Update app.py

app.py

@@ -212,7 +212,6 @@ def extract_frames(input_video, session_id):
 
 
 def update_gallery_on_video_upload(input_video, session_id):
-
     if not input_video:
         return None, None, None
         
@@ -229,6 +228,17 @@ def update_gallery_on_images_upload(input_images, session_id):
 
 @spaces.GPU()
 def generate_splats_from_video(video_path, session_id=None):
+    """
+    Perform Gaussian Splatting from Unconstrained Views a Given Video, using a Feed-forward model.
+    
+    Args:
+        video_path (str): Path to the input video file on disk.
+    Returns:
+        plyfile: Path to the reconstructed 3D object from the given video.
+        rgb_vid: Path the the interpolated rgb video, increasing the frame rate using guassian splatting and interpolation of frames.
+        depth_vid: Path the the interpolated depth video, increasing the frame rate using guassian splatting and interpolation of frames.
+        image_paths: A list of paths from extracted frame from the video that is used for training Gaussian Splatting.
+    """
     
     if session_id is None:
         session_id = uuid.uuid4().hex
@@ -240,7 +250,16 @@ def generate_splats_from_video(video_path, session_id=None):
     
 @spaces.GPU()
 def generate_splats_from_images(image_paths, session_id=None):
-
+    """
+    Perform Gaussian Splatting from Unconstrained Views a Given Images , using a Feed-forward model.
+    
+    Args:
+        image_paths (str): Path to the input image files on disk.
+    Returns:
+        plyfile: Path to the reconstructed 3D object from the given image files.
+        rgb_vid: Path the the interpolated rgb video, increasing the frame rate using guassian splatting and interpolation of frames.
+        depth_vid: Path the the interpolated depth video, increasing the frame rate using guassian splatting and interpolation of frames.
+    """
     processed_image_paths = []
 
     for file_data in image_paths:
@@ -267,12 +286,12 @@ def generate_splats_from_images(image_paths, session_id=None):
 
     print("Running run_model...")
     with torch.no_grad():
-        plyfile, video, depth_colored = get_reconstructed_scene(base_dir, image_paths, model, device)
+        plyfile, rgb_vid, depth_vid = get_reconstructed_scene(base_dir, image_paths, model, device)
 
     end_time = time.time()
     print(f"Total time: {end_time - start_time:.2f} seconds (including IO)")
 
-    return plyfile, video, depth_colored
+    return plyfile, rgb_vid, depth_vid
 
 def cleanup(request: gr.Request):
 
@@ -422,14 +441,16 @@ if __name__ == "__main__":
             fn=update_gallery_on_video_upload,
             inputs=[input_video, session_state],
             outputs=[reconstruction_output, target_dir_output, image_gallery],
+            show_api=False
         )
 
         input_images.upload(
             fn=update_gallery_on_images_upload,
             inputs=[input_images, session_state],
             outputs=[reconstruction_output, target_dir_output, image_gallery],
+            show_api=False
         )
 
         demo.unload(cleanup)
         demo.queue()
-        demo.launch(show_error=True, share=True)
+        demo.launch(show_error=True, share=True, mcp_server=True)