final fixes hopefully

app.py

@@ -115,9 +115,9 @@ demo = gr.Interface(
     outputs = 'image',
     examples= [example1, example2, example3 ,example4 ,example5],
     title="🛋 Style your sofa 🛋 ",
-    description="Customize your sofa to your 🌈 wildest dreams 🌈!\
+    description="Customize your sofa to your wildest dreams 💭!\
                 \nProvide a picture of your sofa and a desired pattern\
-                 or choose one of the examples below. ⬇️",
+                 or choose one of the examples below. ⬇",
     # article="**References**\n\n"
     # "<a href='https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization' target='_blank'>1. Tutorial to implement Fast Neural Style Transfer using the pretrained model from TensorFlow Hub</a>  \n"
     # "<a href='https://huggingface.co/spaces/luca-martial/neural-style-transfer' target='_blank'>2. The idea to build a neural style transfer application was inspired from this Hugging Face Space </a>"

segmentation.py

@@ -67,8 +67,6 @@ def replace_sofa(image:Image, mask:Image, styled_sofa:Image) -> Image:
         new_image   = Image containing the styled sofa
     """
     image,mask,styled_sofa = np.array(image),np.array(mask),np.array(styled_sofa) 
-    #image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    styled_sofa = cv2.cvtColor(styled_sofa, cv2.COLOR_BGR2RGB)
 
     _, mask = cv2.threshold(mask, 10, 255, cv2.THRESH_BINARY)
     mask_inv = cv2.bitwise_not(mask)

styleTransfer.py

@@ -123,6 +123,9 @@ def perform_style_transfer(content_image, style_image):
     stylized_image = output[0]
     return Image.fromarray(np.uint8(stylized_image[0] * 255))
 
+################################################# MAIN ################################################
+
+
 def create_styledSofa(sofa:Image, style:Image):
     styled_sofa = StyleTransformer(sofa,style)
     return styled_sofa