Update app.py

app.py

@@ -1,6 +1,10 @@
 import gradio as gr
 import numpy as np
+import tensorflow as tf
+import PIL
+import os
 
+'''
 def sepia(input_img):
   sepia_filter = np.array([[.393, .769, .189],
                            [.349, .686, .168],
@@ -8,7 +12,33 @@ def sepia(input_img):
   sepia_img = input_img.dot(sepia_filter.T)
   sepia_img /= sepia_img.max()                          
   return sepia_img
+ '''
+ 
+ def normalize_img(img):
+    img = tf.cast(img, dtype=tf.float32)
+    # Map values in the range [-1, 1]
+    return (img / 127.5) - 1.0
 
-iface = gr.Interface(sepia, gr.inputs.Image(shape=(200, 200)), "image")
+def predict_and_save(img, generator_model):
+    img = normalize_img(img)
+    prediction = generator_model(img, training=False)[0].numpy() 
+    prediction = (prediction * 127.5 + 127.5).astype(np.uint8)  
+    im = PIL.Image.fromarray(prediction)
+    return im
+    
+def run(image_path):
+    model = tf.keras.models.load_model('generator')  
+    img = tf.keras.preprocessing.image.load_img(
+        image_path, target_size=(256, 256)
+    )
+    
+    #https://www.tensorflow.org/api_docs/python/tf/keras/utils/load_img
+    
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0) 
+    
+    predict_and_save(img_array, model)
+
+iface = gr.Interface(sepia, gr.inputs.Image(shape=(256, 256)), "image")
 
 iface.launch(share = True)