Update app.py

app.py

@@ -1,24 +1,15 @@
-import gradio as gr
-import matplotlib.pyplot as plt
-import numpy as np
 import os
-import PIL
+import zipfile
+import gdown
+import pathlib
 import tensorflow as tf
-
 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
-
-
-
-
+import matplotlib.pyplot as plt
+import gradio as gr
+import numpy as np
 
 # Define the Google Drive shareable link
 gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'
@@ -48,9 +39,9 @@ except zipfile.BadZipFile:
 os.remove(local_zip_file)
 
 # Convert the extracted directory path to a pathlib.Path object
-data_dir = pathlib.Path(extracted_path)
+data_dir = pathlib.Path('extracted_files/Pest_Dataset')
 
-# Print the directory structure to debug
+# Verify the directory structure
 for root, dirs, files in os.walk(extracted_path):
     level = root.replace(extracted_path, '').count(os.sep)
     indent = ' ' * 4 * (level)
@@ -59,128 +50,151 @@ 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]))
-
+# Set image dimensions and batch size
+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
+# Create training and validation datasets
 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)
 
-
-import matplotlib.pyplot as plt
-
+# 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")
-
+    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.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),
+    ]
 )
 
-
+# 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")
-
+    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([
-  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.Flatten(),
-  layers.Dense(128, activation='relu'),
-  layers.Dense(num_classes, activation='softmax', name="outputs")  # Use softmax here
+    data_augmentation,
+    layers.Rescaling(1./255),
+    
+    layers.Conv2D(32, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
+    layers.BatchNormalization(),
+    layers.MaxPooling2D(),
+    
+    layers.Conv2D(64, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
+    layers.BatchNormalization(),
+    layers.MaxPooling2D(),
+    
+    layers.Conv2D(128, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
+    layers.BatchNormalization(),
+    layers.MaxPooling2D(),
+    
+    layers.Conv2D(256, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
+    layers.BatchNormalization(),
+    layers.MaxPooling2D(),
+    
+    layers.Conv2D(512, 3, padding='same', activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
+    layers.BatchNormalization(),
+    layers.MaxPooling2D(),
+    
+    layers.Dropout(0.5),
+    layers.Flatten(),
+    
+    layers.Dense(256, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
+    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
+model.compile(optimizer=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
 
-epochs = 15
+early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
+
+# Train the model
+epochs = 30
 history = model.fit(
-  train_ds,
-  validation_data=val_ds,
-  epochs=epochs
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs,
+    callbacks=[early_stopping]
 )
 
+# Define category descriptions
+category_descriptions = {
+    "Ants": "Ants are small insects known for their complex social structures and teamwork.",
+    "Bees": "Bees are flying insects known for their role in pollination and producing honey.",
+    "Beetles": "Beetles are a group of insects with hard exoskeletons and wings. They are the largest order of insects.",
+    "Caterpillars": "Caterpillars are the larval stage of butterflies and moths, known for their voracious appetite.",
+    "Earthworms": "Earthworms are segmented worms that are crucial for soil health and nutrient cycling.",
+    "Earwigs": "Earwigs are insects with pincers on their abdomen and are known for their nocturnal activity.",
+    "Grasshoppers": "Grasshoppers are insects known for their powerful hind legs, which they use for jumping.",
+    "Moths": "Moths are nocturnal insects related to butterflies, known for their attraction to light.",
+    "Slugs": "Slugs are soft-bodied mollusks that are similar to snails but lack a shell.",
+    "Snails": "Snails are mollusks with a coiled shell, known for their slow movement and slimy trail.",
+    "Wasps": "Wasps are stinging insects that can be solitary or social, and some species are important pollinators.",
+    "Weevils": "Weevils are a type of beetle with a long snout, known for being pests to crops and stored grains."
+}
 
-
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-
+# Define the prediction function
 def predict_image(img):
     img = np.array(img)
     img_resized = tf.image.resize(img, (180, 180))
     img_4d = tf.expand_dims(img_resized, axis=0)
     prediction = model.predict(img_4d)[0]
-    return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
+    predicted_class = np.argmax(prediction)
+    predicted_label = class_names[predicted_class]
+    predicted_description = category_descriptions[predicted_label]
+    return {predicted_label: f"{float(prediction[predicted_class]):.2f} - {predicted_description}"}
 
+# Set up Gradio interface
 image = gr.Image()
-label = gr.Label(num_top_classes=12)
+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-image: url('extracted_files/Pest_Dataset/bees/bees (444).jpg');
     background-size: cover;
     background-repeat: no-repeat;
     background-attachment: fixed;
@@ -193,7 +207,6 @@ gr.Interface(
     inputs=image,
     outputs=label,
     title="Welcome to Agricultural Pest Image Classification",
-    description="The image data set used was obtaied from Kaggle and has a collection of 12 different types of agricultral pests: Ants, Bees, Beetles, Caterpillars, Earthworms, Earwigs, Grasshoppers, Moths, Slugs, Snails, Wasps, and Weevils",
+    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)
-