Update app.py

app.py

@@ -1,65 +1,65 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-import cv2
-import os
-from main import tensor_to_image,StyleContentModel, run_style_transfer
-
-# Define layers and instantiate the model once globally
-content_layers = ['block5_conv2'] 
-style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']
-extractor = StyleContentModel(style_layers, content_layers)
-
-def style_transfer_wrapper(content_img_np, style_img_np):
-    """
-    A wrapper to handle I/O for the Gradio interface.
-    Saves numpy arrays to temp files to use with the main function.
-    """
-    if content_img_np is None or style_img_np is None:
-        return None # Return None if either image is missing
-
-    # Save numpy arrays to temporary files
-    content_path = "temp_content.jpg"
-    style_path = "temp_style.jpg"
-    
-    # Gradio provides RGB, but cv2 saves in BGR order
-    cv2.imwrite(content_path, cv2.cvtColor(content_img_np, cv2.COLOR_RGB2BGR))
-    cv2.imwrite(style_path, cv2.cvtColor(style_img_np, cv2.COLOR_RGB2BGR))
-
-    # Run the main process (using fewer iterations for a faster demo)
-    final_tensor = run_style_transfer(content_path, style_path, iterations=500)
-    
-    # Convert tensor to displayable image
-    final_image = tensor_to_image(final_tensor)
-    
-    # Clean up temporary files
-    os.remove(content_path)
-    os.remove(style_path)
-
-    return final_image
-
-## 4. GRADIO UI DEFINITION ##
-
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🎨 Neural Style Transfer")
-    gr.Markdown("Combine the content of one image with the artistic style of another. This demo uses a VGG19 model. Processing can take a minute, especially on CPU.")
-
-    with gr.Row():
-        content_img = gr.Image(label="Content Image", type="numpy", value="https://gradio-builds.s3.amazonaws.com/demo-files/acropolis.jpg")
-        style_img = gr.Image(label="Style Image", type="numpy", value="https://gradio-builds.s3.amazonaws.com/demo-files/starry_night.jpg")
-    
-    run_button = gr.Button("Generate Image", variant="primary")
-    
-    output_img = gr.Image(label="Result")
-    
-    run_button.click(
-        fn=style_transfer_wrapper, 
-        inputs=[content_img, style_img], 
-        outputs=output_img
-    )
-    
-    gr.Markdown("---")
-    gr.Markdown("Based on the paper '[A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576)' by Gatys et al.")
-
-# Launch the Gradio app
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+import os
+from main import tensor_to_image,StyleContentModel, run_style_transfer
+
+# Define layers and instantiate the model once globally
+content_layers = ['block5_conv2'] 
+style_layers = ['block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1']
+extractor = StyleContentModel(style_layers, content_layers)
+
+def style_transfer_wrapper(content_img_np, style_img_np):
+    """
+    A wrapper to handle I/O for the Gradio interface.
+    Saves numpy arrays to temp files to use with the main function.
+    """
+    if content_img_np is None or style_img_np is None:
+        return None # Return None if either image is missing
+
+    # Save numpy arrays to temporary files
+    content_path = "temp_content.jpg"
+    style_path = "temp_style.jpg"
+    
+    # Gradio provides RGB, but cv2 saves in BGR order
+    cv2.imwrite(content_path, cv2.cvtColor(content_img_np, cv2.COLOR_RGB2BGR))
+    cv2.imwrite(style_path, cv2.cvtColor(style_img_np, cv2.COLOR_RGB2BGR))
+
+    # Run the main process (using fewer iterations for a faster demo)
+    final_tensor = run_style_transfer(content_path, style_path, iterations=500)
+    
+    # Convert tensor to displayable image
+    final_image = tensor_to_image(final_tensor)
+    
+    # Clean up temporary files
+    os.remove(content_path)
+    os.remove(style_path)
+
+    return final_image
+
+## 4. GRADIO UI DEFINITION ##
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🎨 Neural Style Transfer")
+    gr.Markdown("Combine the content of one image with the artistic style of another. This demo uses a VGG19 model. Processing can take a minute, especially on CPU.")
+
+    with gr.Row():
+        content_img = gr.Image(label="Content Image", type="numpy")
+        style_img = gr.Image(label="Style Image", type="numpy")
+    
+    run_button = gr.Button("Generate Image", variant="primary")
+    
+    output_img = gr.Image(label="Result")
+    
+    run_button.click(
+        fn=style_transfer_wrapper, 
+        inputs=[content_img, style_img], 
+        outputs=output_img
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("Based on the paper '[A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576)' by Gatys et al.")
+
+# Launch the Gradio app
 demo.launch(debug=True)