diff --git a/backend/video_capture.py b/backend/video_capture.py
index 6de64c677002651378f6f13298137373ba765fca..a0d537ce4cc5fd2bd029e64f19d57d8a8bdc298b 100644
--- a/backend/video_capture.py
+++ b/backend/video_capture.py
@@ -10,7 +10,7 @@ from cameras import restaurants
 from db import models
 from db.database import SessionLocal
 from routers.websocket import manager
-    
+
 
 async def handle_cameras():
     model = keras.models.load_model('assets', compile=False)
@@ -54,12 +54,12 @@ async def handle_cameras():
                                     np.array(
                                         [treated_img]))),
                             axis=0)
-                        pre_pred=time.time()
+                        pre_pred = time.time()
                         pred_map = np.squeeze(model.predict(input_image, verbose=0))
-                        print(time.time()-pre_pred)
+                        print(time.time() - pre_pred)
                         count_prediction += np.sum(pred_map)
                         for caisse in camera["caisses"]:
-                            if np.sum(pred_map[caisse["x1"]//2:caisse["x2"]//2, caisse["y1"]//2:caisse["y2"]//2]) > 0.5:
+                            if np.sum(pred_map[caisse["x1"] // 2:caisse["x2"] // 2, caisse["y1"] // 2:caisse["y2"] // 2]) > 0.5:
                                 open_checkouts += 1
                     else:
                         cams_working = False
@@ -69,7 +69,7 @@ async def handle_cameras():
                     waiting_time = timedelta(
                         seconds=restaurant['b_factor'] +
                         int(count_prediction *
-                        restaurant['a_factor'] / max(open_checkouts, 1)))
+                            restaurant['a_factor'] / max(open_checkouts, 1)))
                     db_record = models.Records(
                         place=restaurant['restaurant'],
                         date=current_date,