Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
@@ -10,19 +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'
 
@@ -62,109 +53,76 @@ 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
 
-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)
 
-
-
-
-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")
+
+# Define data augmentation
+data_augmentation = keras.Sequential([
+    layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+])
 
 num_classes = 12
 
 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(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")
 ])
 
-
-
-
-
 model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
 
-
-
-
-epochs=10
+epochs = 10
 history = model.fit(
-  train_ds,
-  validation_data=val_ds,
-  epochs=epochs
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs
 )
 
-
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-
 def predict_image(img):
     img = np.array(img)
     img_resized = tf.image.resize(img, (180, 180))
@@ -175,13 +133,10 @@ def predict_image(img):
 image = gr.Image()
 label = gr.Label(num_top_classes=5)
 
-
-
 gr.Interface(
     fn=predict_image, 
     inputs=image, 
     outputs=label, 
     title="Pest Classification",
-    description="Upload an image of a pest to classify it into one of the predefined categories.",
-    css=custom_css
-).launch(debug=True)
+    description="Upload an image of a pest to classify it into one of the predefined categories."
+).launch(debug=True)