Update gradio_app.py

gradio_app.py

@@ -137,11 +137,14 @@ def forward_model(batch, system, guidance_scale=3.0, seed=0, device="cuda"):
 
     return pc_cond
 
-def generate_and_process_3d(prompt: str, seed: int = 42) -> tuple[str | None, Image.Image | None]:
+def generate_and_process_3d(prompt: str) -> tuple[str | None, Image.Image | None]:
     """Generate image from prompt and convert to 3D model."""
 
     width: int = 1024
     height: int = 1024
+
+    # Generate random seed
+    seed = np.random.randint(0, np.iinfo(np.int32).max)
     
     try:
         # Set random seeds
@@ -219,32 +222,34 @@ def generate_and_process_3d(prompt: str, seed: int = 42) -> tuple[str | None, Im
         import traceback
         traceback.print_exc()
         return None
-        
-# Create Gradio interface
-demo = gr.Interface(
-    fn=generate_and_process_3d,
-    inputs=[
-        gr.Text(
+
+# Create Gradio app using Blocks
+with gr.Blocks() as demo:
+    gr.Markdown("# Text to 3D")
+    gr.Markdown("This space is base on Stable Point-Awaire 3D by Stability AI.")
+    
+    with gr.Row():
+        prompt_input = gr.Text(
             label="Enter your prompt",
-            placeholder="Describe what you want to generate..."
-        ),
-        gr.Slider(
-            label="Seed",
-            minimum=0,
-            maximum=np.iinfo(np.int32).max,
-            step=1,
-            value=42
+            placeholder="eg. isometric 3D game castle"
         )
-    ],
-    outputs=[
-        gr.Model3D(
-            label="Generated 3D model",
+    
+    with gr.Row():
+        generate_btn = gr.Button("Generate", variant="primary")
+    
+    with gr.Row():
+        model_output = gr.Model3D(
+            label="Generated .GLB model",
             clear_color=[0.0, 0.0, 0.0, 0.0],
         )
-    ],
-    title="Text to 3D",
-    description="This space is based on Stable Point-Aware 3D by Stability AI.",
-)
-
+    
+    # Event handler
+    generate_btn.click(
+        fn=generate_and_process_3d,
+        inputs=[prompt_input],
+        outputs=[model_output],
+        api_name="generate"
+    )
+    
 if __name__ == "__main__":
     demo.queue().launch()