Update app.py

app.py

@@ -1,3 +1,44 @@
+# '''NEURAL STYLE TRANSFER '''
+
+# import numpy as np
+# import tensorflow as tf
+# import tensorflow_hub as hub
+# import gradio as gr
+# from PIL import Image
+
+# np.set_printoptions(suppress=True)
+# model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+
+# def tensor_to_image(tensor):
+#     tensor *= 255
+#     tensor = np.array(tensor, dtype=np.uint8)
+#     if tensor.ndim > 3:
+#         tensor = tensor[0]
+#     return Image.fromarray(tensor)
+
+# def transform_my_model(content_image, style_image):
+#     content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.0
+#     style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.0
+#     stylized_image = model(tf.constant(content_image), tf.constant(style_image))[0]
+#     return tensor_to_image(stylized_image)
+
+# demo = gr.Interface(
+#     fn=transform_my_model,
+#     inputs=[gr.Image(label="Content Image"), gr.Image(label="Style Image")],
+#     outputs=gr.Image(label="Result"),
+#     title="Style Transfer",
+#     examples=[
+#         ["Content_Images/contnt12.jpg", "VG516.jpg"],
+#         ["Content_Images/contnt2.jpg", "Content_Images/styl9.jpg"],
+#         ["Content_Images/contnt.jpg", "Content_Images/styl22.jpg"]
+#     ],
+#     article="References-\n\nExploring the structure of a real-time, arbitrary neural artistic stylization network. Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin."
+# )
+
+# demo.launch(share=True)
+
+
+
 '''NEURAL STYLE TRANSFER '''
 
 import numpy as np
@@ -5,34 +46,75 @@ import tensorflow as tf
 import tensorflow_hub as hub
 import gradio as gr
 from PIL import Image
+import os
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 np.set_printoptions(suppress=True)
-model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+
+# Load model with error handling
+try:
+    logger.info("Loading TensorFlow Hub model...")
+    model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+    logger.info("Model loaded successfully!")
+except Exception as e:
+    logger.error(f"Error loading model: {str(e)}")
+    raise
 
 def tensor_to_image(tensor):
-    tensor *= 255
-    tensor = np.array(tensor, dtype=np.uint8)
-    if tensor.ndim > 3:
-        tensor = tensor[0]
-    return Image.fromarray(tensor)
+    try:
+        tensor *= 255
+        tensor = np.array(tensor, dtype=np.uint8)
+        if tensor.ndim > 3:
+            tensor = tensor[0]
+        return Image.fromarray(tensor)
+    except Exception as e:
+        logger.error(f"Error in tensor_to_image: {str(e)}")
+        raise
 
 def transform_my_model(content_image, style_image):
-    content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.0
-    style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.0
-    stylized_image = model(tf.constant(content_image), tf.constant(style_image))[0]
-    return tensor_to_image(stylized_image)
+    try:
+        if content_image is None or style_image is None:
+            raise ValueError("Both content and style images are required")
+            
+        logger.info("Processing images...")
+        content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.0
+        style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.0
+        stylized_image = model(tf.constant(content_image), tf.constant(style_image))[0]
+        logger.info("Style transfer completed successfully!")
+        return tensor_to_image(stylized_image)
+    except Exception as e:
+        logger.error(f"Error in transform_my_model: {str(e)}")
+        raise
+
+# Verify example images exist
+example_images = [
+    ["Content_Images/contnt12.jpg", "VG516.jpg"],
+    ["Content_Images/contnt2.jpg", "Content_Images/styl9.jpg"],
+    ["Content_Images/contnt.jpg", "Content_Images/styl22.jpg"]
+]
+
+valid_examples = []
+for content_path, style_path in example_images:
+    if os.path.exists(content_path) and os.path.exists(style_path):
+        valid_examples.append([content_path, style_path])
+    else:
+        logger.warning(f"Example image not found: {content_path} or {style_path}")
 
 demo = gr.Interface(
     fn=transform_my_model,
     inputs=[gr.Image(label="Content Image"), gr.Image(label="Style Image")],
     outputs=gr.Image(label="Result"),
-    title="Style Transfer",
-    examples=[
-        ["Content_Images/contnt12.jpg", "VG516.jpg"],
-        ["Content_Images/contnt2.jpg", "Content_Images/styl9.jpg"],
-        ["Content_Images/contnt.jpg", "Content_Images/styl22.jpg"]
-    ],
+    title="Neural Style Transfer",
+    description="Upload a content image and a style image to create a stylized version of your content image.",
+    examples=valid_examples if valid_examples else None,
     article="References-\n\nExploring the structure of a real-time, arbitrary neural artistic stylization network. Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin."
 )
 
-demo.launch(share=True)
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
+
+