update app.py #5

app.py

@@ -22,67 +22,6 @@ model.load_state_dict(torch.load("model.pt"))
 model.eval()
 avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
 
-import urllib.request
-urllib.request.urlretrieve("https://media.githubusercontent.com/media/commaai/speedchallenge/master/data/test.mp4", 'video_name.mp4')
-input_file = 'video_name.mp4'
-
-downsample_size = int(100)
-
-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")
-
-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():
-    for fp_i, file_path in enumerate([input_file]):
-        for i, in_img in enumerate(video_reader(file_path,
-                                                targetFPS=9,
-                                                targetWidth=100,
-                                                to_rgb=True)):
-            in_img = (in_img.astype(np.float32) / 255.)
-            in_img = (in_img - mean) / std
-            in_img = np.expand_dims(in_img, 0)
-            in_img = np.transpose(in_img, (0, 3, 1, 2))
-            in_img = torch.from_numpy(in_img).float()
-            encoded = avg_pool(model(in_img))[0, :, 0, 0].cpu().numpy()
-            img_vecs += [encoded]
-img_vecs = np.asarray(img_vecs)
-print("images encoded")
-rv_indices, _ = furthest_neighbours(
-    img_vecs,
-    downsample_size,
-    seed=0)
-indices = np.zeros((img_vecs.shape[0],))
-indices[np.asarray(rv_indices)] = 1
-print("images selected")
-
-global_ctr = 0
-for fp_i, file_path in enumerate([input_file]):
-    for i, img in enumerate(video_reader(file_path,
-                                         targetFPS=9,
-                                         targetWidth=None,
-                                         to_rgb=False)):
-        if indices[global_ctr] == 1:
-            cv2.imwrite(join(selected_directory, str(global_ctr) + ".jpg"), img)
-        global_ctr += 1
-print("selected images extracted")
-
-all_selected_imgs_path = [join(selected_directory, f) for f in os.listdir(selected_directory) if
-                          isfile(join(selected_directory, f))]
-
-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")
-
 
 def predict(input_file, downsample_size):
     downsample_size = int(downsample_size)
@@ -149,6 +88,8 @@ demo = gr.Interface(
     fn=predict,
     inputs=[gr.components.Video(label="Upload Video File"),
             gr.components.Number(label="Downsample size")],
-    outputs=gr.components.File(label="Zip"))
+    outputs=gr.components.File(label="Zip"),
+    live=True,
+)
 
-demo.launch(debug=True)
+demo.launch(debug=True, share=True)