one more test with user HF_TOKEN

app.py

@@ -23,52 +23,6 @@ IMAGE_SIZE = 1024
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 PIPE = FluxInpaintPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
-CLIENT = Client("SkalskiP/florence-sam-masking")
-
-
-# def remove_background(image: Image.Image, threshold: int = 50) -> Image.Image:
-#     image = image.convert("RGBA")
-#     data = image.getdata()
-#     new_data = []
-#     for item in data:
-#         avg = sum(item[:3]) / 3
-#         if avg < threshold:
-#             new_data.append((0, 0, 0, 0))
-#         else:
-#             new_data.append(item)
-#
-#     image.putdata(new_data)
-#     return image
-
-
-# EXAMPLES = [
-#     [
-#         {
-#             "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
-#             "layers": [remove_background(Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-2.png", stream=True).raw))],
-#             "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-2.png", stream=True).raw),
-#         },
-#         "little lion",
-#         None,
-#         42,
-#         False,
-#         0.85,
-#         30
-#     ],
-#     [
-#         {
-#             "background": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-image.png", stream=True).raw),
-#             "layers": [remove_background(Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-mask-3.png", stream=True).raw))],
-#             "composite": Image.open(requests.get("https://media.roboflow.com/spaces/doge-2-composite-3.png", stream=True).raw),
-#         },
-#         "tattoos",
-#         None,
-#         42,
-#         False,
-#         0.85,
-#         30
-#     ]
-# ]
 
 
 def calculate_image_dimensions_for_flux(
@@ -193,7 +147,7 @@ def process(
         return None, None
 
     if is_mask_empty(mask):
-        mask = CLIENT.predict(
+        mask = client.predict(
             image_input=handle_file(image_path),
             text_input=masking_prompt_text,
             api_name="/process_image")
@@ -308,26 +262,6 @@ with gr.Blocks() as demo:
             with gr.Accordion("Debug", open=False):
                 output_mask_component = gr.Image(
                     type='pil', image_mode='RGB', label='Input mask', format="png")
-    # with gr.Row():
-    #     gr.Examples(
-    #         fn=process,
-    #         examples=EXAMPLES,
-    #         inputs=[
-    #             input_image_editor_component,
-    #             inpainting_prompt_text_component,
-    #             masking_prompt_text_component,
-    #             seed_slicer_component,
-    #             randomize_seed_checkbox_component,
-    #             strength_slider_component,
-    #             num_inference_steps_slider_component
-    #         ],
-    #         outputs=[
-    #             output_image_component,
-    #             output_mask_component
-    #         ],
-    #         run_on_click=True,
-    #         cache_examples=True
-    #     )
 
     submit_button_component.click(
         fn=process,