diff --git a/Python/similar-images-classification/main.py b/Python/similar-images-classification/main.py
index 2cd3eda..a0c1406 100644
--- a/Python/similar-images-classification/main.py
+++ b/Python/similar-images-classification/main.py
@@ -6,6 +6,40 @@ from tensorflow.keras.applications.vgg19 import preprocess_input
 import faiss
 import cv2
 
+
+def query_yes_no(question, default="yes"):
+    """Ask a yes/no question via raw_input() and return their answer.
+
+    "question" is a string that is presented to the user.
+    "default" is the presumed answer if the user just hits <Enter>.
+        It must be "yes" (the default), "no" or None (meaning
+        an answer is required of the user).
+
+    The "answer" return value is True for "yes" or False for "no".
+    """
+    valid = {"yes": True, "y": True, "ye": True,
+             "no": False, "n": False}
+    if default is None:
+        prompt = " [y/n] "
+    elif default == "yes":
+        prompt = " [Y/n] "
+    elif default == "no":
+        prompt = " [y/N] "
+    else:
+        raise ValueError("invalid default answer: '%s'" % default)
+
+    while True:
+        sys.stdout.write(question + prompt)
+        choice = input().lower()
+        if default is not None and choice == '':
+            return valid[default]
+        elif choice in valid:
+            return valid[choice]
+        else:
+            sys.stdout.write("Please respond with 'yes' or 'no' "
+                             "(or 'y' or 'n').\n")
+
+
 model = vgg19.VGG19(weights="imagenet", include_top=False, pooling="avg")
 
 
@@ -24,15 +58,35 @@ image_paths = [
 ]
 features = []
 
-for image_path in image_paths:
-    img_feature = extract_features(image_path, model)
-    features.append(img_feature)
+if os.path.exists("image_index.bin"):
+    if query_yes_no("Load the index?", default="yes"):
+        index = faiss.read_index("image_index.bin")
+    else:
+        for image_path in image_paths:
+            img_feature = extract_features(image_path, model)
+            features.append(img_feature)
 
-features = np.array(features)
+        features = np.array(features)
 
-d = features.shape[1]
-index = faiss.IndexFlatL2(d)
-index.add(features)
+        d = features.shape[1]
+        index = faiss.IndexFlatL2(d)
+        index.add(features)
+
+        if query_yes_no("Save the index?", default="yes"):
+            faiss.write_index(index, "image_index.bin")
+else:
+    for image_path in image_paths:
+        img_feature = extract_features(image_path, model)
+        features.append(img_feature)
+
+    features = np.array(features)
+
+    d = features.shape[1]
+    index = faiss.IndexFlatL2(d)
+    index.add(features)
+
+    if query_yes_no("Save the index?", default="yes"):
+        faiss.write_index(index, "image_index.bin")
 
 
 def find_similar_images(query_image_path, index, k=6):