diff --git a/GUI.py b/GUI.py
index f7bf6c9..9554b72 100644
--- a/GUI.py
+++ b/GUI.py
@@ -111,9 +111,7 @@ def update(self):
                     words.append((x, y, w, h, text))
             if words:
                 sentences = group_words_to_sentences(words)
-                ss = ""
-                for sentence in sentences:
-                    ss += sentence + "\n"
+                ss = "".join(sentence + "\n" for sentence in sentences)
                 if prev_ss != ss:
                     self.text_label.delete(1.0, tk.END)
                     self.text_label.insert(tk.END, ss)
@@ -165,16 +163,14 @@ def save_face(self):
         if name == "":
             tk.messagebox.showerror("Error", "Please enter a name.")
             return
-        filename = os.path.join("./known_faces/", name + ".jpg")
+        filename = os.path.join("./known_faces/", f"{name}.jpg")
         if os.path.exists(filename):
             return tk.messagebox.showerror(
                 "Error", "A face with this name already exists."
             )
-        # Show a pop-up message
-        resp = tk.messagebox.askokcancel(
+        if resp := tk.messagebox.askokcancel(
             "Cheese", "Smile for the camera! Click OK to click and image."
-        )
-        if resp:
+        ):
             ret, frame = self.cap.read()
             if ret:
                 cv2.imwrite(filename, frame)
diff --git a/detectors/face.py b/detectors/face.py
index 0b3d425..da59435 100644
--- a/detectors/face.py
+++ b/detectors/face.py
@@ -10,10 +10,10 @@
 faces_folder_path = "./known_faces/"
 dlib_frontal_face_detector = dlib.get_frontal_face_detector()
 shape_predictor = dlib.shape_predictor(
-    data_dir + "/shape_predictor_68_face_landmarks.dat"
+    f"{data_dir}/shape_predictor_68_face_landmarks.dat"
 )
 face_recognition_model = dlib.face_recognition_model_v1(
-    data_dir + "/dlib_face_recognition_resnet_model_v1.dat"
+    f"{data_dir}/dlib_face_recognition_resnet_model_v1.dat"
 )
 face_detector = dlib.get_frontal_face_detector()
 
@@ -40,7 +40,7 @@ def find_match(known_faces, person_names, face):
     if min_value < 0.58:
         return person_names[min_index] + " ({0:.2f})".format(min_value)
     if min_value < 0.65:
-        return person_names[min_index] + "?" + " ({0:.2f})".format(min_value)
+        return f"{person_names[min_index]}?" + " ({0:.2f})".format(min_value)
     return "Not Found"
 
 
@@ -77,7 +77,7 @@ def load_face_encodings(faces_folder_path):
 
 def recognize_faces_in_frame(frame):
     faceClassifier = cv2.CascadeClassifier(
-        data_dir + "/haarcascade_frontalface_default.xml"
+        f"{data_dir}/haarcascade_frontalface_default.xml"
     )
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
     face_rects = faceClassifier.detectMultiScale(
@@ -89,8 +89,7 @@ def recognize_faces_in_frame(frame):
     )
     for x, y, w, h in face_rects:
         cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
-        face_encodings_in_image = get_face_encodings(frame)
-        if face_encodings_in_image:
+        if face_encodings_in_image := get_face_encodings(frame):
             match = find_match(face_encodings, person_names, face_encodings_in_image[0])
             cv2.putText(
                 frame,
diff --git a/detectors/traffic.py b/detectors/traffic.py
index e608aa3..5de78ff 100644
--- a/detectors/traffic.py
+++ b/detectors/traffic.py
@@ -52,10 +52,9 @@
 
 async def image_processing(img):
     model = load_model("./models/TSR.h5")
-    data = []
     image = Image.fromarray(img)
     image = image.resize((30, 30))
-    data.append(np.array(image))
+    data = [np.array(image)]
     X_test = np.array(data)
     Y_pred = np.argmax(model.predict(X_test))
     try:
diff --git a/detectors/utils.py b/detectors/utils.py
index 60f82ec..188b899 100644
--- a/detectors/utils.py
+++ b/detectors/utils.py
@@ -50,9 +50,7 @@ def group_words_to_sentences(words):
 
     # Convert the words to strings
     sentences = [[w[4] for w in s] for s in sentences]
-    sentences = [" ".join(s) for s in sentences]
-
-    return sentences
+    return [" ".join(s) for s in sentences]
 
 
 def wrap(func):