predict.py

import sys
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import matplotlib.pyplot as plt
from lime import lime_image
from skimage.segmentation import mark_boundaries

def explain_instance(image_path, model, num_features=5, num_samples=1000):
    img = load_img(image_path, target_size=image_size)
    img_array = img_to_array(img) / 255
    explanation = explainer.explain_instance(img_array, model.predict, top_labels=num_classes, hide_color=0,
                                             num_samples=num_samples, num_features=num_features)
    return explanation

if __name__ == "__main__":
    if len(sys.argv) != 2:
        print("Usage: predict.py image_path")
        sys.exit(1)

    image_path = sys.argv[1]
    image_size = (200, 200)
    model_path = "malware_classifier_lime.h5"
    model = load_model(model_path)
    num_classes = 119

    explainer = lime_image.LimeImageExplainer()
    explanation = explain_instance(image_path, model)

    temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=False)
    img = load_img(image_path, target_size=image_size)
    img_array = img_to_array(img) / 255

    # Display the original image
    plt.figure(figsize=(10, 5))
    plt.subplot(1, 2, 1)
    plt.imshow(img_array)
    plt.title("Original Image")
    plt.axis("off")

    # Display the LIME explanation
    plt.subplot(1, 2, 2)
    plt.imshow(mark_boundaries(temp, mask))
    plt.title("LIME Explanation")
    plt.axis("off")

    plt.show()

    # Make a prediction
    img = load_img(image_path, target_size=image_size)
    img_array = img_to_array(img) / 255
    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)
    predicted_class = np.argmax(prediction)

    # Get the class name
    class_name = list(train_generator.class_indices.keys())[list(train_generator.class_indices.values()).index(predicted_class)]

    print(f"Predicted class: {predicted_class}, Class name: {class_name}")