Update app.py

app.py

@@ -32,9 +32,10 @@ import shutil
 from gradio_client import Client, handle_file
 
 # Load the model once globally to avoid repeated loading
+"""
 def load_inpainting_model():
     # Load pipeline
-    model_path = "urpmv13Inpainting.safetensors"
+    #model_path = "urpmv13Inpainting.safetensors"
     #model_path = "uberRealisticPornMerge_v23Inpainting.safetensors"
     #model_path = "pornmasterFantasy_v4-inpainting.safetensors"
     #model_path = "pornmasterAmateur_v6Vae-inpainting.safetensors"
@@ -45,7 +46,7 @@ def load_inpainting_model():
         safety_checker=None
     ).to(device)
     return pipe
-
+"""
 """
 # Load the model once globally to avoid repeated loading
 def load_upscaling_model():
@@ -65,7 +66,7 @@ def load_upscaling_model():
 """
 
 # Preload the model once
-inpaint_pipeline = load_inpainting_model()
+#inpaint_pipeline = load_inpainting_model()
 # Preload the model once
 #upscale_pipeline = load_upscaling_model()
 
@@ -95,7 +96,7 @@ def resize_to_match(input_image, output_image):
 
 # Function to generate the mask using Florence SAM Masking API (Replicate)
 def generate_mask(image_path, text_prompt="clothing"):
-    client_sam = Client("SkalskiP/florence-sam-masking")
+    client_sam = Client("SkalskiP/florence-sam")
     mask_result = client_sam.predict(
         image_input=handle_file(image_path),  # Provide your image path here
         text_input=text_prompt,  # Use "clothing" as the prompt
@@ -121,11 +122,11 @@ def inpaint_image(input_image, mask_image):
     blurred_mask_image = inpaint_pipeline.mask_processor.blur(initial_mask_image,blur_factor=10)
     result = inpaint_pipeline(prompt=prompt, negative_prompt=negative_prompt, height=IMAGE_SIZE[0], width=IMAGE_SIZE[0], image=initial_input_image, mask_image=blurred_mask_image, padding_mask_crop=32)
     """
-    blurred_mask_image = inpaint_pipeline.mask_processor.blur(mask_image,blur_factor=10)
-    result = inpaint_pipeline(prompt=prompt, negative_prompt=negative_prompt, image=input_image, mask_image=blurred_mask_image, padding_mask_crop=10)
-    inpainted_image = result.images[0]
+    #blurred_mask_image = inpaint_pipeline.mask_processor.blur(mask_image,blur_factor=10)
+    #result = inpaint_pipeline(prompt=prompt, negative_prompt=negative_prompt, image=input_image, mask_image=blurred_mask_image, padding_mask_crop=10)
+    #inpainted_image = result.images[0]
     #inpainted_image = resize_to_match(input_image, inpainted_image)
-    return inpainted_image
+    return mask_image
 
 # Function to process input image and mask
 def process_image(input_image):
@@ -135,7 +136,8 @@ def process_image(input_image):
 
     # Generate the mask using Florence SAM API
     mask_local_path = generate_mask(image_path=input_image_path)
-
+    mask_local_path1 = mask_local_path[0]
+    
     # Save the generated mask
     mask_image_path = "generated_mask.png"
     save_mask(mask_local_path, save_path=mask_image_path)