app.py

app.py

@@ -59,10 +59,10 @@ def predict(image_file, segmentation_png, bitmap_img):
   # fake_image = tf.squeeze(model.predict([latent_vector, final_img_list[2]]), axis=0)
   fake_image = model.predict([latent_vector, final_img_list[2]])
 
-  real_images = final_img_list
-
+  #real_images = final_img_list
+  fake = Image.fromarray((fake_image[0]+1)/2)
   # return tf.squeeze(real_images[1], axis=0), fake_image
-  return (fake_image[0]+1)/2
+  return fake
   
 # input
 input = [gr.inputs.Image(type="filepath", label="Ground Truth - Real Image (jpg)"), 
@@ -83,7 +83,7 @@ examples = [["/content/facades_data/cmp_b0010.jpg", "/content/facades_data/cmp_b
             ["/content/facades_data/cmp_b0050.jpg", "/content/facades_data/cmp_b0050.png", "/content/facades_data/cmp_b0050.bmp"]]
 
 # output
-output = [gr.outputs.Image(type="numpy", label="Generated - Conditioned Images")]
+output = [gr.outputs.Image(type="pil", label="Generated - Conditioned Images")]
 
 title = "GauGAN For Conditional Image Generation"
 description = "Upload an Image or take one from examples to generate realistic images that are conditioned on cue images and segmentation maps"