diff --git a/Python/similar-images-classification/main.py b/Python/similar-images-classification/main.py
index a0c1406..2cd3eda 100644
--- a/Python/similar-images-classification/main.py
+++ b/Python/similar-images-classification/main.py
@@ -6,40 +6,6 @@ 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")
 
 
@@ -58,35 +24,15 @@ image_paths = [
 ]
 features = []
 
-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)
+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)
-
-        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")
+d = features.shape[1]
+index = faiss.IndexFlatL2(d)
+index.add(features)
 
 
 def find_similar_images(query_image_path, index, k=6):