Update app.py

app.py

@@ -8,18 +8,11 @@ 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
 
-
-
-
-
 # Define the Google Drive shareable link
 gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'
 
@@ -59,92 +52,55 @@ 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]))
-
 
-bees = list(data_dir.glob('bees/*'))
-print(bees[0])
-PIL.Image.open(str(bees[0]))
+img_height, img_width = 180, 180
+batch_size = 32
 
-
-img_height,img_width=180,180
-batch_size=32
+# Load 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)
-
-
-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")
-
+    data_dir,
+    validation_split=0.2,
+    subset="validation",
+    seed=123,
+    image_size=(img_height, img_width),
+    batch_size=batch_size
+)
 
+# Define data augmentation layers
 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")
-
-
-num_classes = len(class_names)
+# Define the model
+num_classes = len(train_ds.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(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='adam',
@@ -153,14 +109,30 @@ model.compile(optimizer='adam',
 
 model.summary()
 
-
+# Train the model
 epochs = 15
 history = model.fit(
-  train_ds,
-  validation_data=val_ds,
-  epochs=epochs
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs
 )
 
+# Plot training history
+plt.plot(history.history['accuracy'], label='accuracy')
+plt.plot(history.history['val_accuracy'], label='val_accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.show()
+
+plt.plot(history.history['loss'], label='loss')
+plt.plot(history.history['val_loss'], label='val_loss')
+plt.xlabel('Epoch')
+plt.ylabel('Loss')
+plt.legend()
+plt.show()
+
+
 
 
 # Define category descriptions