update app.py # 5

app.py

@@ -27,15 +27,15 @@ def predict(input_file, downsample_size):
     downsample_size = int(downsample_size)
     base_directory = os.getcwd()
     selected_directory = os.path.join(base_directory, "selected_images")
-    if os.path.isdir(selected_directory):
-        shutil.rmtree(selected_directory)
-    os.mkdir(selected_directory)
-
+    # if os.path.isdir(selected_directory):
+    #     shutil.rmtree(selected_directory)
+    # os.mkdir(selected_directory)
+    #
     file_name = (input_file.split('/')[-1]).split('.')[-1]
     zip_path = os.path.join(selected_directory, file_name + ".zip")
-
-    mean = np.asarray([0.3156024, 0.33569682, 0.34337464], dtype=np.float32)
-    std = np.asarray([0.16568947, 0.17827448, 0.18925823], dtype=np.float32)
+    #
+    # mean = np.asarray([0.3156024, 0.33569682, 0.34337464], dtype=np.float32)
+    # std = np.asarray([0.16568947, 0.17827448, 0.18925823], dtype=np.float32)
 
     # img_vecs = []
     # with torch.no_grad():