Update app.py

app.py

@@ -18,6 +18,7 @@ MAX_SEED = np.iinfo(np.int32).max
 TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
 os.makedirs(TMP_DIR, exist_ok=True)
 
+# Funciones auxiliares
 def start_session(req: gr.Request):
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     os.makedirs(user_dir, exist_ok=True)
@@ -28,11 +29,7 @@ def end_session(req: gr.Request):
 
 def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
     """
-    Preprocess a list of input images.
-    Args:
-        images (List[Tuple[Image.Image, str]]): The input images.
-    Returns:
-        List[Image.Image]: The preprocessed images.
+    Preprocesa una lista de imágenes.
     """
     images = [image[0] for image in images]
     processed_images = [pipeline.preprocess_image(image) for image in images]
@@ -75,7 +72,7 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict]:
 
 def get_seed(randomize_seed: bool, seed: int) -> int:
     """
-    Get the random seed.
+    Obtiene una semilla aleatoria.
     """
     return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
@@ -91,7 +88,7 @@ def image_to_3d(
     req: gr.Request,
 ) -> Tuple[dict, str]:
     """
-    Convert multiple images to a 3D model.
+    Convierte múltiples imágenes en un modelo 3D.
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     outputs = pipeline.run_multi_image(
@@ -126,7 +123,7 @@ def extract_glb(
     req: gr.Request,
 ) -> Tuple[str, str]:
     """
-    Extract a GLB file from the 3D model.
+    Extrae un archivo GLB del modelo 3D.
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, mesh = unpack_state(state)
@@ -139,7 +136,7 @@ def extract_glb(
 @spaces.GPU
 def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
     """
-    Extract a Gaussian file from the 3D model.
+    Extrae un archivo Gaussiano del modelo 3D.
     """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, _ = unpack_state(state)
@@ -150,22 +147,21 @@ def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
 
 def prepare_multi_example() -> List[Tuple[str, str]]:
     """
-    Prepare example images for the 'Multiple Images' gallery.
-    Returns:
-        List[Tuple[str, str]]: A list of tuples containing image paths and captions.
+    Prepara ejemplos de múltiples imágenes para la galería.
     """
     multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
     examples = []
     for case in multi_case:
         case_images = []
-        for i in range(1, 4):  # Assuming 3 views per case
+        for i in range(1, 4):  # Suponemos 3 vistas por caso
             img_path = f'assets/example_multi_image/{case}_{i}.png'
-            if os.path.exists(img_path):  # Ensure the image exists
+            if os.path.exists(img_path):  # Asegurarse de que la imagen existe
                 case_images.append((img_path, f"View {i}"))
-        if case_images:  # Only add cases with valid images
+        if case_images:  # Solo añadir casos con imágenes válidas
             examples.append(case_images)
     return examples
 
+# Interfaz Gradio
 with gr.Blocks(delete_cache=(600, 600)) as demo:
     gr.Markdown("""
     ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
@@ -220,18 +216,18 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
 
     output_buf = gr.State()
 
-    # Example images at the bottom of the page
+    # Ejemplos de imágenes múltiples
     with gr.Row(visible=True) as multiimage_example:
         examples_multi = gr.Examples(
             examples=prepare_multi_example(),
             inputs=[multiimage_prompt],
-            fn=lambda x: x,
+            fn=lambda x: x,  # Pasar la entrada directamente (sin preprocesamiento adicional)
             outputs=[multiimage_prompt],
             run_on_click=True,
             examples_per_page=8,
         )
 
-    # Handlers
+    # Manejadores
     demo.load(start_session)
     demo.unload(end_session)
 
@@ -282,12 +278,12 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         outputs=[download_glb],
     )
 
-# Launch the Gradio app
+# Lanzar la aplicación Gradio
 if __name__ == "__main__":
     pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
     pipeline.cuda()
     try:
-        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
+        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))  # Precargar rembg
     except:
         pass
     demo.launch(show_error=True)