Update app.py

app.py

@@ -1,4 +1,3 @@
-
 import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
@@ -15,8 +14,6 @@ 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'
 
@@ -55,114 +52,92 @@ for root, dirs, files in os.walk(extracted_path):
     subindent = ' ' * 4 * (level + 1)
     for f in files:
         print(f"{subindent}{f}")
-        
 
-# Path to the dataset directory
-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]))
+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),
+    layers.RandomContrast(0.1),
+])
 
-num_classes = 12
+# Load a pretrained model and fine-tune it
+base_model = tf.keras.applications.MobileNetV2(input_shape=(img_height, img_width, 3),
+                                               include_top=False,
+                                               weights='imagenet')
+base_model.trainable = False  # Freeze the base model
 
+# Add custom layers on top of the pretrained model
 model = Sequential([
-  layers.experimental.preprocessing.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,activation='softmax')
+    data_augmentation,
+    layers.Rescaling(1./255),
+    base_model,
+    layers.GlobalAveragePooling2D(),
+    layers.Dropout(0.2),
+    layers.Dense(128, activation='relu'),
+    layers.Dense(len(class_names), 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))
+    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))}
 
-
-    image = gr.Image()
-label = gr.Label(num_top_classes=5)
-
-# 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;
-}
-"""
+image = gr.Image()
+label = gr.Label(num_top_classes=12)
 
 gr.Interface(
-    fn=predict_image,
-    inputs=image,
-    outputs=label,
+    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)