Update app.py

app.py

@@ -9,11 +9,14 @@ 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'
 
@@ -53,98 +56,161 @@ for root, dirs, files in os.walk(extracted_path):
     for f in files:
         print(f"{subindent}{f}")
 
+
 # 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]))
+
+
 bees = list(data_dir.glob('bees/*'))
 print(bees[0])
 PIL.Image.open(str(bees[0]))
 
+
+
 batch_size = 32
 img_height = 180
 img_width = 180
 
+
 train_ds = tf.keras.utils.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.utils.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
+
+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 image_batch, labels_batch in train_ds:
+  print(image_batch.shape)
+  print(labels_batch.shape)
+  break
+
+
 AUTOTUNE = tf.data.AUTOTUNE
 
 train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
 val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
 
+
 normalization_layer = layers.Rescaling(1./255)
 
+
+
+
+
+
+normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
+image_batch, labels_batch = next(iter(normalized_ds))
+first_image = image_batch[0]
+# Notice the pixel values are now in `[0,1]`.
+print(np.min(first_image), np.max(first_image))
+
+
+
+
+
+
+
 num_classes = len(class_names)
 
+
+
+
 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(0.1),
+    layers.RandomZoom(0.1),
+  ]
 )
 
-# Define a deeper convolutional neural network
+
+
+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")
+
+
+
+
 model = Sequential([
-    data_augmentation,
-    normalization_layer,
-    layers.Conv2D(32, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 3)),
-    layers.MaxPooling2D(),
-    layers.Conv2D(64, 3, padding='same', activation='relu'),
-    layers.MaxPooling2D(),
-    layers.Conv2D(128, 3, padding='same', activation='relu'),
-    layers.MaxPooling2D(),
-    layers.Conv2D(256, 3, padding='same', activation='relu'),
-    layers.MaxPooling2D(),
-    layers.Conv2D(512, 3, padding='same', activation='relu'),
-    layers.MaxPooling2D(),
-    layers.Conv2D(512, 3, padding='same', activation='relu'),
-    layers.MaxPooling2D(),
-    layers.Dropout(0.5),
-    layers.Flatten(),
-    layers.Dense(1024, activation='relu'),
-    layers.Dropout(0.5),
-    layers.Dense(num_classes, activation='softmax')
+  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, name="outputs")
 ])
 
-# Ensure the input shape is correctly passed
-model.build((None, img_height, img_width, 3))
 
-model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
+
+
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
 
+
 model.summary()
 
+
+
 epochs = 15
 history = model.fit(
-    train_ds,
-    validation_data=val_ds,
-    epochs=epochs
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
 )
 
+
+
 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]
     return {class_names[i]: float(prediction[i]) for i in range(len(class_names))}
@@ -152,6 +218,7 @@ 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');
@@ -170,3 +237,10 @@ 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)
+
+
+
+
+
+
+