Update app.py

app.py

@@ -1,4 +1,3 @@
-!pip install gradio
 import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
@@ -10,14 +9,11 @@ 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'
 
@@ -57,158 +53,119 @@ 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 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
-
+    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),
+    ]
 )
 
-
-
 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")
 
 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, name="outputs")
+    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.Conv2D(256, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Dropout(0.2),
+    layers.Flatten(),
+    layers.Dense(512, activation='relu'),
+    layers.Dense(num_classes, activation='softmax')
 ])
 
-
-
-
-model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
               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, (180, 180))
@@ -238,10 +195,3 @@ 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)
-
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-
-