update app.py # 6

app.py

@@ -25,14 +25,14 @@ avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
 
 def predict(input_file, downsample_size):
     downsample_size = int(downsample_size)
-    base_directory = os.getcwd()
-    selected_directory = os.path.join(base_directory, "selected_images")
+    # 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)
     #
-    file_name = (input_file.split('/')[-1]).split('.')[-1]
-    zip_path = os.path.join(selected_directory, file_name + ".zip")
+    # 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)
@@ -75,6 +75,7 @@ def predict(input_file, downsample_size):
     # all_selected_imgs_path = [join(selected_directory, f) for f in os.listdir(selected_directory) if
     #                           isfile(join(selected_directory, f))]
 
+    zip_path = "asd.zip"
     zipf = zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED)
     # for i, f in enumerate(all_selected_imgs_path):
     #     zipf.write(f, basename(f))