making load model efficient

app.py

@@ -73,7 +73,7 @@ def process_image(args, inpainting_pipeline, net):
     transforms_list += [transforms.ToTensor()]
     transforms_list += [Normalize_image(0.5, 0.5)]
     transform_rgb = transforms.Compose(transforms_list)
-    # img = Image.open(image_path)
+    img = Image.open(image_path)
     img = img.convert("RGB")
     img = img.resize((args.resolution, args.resolution))
     if args.rembg:
@@ -107,16 +107,15 @@ def process_image(args, inpainting_pipeline, net):
     clothed_image_from_pipeline = change_bg_color(clothed_image_from_pipeline, "WHITE")
     return clothed_image_from_pipeline.convert("RGB")
 
-
-st.title("Stable Fashion Huggingface Spaces")
-file_name = st.file_uploader("Upload a clear full length picture of yourself, preferably in a less noisy background")
 net = load_u2net()
 inpainting_pipeline = load_inpainting_pipeline()
+st.title("Stable Fashion Huggingface Spaces")
+file_name = st.file_uploader("Upload a clear full length picture of yourself, preferably in a less noisy background")
+
 
 if file_name is not None:
-    image = Image.open(file_name)
     stable_fashion_args = StableFashionCLIArgs()
-    stable_fashion_args.image = image
+    stable_fashion_args.image = file_name
     body_part = st.radio("Would you like to try clothes on your upper body (such as shirts, kurtas etc) or lower (Jeans, Pants etc)? ", ('Upper', 'Lower'))
     stable_fashion_args.part = body_part
     resolution = st.radio("Which resolution would you like to get the resulting picture in? (Keep in mind, higher the resolution, higher the queue times)", (128, 256, 512))
@@ -132,7 +131,7 @@ if file_name is not None:
 
 
     result_image = process_image(stable_fashion_args, inpainting_pipeline, net)
-    st.image(result_image, caption='Sunrise by the mountains')
+    st.image(result_image, caption='Result')