debug

app.py

@@ -1,14 +1,35 @@
 import gradio as gr
 import tensorflow as tf
 import tensorflow_hub as hub
+import matplotlib.pylab as plt
+import numpy as np
 
 hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
 
-def stylize(content_path, style_path):
-    content_image = load_img(content_path)
-    style_image = load_img(style_path)
+def stylize(content_image_path, style_image_path):
+    content_image = plt.imread(content_image_path)
+    style_image = plt.imread(style_image_path)
+    content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
+    style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
+    style_image = tf.image.resize(style_image, (256, 256))
+    
     stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
     return tensor_to_image(stylized_image)
+    
+    
+# Load content and style images (see example in the attached colab).
+#content_image = plt.imread(content_image_path)
+#style_image = plt.imread(style_image_path)
+
+# Convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. Example using numpy:
+#content_image = content_image.astype(np.float32)[np.newaxis, ...] / 255.
+#style_image = style_image.astype(np.float32)[np.newaxis, ...] / 255.
+
+# Optionally resize the images. It is recommended that the style image is about
+# 256 pixels (this size was used when training the style transfer network).
+# The content image can be any size.
+# style_image = tf.image.resize(style_image, (256, 256))
+
 
 iface = gr.Interface(fn=stylize, inputs=["image", "image"], outputs="image")
 iface.launch()