update app.py # 7

app.py

@@ -1,26 +1,26 @@
-import os
-import shutil
-import zipfile
-from os.path import join, isfile, basename
-
-import cv2
-import numpy as np
+# import os
+# import shutil
+# import zipfile
+# from os.path import join, isfile, basename
+#
+# import cv2
+# import numpy as np
 import gradio as gr
-import torch
-
-from resnet50 import resnet18
-from sampling_util import furthest_neighbours
-from video_reader import video_reader
+# import torch
 
-model = resnet18(
-    output_dim=0,
-    nmb_prototypes=0,
-    eval_mode=True,
-    hidden_mlp=0,
-    normalize=False)
-model.load_state_dict(torch.load("model.pt"))
-model.eval()
-avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
+# from resnet50 import resnet18
+# from sampling_util import furthest_neighbours
+# from video_reader import video_reader
+#
+# model = resnet18(
+#     output_dim=0,
+#     nmb_prototypes=0,
+#     eval_mode=True,
+#     hidden_mlp=0,
+#     normalize=False)
+# model.load_state_dict(torch.load("model.pt"))
+# model.eval()
+# avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
 
 
 def predict(input_file, downsample_size):
@@ -76,11 +76,11 @@ def predict(input_file, downsample_size):
     #                           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))
-    zipf.close()
-    print("selected images zipped")
+    # zipf = zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED)
+    # # for i, f in enumerate(all_selected_imgs_path):
+    # #     zipf.write(f, basename(f))
+    # zipf.close()
+    # print("selected images zipped")
 
     return zip_path