update app.py # 2

app.py

@@ -22,14 +22,17 @@ model.load_state_dict(torch.load("model.pt"))
 model.eval()
 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")
     if os.path.isdir(selected_directory):
         shutil.rmtree(selected_directory)
     os.mkdir(selected_directory)
 
-    zip_path = os.path.join(selected_directory, input_file.split('/')[-1][:-4] + ".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)
@@ -85,9 +88,9 @@ demo = gr.Interface(
     enable_queue=True,
     title="Frame selection by visual difference",
     fn=predict,
-    inputs=[gr.components.Video(label="Upload Video File"),
-            gr.components.Number(label="Downsample size", precision=0)],
-    outputs=gr.components.File(label="Zip"),
+    inputs=[gr.inputs.Video(label="Upload Video File"),
+            gr.inputs.Number(label="Downsample size")],
+    outputs=gr.outputs.File(label="Zip"),
 )
 
 demo.launch(debug=True)