nicer UI, catch rec errors, try fix rendering mesh by re-enabling tabs

app.py

@@ -15,7 +15,7 @@ def run_on_gpu(input_point_cloud: gr.utils.NamedString,
                gen_resolution_global: int,
                padding_factor: float,
                gen_subsample_manifold_iter: int,
-               gen_refine_iter: int):
+               gen_refine_iter: int) -> str:
     print('Started inference at {}'.format(datetime.datetime.now()))
     print('Inputs:', input_point_cloud, gen_resolution_global, padding_factor,
           gen_subsample_manifold_iter, gen_refine_iter)
@@ -32,7 +32,6 @@ def run_on_gpu(input_point_cloud: gr.utils.NamedString,
     # splitext_result = os.path.splitext(in_file)
     rand_hash = uuid.uuid4().hex
     out_dir = '/tmp/outputs/{}'.format(rand_hash)
-    # out_file = os.path.join(out_dir, in_file, in_file + '.ply')
     out_file_basename = os.path.basename(in_file) + '.ply'
     out_file = os.path.join(out_dir, os.path.basename(in_file), out_file_basename)
     os.makedirs(out_dir, exist_ok=True)
@@ -55,34 +54,41 @@ def run_on_gpu(input_point_cloud: gr.utils.NamedString,
         ]
 
     sys.argv = args
-    subprocess.run(['python', 'ppsurf/pps.py'] + args[1:])  # need subprocess to spawn workers
+    try:
+        subprocess.run(['python', 'ppsurf/pps.py'] + args[1:])  # need subprocess to spawn workers
+    except Exception as e:
+        print('Error:', e)  # print to console
+        gr.Warning("Reconstruction failed, see console log for details.")  # notify user
+
     print('Finished inference at {}'.format(datetime.datetime.now()))
 
     result_3d_model = out_file
-    progress_text = 'done'
 
-    return result_3d_model, progress_text
+    return result_3d_model
 
 
 def main():
-    description = '''# [PPSurf](https://github.com/cg-tuwien/ppsurf)
-    
+    description_header = '# PPSurf [Github](https://github.com/cg-tuwien/ppsurf) [Project](https://www.cg.tuwien.ac.at/research/publications/2024/erler_2024_ppsurf/)'
+
+    description_col0 = '''
     Supported file formats: 
     - PLY, STL, OBJ and other mesh files, 
     - XYZ as whitespace-separated text file, 
     - NPY and NPZ (key='arr_0'), 
     - LAS and LAZ (version 1.0-1.4), COPC and CRS.
     Best results for 50k-250k points. 
-    
+    '''
+
+    description_col1 = '''
     This method is meant for scans of single and few objects. 
     Quality for scenes and landscapes will be lower.
     
-    Inference takes about 2 minutes.
+    Inference takes up to 180 seconds.
     '''
 
     # can't render many input types directly in Gradio Model3D
     # so we need to convert to supported format
-    # Gradio can't draw point clouds anyway, so we skip this for now
+    # Gradio can't draw point clouds anyway (2024-03-04), so we skip this for now
     # def convert_to_ply(input_point_cloud_upload: gr.utils.NamedString):
     #
     #     # add absolute path to import dirs
@@ -115,17 +121,18 @@ def main():
     #     # input_point_cloud_viewer.value = input_shape
 
     if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
-        description += (f'\n<p>For faster inference without waiting in queue, '
-                        f'you may duplicate the space and upgrade to GPU in settings. '
-                        f'<a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true">'
-                        f'<img style="display: inline; margin-top: 0em; margin-bottom: 0em" '
-                        f'src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>')
+        description_col1 += (f'\n<p>For faster inference without waiting in queue, '
+                             f'you may duplicate the space and upgrade to GPU in settings. '
+                             f'<a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true">'
+                             f'<img style="display: inline; margin-top: 0em; margin-bottom: 0em" '
+                             f'src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>')
 
     with gr.Blocks(css='style.css') as demo:
-        gr.Markdown(description)
+        gr.Markdown(description_header)
         with gr.Row():
             with gr.Column():
-                # with gr.Tabs() as input_tabs:
+                gr.Markdown(description_col0)
+                # with gr.Tabs() as input_tabs:  # re-enable when Gradio supports point clouds
                 #     with gr.TabItem(label='Input Point Cloud Upload', id='pc_upload'):
                 input_point_cloud_upload = gr.File(show_label=False, file_count='single')
                         # input_point_cloud_upload.upload(
@@ -151,10 +158,11 @@ def main():
                     label='Edge Refinement Iterations (larger for more details)',
                     minimum=3, maximum=30, value=10, step=1)
             with gr.Column():
-                progress_text = gr.Text(label='Progress')
-                # with gr.Tabs():
-                #     with gr.TabItem(label='Reconstructed 3D model'):
-                result_3d_model = gr.Model3D(show_label=False)
+                gr.Markdown(description_col1)
+                # progress_text = gr.Text(label='Progress')
+                with gr.Tabs():
+                    with gr.TabItem(label='Reconstructed 3D model'):
+                        result_3d_model = gr.Model3D(show_label=False)
                     # with gr.TabItem(label='Output mesh file'):
                     #     output_file = gr.File(show_label=False)
         # with gr.Row():
@@ -192,7 +200,7 @@ def main():
                          outputs=[
                              result_3d_model,
                              # output_file,
-                             progress_text,
+                             # progress_text,
                          ])
 
     demo.queue(max_size=5)