diff --git a/predict.py b/predict.py
index ee1b1ee8..cff01c3a 100644
--- a/predict.py
+++ b/predict.py
@@ -13,15 +13,15 @@
 import argparse
 
 def rect_to_bb(rect):
-	# take a bounding predicted by dlib and convert it
-	# to the format (x, y, w, h) as we would normally do
-	# with OpenCV
-	x = rect.left()
-	y = rect.top()
-	w = rect.right() - x
-	h = rect.bottom() - y
-	# return a tuple of (x, y, w, h)
-	return (x, y, w, h)
+    # take a bounding predicted by dlib and convert it
+    # to the format (x, y, w, h) as we would normally do
+    # with OpenCV
+    x = rect.left()
+    y = rect.top()
+    w = rect.right() - x
+    h = rect.bottom() - y
+    # return a tuple of (x, y, w, h)
+    return (x, y, w, h)
 
 def detect_face(image_paths,  SAVE_DETECTED_AT, default_max_size=800,size = 300, padding = 0.25):
     cnn_face_detector = dlib.cnn_face_detection_model_v1('dlib_models/mmod_human_face_detector.dat')
@@ -59,17 +59,17 @@ def detect_face(image_paths,  SAVE_DETECTED_AT, default_max_size=800,size = 300,
 
 def predidct_age_gender_race(save_prediction_at, imgs_path = 'cropped_faces/'):
     img_names = [os.path.join(imgs_path, x) for x in os.listdir(imgs_path)]
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device('cpu')
 
     model_fair_7 = torchvision.models.resnet34(pretrained=True)
     model_fair_7.fc = nn.Linear(model_fair_7.fc.in_features, 18)
-    model_fair_7.load_state_dict(torch.load('fair_face_models/fairface_alldata_20191111.pt'))
+    model_fair_7.load_state_dict(torch.load('fair_face_models/res34_fair_align_multi_7_20190809.pt', map_location=torch.device('cpu')))
     model_fair_7 = model_fair_7.to(device)
     model_fair_7.eval()
 
     model_fair_4 = torchvision.models.resnet34(pretrained=True)
     model_fair_4.fc = nn.Linear(model_fair_4.fc.in_features, 18)
-    model_fair_4.load_state_dict(torch.load('fair_face_models/fairface_alldata_4race_20191111.pt'))
+    model_fair_4.load_state_dict(torch.load('fair_face_models/fairface_alldata_4race_20191111.pt', map_location=torch.device('cpu')))
     model_fair_4 = model_fair_4.to(device)
     model_fair_4.eval()
 
@@ -206,7 +206,7 @@ def ensure_dir(directory):
     parser = argparse.ArgumentParser()
     parser.add_argument('--csv', dest='input_csv', action='store',
                         help='csv file of image path where col name for image path is "img_path')
-    dlib.DLIB_USE_CUDA = True
+    dlib.DLIB_USE_CUDA = False
     print("using CUDA?: %s" % dlib.DLIB_USE_CUDA)
     args = parser.parse_args()
     SAVE_DETECTED_AT = "detected_faces"