Update app.py

app.py

@@ -61,10 +61,6 @@ 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
@@ -90,21 +86,6 @@ val_ds = tf.keras.utils.image_dataset_from_directory(
 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)
@@ -112,12 +93,6 @@ 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(
@@ -128,17 +103,10 @@ data_augmentation = keras.Sequential(
     ]
 )
 
-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")
-
+# Define a deeper convolutional neural network
 model = Sequential([
     data_augmentation,
-    layers.Rescaling(1./255),
+    normalization_layer,
     layers.Conv2D(32, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
     layers.Conv2D(64, 3, padding='same', activation='relu'),
@@ -147,9 +115,14 @@ model = Sequential([
     layers.MaxPooling2D(),
     layers.Conv2D(256, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
-    layers.Dropout(0.2),
+    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(512, activation='relu'),
+    layers.Dense(1024, activation='relu'),
+    layers.Dropout(0.5),
     layers.Dense(num_classes, activation='softmax')
 ])
 
@@ -168,7 +141,7 @@ history = model.fit(
 
 def predict_image(img):
     img = np.array(img)
-    img_resized = tf.image.resize(img, (180, 180))
+    img_resized = tf.image.resize(img, (img_height, img_width))
     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))}
@@ -176,7 +149,6 @@ 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');