Update app.py

app.py

@@ -4,24 +4,14 @@ 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 PIL import Image
 import gdown
 import zipfile
-
 import pathlib
-
-
+from tensorflow import keras
+from tensorflow.keras import layers, callbacks
 
 # Define the Google Drive shareable link
 gdrive_url = 'https://drive.google.com/file/d/1HjHYlQyRz5oWt8kehkt1TiOGRRlKFsv8/view?usp=drive_link'
-
-# Extract the file ID from the URL
 file_id = gdrive_url.split('/d/')[1].split('/view')[0]
 direct_download_url = f'https://drive.google.com/uc?id={file_id}'
 
@@ -46,137 +36,115 @@ except zipfile.BadZipFile:
 os.remove(local_zip_file)
 
 # Convert the extracted directory path to a pathlib.Path object
-data_dir = pathlib.Path(extracted_path)
+data_dir = pathlib.Path(extracted_path) / 'Pest_Dataset'
 
-# 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}")
+# Load and preprocess data
+img_height, img_width = 180, 180
+batch_size = 32
 
-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
 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
 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
+data_augmentation = keras.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")
-
+])
 
+# Model
 num_classes = len(class_names)
 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")
 ])
 
-
+# Compile the model
 model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
 
+# Early stopping callback
+early_stopping = callbacks.EarlyStopping(
+    monitor='val_loss', patience=5, restore_best_weights=True
+)
 
-model.summary()
-
+# Train the model
 epochs = 50
 history = model.fit(
-  train_ds,
-  validation_data=val_ds,
-  epochs=epochs
+    train_ds,
+    validation_data=val_ds,
+    epochs=epochs,
+    callbacks=[early_stopping]
 )
 
+# Evaluate the model on validation data
 results = model.evaluate(val_ds, verbose=0)
-
 print("Validation Loss: {:.5f}".format(results[0]))
 print("Validation Accuracy: {:.2f}%".format(results[1] * 100))
 
-import gradio as gr
-import numpy as np
-import tensorflow as tf
-
+# Plot training history
+plt.figure(figsize=(12, 6))
+plt.subplot(1, 2, 1)
+plt.plot(history.history['loss'], label='Training Loss')
+plt.plot(history.history['val_loss'], label='Validation Loss')
+plt.xlabel('Epoch')
+plt.ylabel('Loss')
+plt.legend()
+plt.title('Training and Validation Loss')
+
+plt.subplot(1, 2, 2)
+plt.plot(history.history['accuracy'], label='Training Accuracy')
+plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
+plt.xlabel('Epoch')
+plt.ylabel('Accuracy')
+plt.legend()
+plt.title('Training and Validation Accuracy')
+plt.show()
+
+# Prediction function
 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))}
-
-
-
+    return {class_names[i]: float(prediction[i]) for i in range(num_classes)}
 
+# Interface
 image = gr.Image()
-label = gr.Label(num_top_classes=12)
+label = gr.Label(num_top_classes=num_classes)
 
-# Define custom CSS for background image
 custom_css = """
 body {
-    background-image: url('\extracted_files\Pest_Dataset\bees\bees (444).jpg');
+    background-image: url('extracted_files/Pest_Dataset/bees/bees (444).jpg');
     background-size: cover;
     background-repeat: no-repeat;
     background-attachment: fixed;
@@ -192,7 +160,3 @@ gr.Interface(
     description="Upload an image of a pest to classify it into one of the predefined categories.",
     css=custom_css
 ).launch(debug=True)
-
-
-
-