app.py

import gradio as gr
import matplotlib.pyplot as plt
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
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'

# 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)

# 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}")

# Path to the dataset directory
data_dir = pathlib.Path('extracted_files/Pest_Dataset')

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

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,
  subset="training",
  seed=123,
  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,
  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")

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))

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),
  ]
)

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")

from tensorflow.keras.applications import EfficientNetB0

base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))

# Freeze the pre-trained weights
base_model.trainable = False

# Create new model on top
inputs = keras.Input(shape=(img_height, img_width, 3))
x = data_augmentation(inputs)  # Apply data augmentation
x = base_model(x, training=False)
x = keras.layers.GlobalAveragePooling2D()(x)
x = keras.layers.Dropout(0.2)(x)
outputs = keras.layers.Dense(len(class_names), activation='softmax')(x)

# Compile the model
model = keras.Model(inputs, outputs)
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.summary()

# Train the model
epochs = 10
history = model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)


# Plot training history
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

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()

test_ds = tf.keras.utils.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

results = model.evaluate(test_ds, verbose=0)

print("    Test Loss: {:.5f}".format(results[0]))
print("Test Accuracy: {:.2f}%".format(results[1] * 100))


# Metrics
y_true = []
y_pred = []

for images, labels in test_ds:
    y_true.extend(labels.numpy())
    preds = model.predict(images)
    y_pred.extend(np.argmax(preds, axis=1))

from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(y_true, y_pred, target_names=class_names))

import pandas as pd
report = classification_report(y_true, y_pred, target_names=class_names, output_dict=True)
df = pd.DataFrame(report).transpose()
print(df)

def make_confusion_matrix(y_true, y_pred, labels):
    cm = confusion_matrix(y_true, y_pred)
    fig, ax = plt.subplots(figsize=(10, 8))
    cax = ax.matshow(cm, cmap=plt.cm.Blues)
    plt.title('Confusion Matrix')
    fig.colorbar(cax)
    ax.set_xticklabels([''] + labels, rotation=90)
    ax.set_yticklabels([''] + labels)
    plt.xlabel('Predicted')
    plt.ylabel('True')
    plt.show()

make_confusion_matrix(y_true, y_pred, class_names)


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))}

image = gr.Image(type="pil")
label = gr.Label(num_top_classes=12)

# 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="PestScout: An Agricultural Pest Image Classification System Using Convolutional Neural Networks",
    description="Upload an image of a pest to classify it into one of the predefined categories.",
    css=custom_css
).launch(debug=True)