Update app.py

app.py

@@ -4,98 +4,151 @@ 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
 
-# Download and extract dataset
+# 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}'
+
+# Define the local filename to save the ZIP file
 local_zip_file = 'file.zip'
+
+# Download the ZIP file
 gdown.download(direct_download_url, local_zip_file, quiet=False)
+
+# Directory to extract files
 extracted_path = 'extracted_files'
+
+# Verify if the downloaded file is a ZIP file and extract it
 try:
     with zipfile.ZipFile(local_zip_file, 'r') as zip_ref:
         zip_ref.extractall(extracted_path)
     print("Extraction successful!")
 except zipfile.BadZipFile:
     print("Error: The downloaded file is not a valid ZIP file.")
+
+# Optionally, you can delete the ZIP file after extraction
 os.remove(local_zip_file)
-data_dir = pathlib.Path(extracted_path) / 'Pest_Dataset'
 
-# Data loading and preprocessing
+# 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]))
+
 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)
+
+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
 data_augmentation = keras.Sequential(
-    [
-        layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
-        layers.RandomRotation(0.1),
-        layers.RandomZoom(0.1),
-        layers.RandomBrightness(0.2),
-        layers.RandomContrast(0.2),
-    ]
+  [
+    layers.RandomFlip("horizontal",
+                      input_shape=(img_height,
+                                  img_width,
+                                  3)),
+    layers.RandomRotation(0.1),
+    layers.RandomZoom(0.1),
+    layers.RandomContrast(0.1),
+    layers.RandomBrightness(0.1)
+  ]
 )
 
-# Model definition
+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, 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.Conv2D(128, 3, padding='same', activation='relu'),
-    layers.MaxPooling2D(),
-    layers.Dropout(0.5),
-    layers.Flatten(),
-    layers.Dense(256, activation='relu'),
-    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', name="outputs")
 ])
 
-optimizer = keras.optimizers.Adam(learning_rate=0.001)
-lr_scheduler = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3)
-early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
-
-model.compile(optimizer=optimizer,
+model.compile(optimizer='adam',
               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
               metrics=['accuracy'])
 
 model.summary()
 
-# Train the model
-epochs = 15
+# Learning rate scheduler
+lr_scheduler = keras.callbacks.LearningRateScheduler(lambda epoch: 1e-3 * 10**(epoch / 20))
+
+# Early stopping
+early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
+
+epochs = 20
 history = model.fit(
-    train_ds,
-    validation_data=val_ds,
-    epochs=epochs,
-    callbacks=[lr_scheduler, early_stopping]
+  train_ds,
+  validation_data=val_ds,
+  epochs=epochs,
+  callbacks=[lr_scheduler, early_stopping]
 )
 
 # Define category descriptions
@@ -114,32 +167,37 @@ category_descriptions = {
     "Weevils": "Weevils are a type of beetle with a long snout, known for being pests to crops and stored grains."
 }
 
-# Prediction function
+# Define the prediction function
 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]
-    top_3_indices = prediction.argsort()[-3:][::-1]
-    results = {}
-    for i in top_3_indices:
-        class_name = class_names[i]
-        results[class_name] = f"{float(prediction[i]):.2f} - {category_descriptions[class_name]}"
-    return results
-
-# Gradio interface setup
+    predicted_class = np.argmax(prediction)
+    predicted_label = class_names[predicted_class]
+    predicted_description = category_descriptions[predicted_label]
+    return {predicted_label: f"{float(prediction[predicted_class]):.2f} - {predicted_description}"}
+
+# Set up Gradio interface
 image = gr.Image()
-label = gr.Label(num_top_classes=3)
+label = gr.Label(num_top_classes=1)
+
+# Define custom CSS for background image
 custom_css = """
-body {background-color: #f5f5f5;}
-.gradio-container {border: 1px solid #ccc; border-radius: 10px; padding: 20px;}
+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="Agricultural Pest Image Classification",
-    description="Identify 12 types of agricultural pests from images. This model was trained on a dataset from Kaggle.",
+    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)