diff --git a/Code-Sleep-Python/object_detection.py b/Code-Sleep-Python/object_detection.py
index 328a958..f270443 100644
--- a/Code-Sleep-Python/object_detection.py
+++ b/Code-Sleep-Python/object_detection.py
@@ -12,7 +12,7 @@
 from PIL import Image
 import cv2
 cap = cv2.VideoCapture(0)
-sys.path.insert(0,'/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/')
+sys.path.insert(0, '/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/')
 from utils import label_map_util
 from utils import visualization_utils as vis_util
 MODEL_NAME = 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03'
@@ -27,47 +27,47 @@
 opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
 tar_file = tarfile.open(MODEL_FILE)
 for file in tar_file.getmembers():
-  file_name = os.path.basename(file.name)
-  if 'frozen_inference_graph.pb' in file_name:
-    tar_file.extract(file, os.getcwd())
+    file_name = os.path.basename(file.name)
+    if 'frozen_inference_graph.pb' in file_name:
+        tar_file.extract(file, os.getcwd())
 detection_graph = tf.Graph()
 with detection_graph.as_default():
-  od_graph_def = tf.GraphDef()
-  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
-    serialized_graph = fid.read()
-    od_graph_def.ParseFromString(serialized_graph)
-    tf.import_graph_def(od_graph_def, name='')
+    od_graph_def = tf.GraphDef()
+    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
+        serialized_graph = fid.read()
+        od_graph_def.ParseFromString(serialized_graph)
+        tf.import_graph_def(od_graph_def, name='')
 label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
 categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
 category_index = label_map_util.create_category_index(categories)
 with detection_graph.as_default():
-  with tf.Session(graph=detection_graph) as sess:
-    while True:
-        ret, image_np = cap.read()
-        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
-        image_np_expanded = np.expand_dims(image_np, axis=0)
-        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
-        # Each box represents a part of the image where a particular object was detected.
-        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
-        # Each score represent how level of confidence for each of the objects.
-        # Score is shown on the result image, together with the class label.
-        scores = detection_graph.get_tensor_by_name('detection_scores:0')
-        classes = detection_graph.get_tensor_by_name('detection_classes:0')
-        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
-        # Actual detection.
-        (boxes, scores, classes, num_detections) = sess.run(
-          [boxes, scores, classes, num_detections],
-          feed_dict={image_tensor: image_np_expanded})
-        # Visualization of the results of a detection.
-        vis_util.visualize_boxes_and_labels_on_image_array(
-            image_np,
-            np.squeeze(boxes),
-            np.squeeze(classes).astype(np.int32),
-            np.squeeze(scores),
-            category_index,
-            use_normalized_coordinates=True,
-            line_thickness=8)
-        cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
-        if cv2.waitKey(25) == ord('q'):
-          cv2.destroyAllWindows()
-          break
\ No newline at end of file
+    with tf.Session(graph=detection_graph) as sess:
+        while True:
+            ret, image_np = cap.read()
+            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
+            image_np_expanded = np.expand_dims(image_np, axis=0)
+            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
+            # Each box represents a part of the image where a particular object was detected.
+            boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
+            # Each score represent how level of confidence for each of the objects.
+            # Score is shown on the result image, together with the class label.
+            scores = detection_graph.get_tensor_by_name('detection_scores:0')
+            classes = detection_graph.get_tensor_by_name('detection_classes:0')
+            num_detections = detection_graph.get_tensor_by_name('num_detections:0')
+            # Actual detection.
+            (boxes, scores, classes, num_detections) = sess.run(
+              [boxes, scores, classes, num_detections],
+              feed_dict={image_tensor: image_np_expanded})
+            # Visualization of the results of a detection.
+            vis_util.visualize_boxes_and_labels_on_image_array(
+                image_np,
+                np.squeeze(boxes),
+                np.squeeze(classes).astype(np.int32),
+                np.squeeze(scores),
+                category_index,
+                use_normalized_coordinates=True,
+                line_thickness=8)
+            cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
+            if cv2.waitKey(25) == ord('q'):
+              cv2.destroyAllWindows()
+              break