Update app.py

app.py

@@ -90,10 +90,7 @@ def transform_my_model(content_image, style_image):
         logger.error(f"Error in transform_my_model: {str(e)}")
         raise
 
-# Create examples directory if it doesn't exist
-os.makedirs("examples", exist_ok=True)
-
-# Create the Gradio interface with minimal configuration
+# Create the Gradio interface with Hugging Face Spaces configuration
 demo = gr.Interface(
     fn=transform_my_model,
     inputs=[
@@ -102,14 +99,34 @@ demo = gr.Interface(
     ],
     outputs=gr.Image(type="pil", label="Result"),
     title="Neural Style Transfer",
-    description="Upload a content image and a style image to create a stylized version of your content image.",
-    examples=None,  # Disable examples to avoid API issues
-    allow_flagging="never"  # Disable flagging to avoid API issues
+    description="""
+    ## Neural Style Transfer
+    Upload a content image and a style image to create a stylized version of your content image.
+    
+    ### How to use:
+    1. Upload your content image (the image you want to style)
+    2. Upload your style image (the image whose style you want to apply)
+    3. Click 'Submit' and wait for the result
+    
+    ### Tips:
+    - For best results, use clear, high-quality images
+    - The style image's characteristics will be transferred to your content image
+    - Processing may take a few seconds depending on image size
+    """,
+    examples=[
+        ["Content_Images/contnt12.jpg", "VG516.jpg"],
+        ["Content_Images/contnt2.jpg", "Content_Images/styl9.jpg"],
+        ["Content_Images/contnt.jpg", "Content_Images/styl22.jpg"]
+    ],
+    cache_examples=True,
+    allow_flagging=False,
+    analytics_enabled=False
 )
 
+# For Hugging Face Spaces deployment
 if __name__ == "__main__":
     demo.launch(
         server_name="0.0.0.0",
         server_port=7860,
-        share=True
+        share=False  # Set to False for Hugging Face Spaces
     )