Update app.py

app.py

@@ -8,18 +8,16 @@ import spaces
 import torch
 from PIL import Image
 from diffusers import FluxInpaintPipeline
-
 from gradio_client import Client, handle_file
-from PIL import Image
 
 # Set an environment variable
 HF_TOKEN = os.environ.get("HF_TOKEN", None)
 
 MARKDOWN = """
 # FLUX.1 Inpainting with Text guided Mask🔥
-Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for FLUX,
-[Piotr Skalski](https://huggingface.co/SkalskiP),and [Gothos](https://github.com/Gothos) 
-for enabling and [showcasing inpainting](https://huggingface.co/spaces/SkalskiP/FLUX.1-inpaint) with the FLUX.
+Shoutout to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for FLUX!
+Special thanks to [Piotr Skalski](https://huggingface.co/SkalskiP) and [Gothos](https://github.com/Gothos) 
+for their work on enabling and [showcasing inpainting](https://huggingface.co/spaces/SkalskiP/FLUX.1-inpaint) with the FLUX.
 """
 
 MAX_SEED = np.iinfo(np.int32).max
@@ -29,7 +27,6 @@ DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 # Using Gradio Python Client to query EVF-SAM demo, hosted on SPaces, as an endpoint   
 client = Client("ysharma/evf-sam", hf_token=HF_TOKEN)
 
-
 pipe = FluxInpaintPipeline.from_pretrained(
     "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
 
@@ -60,17 +57,12 @@ def resize_image_dimensions(
 
 
 def evf_sam_mask(image, prompt):
-    print(image)
     images = client.predict(
       image_np=handle_file(image),
       prompt=prompt, 
       api_name="/predict")
-    print(images)
     # Open the mask image
     pil_image = Image.open(images[1])
-    print(pil_image)
-    print(type(pil_image))
-
     return pil_image
 
 @spaces.GPU(duration=150)
@@ -88,13 +80,7 @@ def process(
         gr.Info("Please enter a text prompt.")
         return None
 
-    #image = input_image_editor['background']
-    #mask = input_image_editor['layers'][0]
-    print(f"type of image: {type(input_image)}")
     mask = evf_sam_mask(input_image, input_text)
-    print(f"type of mask: {type(mask)}")
-    print(f"inpaint_text: {inpaint_text}")
-    print(f"input_text: {input_text}")
     
     if not input_image:
         gr.Info("Please upload an image.")