Update app.py

app.py

@@ -8,13 +8,18 @@ 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
 import zipfile
+
 import pathlib
 
+
+
+
+
 # Define the Google Drive shareable link
 gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'
 
@@ -54,77 +59,110 @@ 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)
 
-img_height, img_width = 180, 180
-batch_size = 32
+
+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
 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)
 
-# Enhanced Data Augmentation
+
+import matplotlib.pyplot as plt
+
+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")
+
+
 data_augmentation = keras.Sequential(
-    [
-        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)
-    ]
+  [
+    layers.RandomFlip("horizontal",
+                      input_shape=(img_height,
+                                  img_width,
+                                  3)),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+  ]
 )
 
-# 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
+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")
+
+
 num_classes = len(class_names)
 model = Sequential([
-    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
+  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
 ])
 
-model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),
+model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),  # Change from_logits to False
               metrics=['accuracy'])
 
 model.summary()
 
-# 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
+epochs = 15
 history = model.fit(
-    train_ds,
-    validation_data=val_ds,
-    epochs=epochs,
-    callbacks=[early_stopping, lr_scheduler]
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
 )
 
+
+
 # Define category descriptions
 category_descriptions = {
     "Ants": "Ants are small insects known for their complex social structures and teamwork.",
@@ -144,7 +182,7 @@ category_descriptions = {
 # Define the prediction function
 def predict_image(img):
     img = np.array(img)
-    img_resized = tf.image.resize(img, (img_height, img_width))
+    img_resized = tf.image.resize(img, (180, 180))
     img_4d = tf.expand_dims(img_resized, axis=0)
     prediction = model.predict(img_4d)[0]
     predicted_class = np.argmax(prediction)
@@ -156,10 +194,13 @@ def predict_image(img):
 image = gr.Image()
 label = gr.Label(num_top_classes=1)
 
+
 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"
+    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)
+