Update app.py

app.py

@@ -1,5 +1,4 @@
 import gradio as gr
-import spaces
 
 import os
 import shutil
@@ -30,7 +29,7 @@ def start_session(req: gr.Request):
     
 def end_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    shutil.rmtree(user_dir)
+    #shutil.rmtree(user_dir)
 
 
 def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
@@ -80,7 +79,6 @@ def get_seed(randomize_seed: bool, seed: int) -> int:
     return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
 
-@spaces.GPU
 def text_to_3d(
     prompt: str,
     seed: int,
@@ -106,6 +104,7 @@ def text_to_3d(
         str: The path to the video of the 3D model.
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    os.makedirs(user_dir, exist_ok=True)
     outputs = pipeline.run(
         prompt,
         seed=seed,
@@ -119,17 +118,15 @@ def text_to_3d(
             "cfg_strength": slat_guidance_strength,
         },
     )
-    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
-    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
-    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
-    video_path = os.path.join(user_dir, 'sample.mp4')
-    imageio.mimsave(video_path, video, fps=15)
     state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
+    ply_path = os.path.join(user_dir, 'point_cloud.ply')
+    gaussian_data = outputs['gaussian'][0]
+    with open(ply_path, "wb") as f:
+        gaussian_data.save_ply(f)
     torch.cuda.empty_cache()
-    return state, video_path
+    return state, ply_path
 
 
-@spaces.GPU(duration=90)
 def extract_glb(
     state: dict,
     mesh_simplify: float,
@@ -156,7 +153,6 @@ def extract_glb(
     return glb_path, glb_path
 
 
-@spaces.GPU
 def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
     """
     Extract a Gaussian file from the 3D model.
@@ -271,4 +267,4 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
 if __name__ == "__main__":
     pipeline = TrellisTextTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-text-xlarge")
     pipeline.cuda()
-    demo.launch()
+    demo.launch(show_error=True,server_name="0.0.0.0",server_port=20000)