updated app

app.py

@@ -13,9 +13,7 @@ sys.path.append("./rome/")
 sys.path.append('./DECA')
 
 # loading models ---- create model repo
-
-default_modnet_path = hf_hub_download(
-    'Pie31415/rome', 'modnet_photographic_portrait_matting.ckpt')
+default_modnet_path = hf_hub_download('Pie31415/rome', 'modnet_photographic_portrait_matting.ckpt')
 default_model_path = hf_hub_download('Pie31415/rome', 'rome.pth')
 
 # parser configurations
@@ -126,25 +124,23 @@ def image_inference(
 ):
     out = infer.evaluate(source_img, driver_img, crop_center=False)
     res = tensor2image(torch.cat([out['source_information']['data_dict']['source_img'][0].cpu(),
-                                  out['source_information']['data_dict']['target_img'][0].cpu(
-    ),
-        out['render_masked'].cpu(), out['pred_target_shape_img'][0].cpu()], dim=2))
+                                  out['source_information']['data_dict']['target_img'][0].cpu(),
+                                  out['render_masked'].cpu(), out['pred_target_shape_img'][0].cpu()], dim=2))
     return res[..., ::-1]
 
-def folder_inference():
+def video_inference():
     pass
 
 with gr.Blocks() as demo:
     with gr.Tab("Image Inference"):
-        image_input = [gr.Image(type="pil"), gr.Image(type="pil")]
+        image_inputs = [gr.Image(type="pil"), gr.Image(type="pil")]
         image_output = gr.Image()
         image_button = gr.Button("Predict")
-    with gr.Tab("Inference Over Folder"):
-        pass
     with gr.Tab("Video Inference"):
+        video_inputs = [gr.Video(), gr.Image()]
         pass
 
-    image_button.click(image_inference, inputs=image_input, outputs=image_output)
+    image_button.click(image_inference, inputs=image_inputs, outputs=image_output)
     title = "ROME: Realistic one-shot mesh-based head avatars"
     examples = gr.Examples(["examples/lincoln.jpg", "examples/tars2.jpg"])