Update app.py

app.py

@@ -2,11 +2,6 @@ import gradio as gr
 import spaces
 from gradio_litmodel3d import LitModel3D
 
-import os
-import shutil
-import subprocess
-import sys
-
 # Install local wheels at runtime
 def install_local_wheels():
     """Install the local wheel files that couldn't be installed during Docker build."""
@@ -24,6 +19,8 @@ def install_local_wheels():
 # Install wheels before importing trellis
 install_local_wheels()
 
+import os
+import shutil
 os.environ['SPCONV_ALGO'] = 'native'
 from typing import *
 import torch
@@ -55,10 +52,8 @@ def end_session(req: gr.Request):
 def preprocess_image(image: Image.Image) -> Image.Image:
     """
     Preprocess the input image.
-
     Args:
         image (Image.Image): The input image.
-
     Returns:
         Image.Image: The preprocessed image.
     """
@@ -143,7 +138,6 @@ def image_to_3d(
 ) -> Tuple[dict, str]:
     """
     Convert an image to a 3D model.
-
     Args:
         image (Image.Image): The input image.
         multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
@@ -154,7 +148,6 @@ def image_to_3d(
         slat_guidance_strength (float): The guidance strength for structured latent generation.
         slat_sampling_steps (int): The number of sampling steps for structured latent generation.
         multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
-
     Returns:
         dict: The information of the generated 3D model.
         str: The path to the video of the 3D model.
@@ -210,12 +203,10 @@ def extract_glb(
 ) -> Tuple[str, str]:
     """
     Extract a GLB file from the 3D model.
-
     Args:
         state (dict): The state of the generated 3D model.
         mesh_simplify (float): The mesh simplification factor.
         texture_size (int): The texture resolution.
-
     Returns:
         str: The path to the extracted GLB file.
     """
@@ -235,10 +226,8 @@ def extract_glb(
 def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
     """
     Extract a Gaussian file from the 3D model.
-
     Args:
         state (dict): The state of the generated 3D model.
-
     Returns:
         str: The path to the extracted Gaussian file.
     """
@@ -281,11 +270,11 @@ def split_image(image: Image.Image) -> List[Image.Image]:
 
 with gr.Blocks(delete_cache=(600, 600)) as demo:
     gr.Markdown("""
-    ## ASM - Advanced Spatial Modeling for 3D Generation
+    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
     * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
     * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
     
-    ✨Features: 1) Multi-image support. 2) Gaussian file extraction. 3) Advanced 3D generation.
+    ✨New: 1) Experimental multi-image support. 2) Gaussian file extraction.
     """)
     
     with gr.Row():
@@ -366,11 +355,11 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
     demo.unload(end_session)
     
     single_image_input_tab.select(
-        lambda: tuple([False, gr.update(visible=True), gr.update(visible=False)]),
+        lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
         outputs=[is_multiimage, single_image_example, multiimage_example]
     )
     multiimage_input_tab.select(
-        lambda: tuple([True, gr.update(visible=False), gr.update(visible=True)]),
+        lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
         outputs=[is_multiimage, single_image_example, multiimage_example]
     )