent = ' ' * 4 * (level)
-    print(f"{indent}{os.path.basename(root)}/")
-    subindent = ' ' * 4 * (level + 1)
-    for f in files:
-        print(f"{subindent}{f}")
+
 
 # Set image dimensions and batch size
 img_height, img_width = 180, 180
 batch_size = 32
 
 # Create training and validation datasets
-train_ds = tf.keras.preprocessing.image_dataset_from_directory(
+train_ds = image_dataset_from_directory(
     data_dir,
     validation_split=0.2,
     subset="training",
@@ -63,7 +58,7 @@ train_ds = tf.keras.preprocessing.image_dataset_from_directory(
     batch_size=batch_size
 )
 
-val_ds = tf.keras.preprocessing.image_dataset_from_directory(
+val_ds = image_dataset_from_directory(
     data_dir,
     validation_split=0.2,
     subset="validation",
@@ -75,17 +70,12 @@ val_ds = tf.keras.preprocessing.image_dataset_from_directory(
 class_names = train_ds.class_names
 print(class_names)
 
-# Display some sample images
-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(
+
+
+
+
+
+data_augmentation = tf.keras.Sequential(
     [
         layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
         layers.RandomRotation(0.2),
@@ -95,59 +85,52 @@ data_augmentation = keras.Sequential(
     ]
 )
 
-# Display augmented images
-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")
 
-# Define a deeper CNN model with more regularization techniques
+
+
 num_classes = len(class_names)
 model = Sequential()
 
 model.add(data_augmentation)
-model.add(layers.Rescaling(1./255))
+model.add(Rescaling(1./255))
 
-model.add(layers.Conv2D(32, 3, padding='same', activation='relu'))
-model.add(layers.BatchNormalization())
-model.add(layers.MaxPooling2D())
+model.add(Conv2D(32, 3, padding='same', activation='relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D())
 
-model.add(layers.Conv2D(64, 3, padding='same', activation='relu'))
-model.add(layers.BatchNormalization())
-model.add(layers.MaxPooling2D())
+model.add(Conv2D(64, 3, padding='same', activation='relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D())
 
-model.add(layers.Conv2D(128, 3, padding='same', activation='relu'))
-model.add(layers.BatchNormalization())
-model.add(layers.MaxPooling2D())
+model.add(Conv2D(128, 3, padding='same', activation='relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D())
 
-model.add(layers.Conv2D(256, 3, padding='same', activation='relu'))
-model.add(layers.BatchNormalization())
-model.add(layers.MaxPooling2D())
+model.add(Conv2D(256, 3, padding='same', activation='relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D())
 
-model.add(layers.Conv2D(512, 3, padding='same', activation='relu'))
-model.add(layers.BatchNormalization())
-model.add(layers.MaxPooling2D())
+model.add(Conv2D(512, 3, padding='same', activation='relu'))
+model.add(BatchNormalization())
+model.add(MaxPooling2D())
 
-model.add(layers.Dropout(0.5))
-model.add(layers.Flatten())
+model.add(Dropout(0.5))
+model.add(Flatten())
 
-model.add(layers.Dense(256, activation='relu'))
-model.add(layers.Dropout(0.5))
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(0.5))
 
-model.add(layers.Dense(num_classes, activation='softmax', name="outputs"))
+model.add(Dense(num_classes, activation='softmax', name="outputs"))
 
-model.compile(optimizer=keras.optimizers.Adam(learning_rate=1e-4),
+model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
               metrics=['accuracy'])
 
 model.summary()
 
-# Implement early stopping
-from tensorflow.keras.callbacks import EarlyStopping
 
+
+# Implement early stopping
 early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
 
 # Learning rate scheduler
@@ -157,7 +140,7 @@ def scheduler(epoch, lr):
     else:
         return lr * tf.math.exp(-0.1)
 
-lr_scheduler = keras.callbacks.LearningRateScheduler(scheduler)
+lr_scheduler = LearningRateScheduler(scheduler)
 
 # Train the model
 epochs = 30
@@ -168,17 +151,17 @@ history = model.fit(
     callbacks=[early_stopping, lr_scheduler]
 )
 
-# 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)
     predicted_label = class_names[predicted_class]
     return {predicted_label: f"{float(prediction[predicted_class]):.2f}"}
 
-# Set up Gradio interface
 image = gr.Image()
 label = gr.Label(num_top_classes=1)
 
@@ -201,3 +184,5 @@ 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)
+
+