Update app.py

app.py

@@ -114,25 +114,35 @@ def end_session(req: gr.Request):
     shutil.rmtree(user_dir)
 
 @spaces.GPU
-def preprocess_image(image: Image.Image, 
-                    prompt: str = "",
-                    negative_prompt: str = "",
-                    style_name: str = "",
-                    num_steps: int = 25,
-                    guidance_scale: float = 5,
-                    controlnet_conditioning_scale: float = 1.0,
-                    ) -> Image.Image:
-    width, height  = image['composite'].size
+def preprocess_image(
+    image: Image.Image,
+    prompt: str = "",
+    negative_prompt: str = "",
+    style_name: str = "",
+    num_steps: int = 25,
+    guidance_scale: float = 5,
+    controlnet_conditioning_scale: float = 1.0,
+    req: gr.Request = None  # Agregamos el parámetro `req`
+) -> Tuple[Image.Image, Image.Image]:
+    # Crear un directorio único para el usuario basado en su sesión
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    os.makedirs(user_dir, exist_ok=True)  # Asegurarse de que el directorio existe
+
+    # Procesar las dimensiones de la imagen
+    width, height = image['composite'].size
     ratio = np.sqrt(1024. * 1024. / (width * height))
     new_width, new_height = int(width * ratio), int(height * ratio)
     image = image['composite'].resize((new_width, new_height))
     image = ImageOps.invert(image)
 
-    print("image:",type(image))
+    print("image:", type(image))
 
+    # Aplicar estilo al prompt
     prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
-    
+
     print("params:", prompt, negative_prompt, style_name, num_steps, guidance_scale, controlnet_conditioning_scale)
+
+    # Generar la imagen procesada usando el pipeline
     output = pipe_control(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -141,11 +151,17 @@ def preprocess_image(image: Image.Image,
         controlnet_conditioning_scale=controlnet_conditioning_scale,
         guidance_scale=guidance_scale,
         width=new_width,
-        height=new_height).images[0]
-    
-        
+        height=new_height
+    ).images[0]
+
+    # Guardar la imagen procesada en el directorio del usuario
+    processed_image_path = os.path.join(user_dir, 'processed_image.png')
+    output.save(processed_image_path)
+
+    # Preprocesar la imagen para el siguiente paso (si es necesario)
     processed_image = pipeline.preprocess_image(output)
-    return (image, processed_image)
+
+    return image, processed_image
 
 def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
     return {