Update app.py

app.py

@@ -4,22 +4,13 @@ import numpy as np
 import os
 import PIL
 import tensorflow as tf
-
 from tensorflow import keras
-from tensorflow.keras import layers
-from tensorflow.keras.models import Sequential
-
-
+from tensorflow.keras import layers, 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'
 
@@ -50,102 +41,49 @@ os.remove(local_zip_file)
 # Convert the extracted directory path to a pathlib.Path object
 data_dir = pathlib.Path(extracted_path)
 
-# Print the directory structure to debug
-for root, dirs, files in os.walk(extracted_path):
-    level = root.replace(extracted_path, '').count(os.sep)
-    indent = ' ' * 4 * (level)
-    print(f"{indent}{os.path.basename(root)}/")
-    subindent = ' ' * 4 * (level + 1)
-    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]))
-
+data_dir = data_dir / 'Pest_Dataset'
 
-img_height,img_width=180,180
-batch_size=32
+# Load dataset
 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=(180, 180),
+    batch_size=32)
 
 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=(180, 180),
+    batch_size=32)
 
 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_augmentation = keras.Sequential(
-  [
-    layers.RandomFlip("horizontal",
-                      input_shape=(img_height,
-                                  img_width,
-                                  3)),
+data_augmentation = Sequential([
+    layers.RandomFlip("horizontal"),
     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)
 model = Sequential([
-  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.Dropout(0.5),  # Adding dropout regularization
-  layers.Flatten(),
-  layers.Dense(256, activation='relu'),
-  layers.Dropout(0.5),  # Adding dropout regularization
-  layers.Dense(num_classes, activation='softmax', 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.Dropout(0.5),
+    layers.Flatten(),
+    layers.Dense(256, activation='relu'),
+    layers.Dropout(0.5),
+    layers.Dense(num_classes, activation='softmax')
 ])
 
 model.compile(optimizer='adam',
@@ -154,16 +92,13 @@ model.compile(optimizer='adam',
 
 model.summary()
 
-
 epochs = 15
 history = model.fit(
-  train_ds,
-  validation_data=val_ds,
-  epochs=epochs
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs
 )
 
-
-
 # Define category descriptions
 category_descriptions = {
     "Ants": "Ants are small insects known for their complex social structures and teamwork.",
@@ -214,4 +149,3 @@ 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)
-