Update app.py

app.py

@@ -8,15 +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'
 
@@ -56,28 +52,18 @@ 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,
@@ -86,7 +72,6 @@ train_ds = tf.keras.utils.image_dataset_from_directory(
   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,
@@ -95,58 +80,71 @@ val_ds = tf.keras.utils.image_dataset_from_directory(
   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 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)
 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)
 
+model = Sequential([
+  layers.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
+  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.Flatten(),
+  layers.Dense(128, activation='relu'),
+  layers.Dense(num_classes)
+])
 
+model.compile(optimizer='adam',
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              metrics=['accuracy'])
 
+model.summary()
 
+epochs=10
+history = model.fit(
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs
+)
 
+acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
 
-num_classes = len(class_names)
+loss = history.history['loss']
+val_loss = history.history['val_loss']
 
+epochs_range = range(epochs)
 
+plt.figure(figsize=(8, 8))
+plt.subplot(1, 2, 1)
+plt.plot(epochs_range, acc, label='Training Accuracy')
+plt.plot(epochs_range, val_acc, label='Validation Accuracy')
+plt.legend(loc='lower right')
+plt.title('Training and Validation Accuracy')
 
+plt.subplot(1, 2, 2)
+plt.plot(epochs_range, loss, label='Training Loss')
+plt.plot(epochs_range, val_loss, label='Validation Loss')
+plt.legend(loc='upper right')
+plt.title('Training and Validation Loss')
+plt.show()
 
 data_augmentation = keras.Sequential(
   [
@@ -159,8 +157,6 @@ data_augmentation = keras.Sequential(
   ]
 )
 
-
-
 plt.figure(figsize=(10, 10))
 for images, _ in train_ds.take(1):
   for i in range(9):
@@ -169,9 +165,6 @@ for images, _ in train_ds.take(1):
     plt.imshow(augmented_images[0].numpy().astype("uint8"))
     plt.axis("off")
 
-
-
-
 model = Sequential([
   data_augmentation,
   layers.Rescaling(1./255),
@@ -187,18 +180,12 @@ model = Sequential([
   layers.Dense(num_classes, name="outputs")
 ])
 
-
-
-
 model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
 
-
 model.summary()
 
-
-
 epochs = 15
 history = model.fit(
   train_ds,
@@ -206,41 +193,17 @@ history = model.fit(
   epochs=epochs
 )
 
-
-
 def predict_image(img):
     img = np.array(img)
     img_resized = tf.image.resize(img, (180, 180))
     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))}
+    probabilities = tf.nn.softmax(prediction).numpy()
+    class_probabilities = {class_names[i]: probabilities[i] * 100 for i in range(len(class_names))}
+    return class_probabilities
 
 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');
-    background-size: cover;
-    background-repeat: no-repeat;
-    background-attachment: fixed;
-    color: white;
-}
-"""
-
-gr.Interface(
-    fn=predict_image,
-    inputs=image,
-    outputs=label,
-    title="Welcome to Agricultural Pest Image Classification",
-    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)
-
-
-
-
-
-
-