app.py

app.py

@@ -11,20 +11,15 @@ from huggingface_hub import from_pretrained_keras
 
 model = from_pretrained_keras("RobotJelly/GauGAN-Image-generation")
 
-def predict(image_file):
-  # print(image_file)
-  # img = Image.open(image_file)
-  # image_file = str(img)
-  print("image_file-->", tf.io.read_file(image_file))
-
-  image_list = []
-
-  segmentation_map = image_file.replace("images", "segmentation_map").replace("jpg", "png")
+def predict(image_file, segmentation_png, bitmap_img):
+  image_list = [segmentation_png, image_file, bitmap_img]
+  
+  #segmentation_map = image_file.replace("images", "segmentation_map").replace("jpg", "png")
 
-  labels = image_file.replace("images", "segmentation_labels").replace("jpg", "bmp")
-  print("labels", labels)
+  #labels = image_file.replace("images", "segmentation_labels").replace("jpg", "bmp")
+  #print("labels", labels)
 
-  image_list = [segmentation_map, image_file, labels]
+  #image_list = [segmentation_map, image_file, labels]
 
   image = tf.image.decode_png(tf.io.read_file(image_list[1]), channels=3)
   image = tf.cast(image, tf.float32) / 127.5 - 1
@@ -67,10 +62,12 @@ def predict(image_file):
   real_images = final_img_list
 
   # return tf.squeeze(real_images[1], axis=0), fake_image
-  return [(real_images[0][0]+1)/2, (fake_image[0]+1)/2]
+  return (fake_image[0]+1)/2
   
 # input
-input = [gr.inputs.Image(type="filepath", label="Ground Truth - Real Image")]
+input = [gr.inputs.Image(type="filepath", label="Ground Truth - Real Image (jpg)"), 
+         gr.inputs.Image(type="filepath", label="Segementated image (png)"),
+         gr.inputs.Image(type="filepath", label="corresponding bitmap image (bmp)")]
 
 facades_data = []
 data_dir = 'examples/'
@@ -80,7 +77,7 @@ for idx, images in enumerate(os.listdir(data_dir)):
     facades_data.append(image)
 
 # output
-output = [gr.outputs.Image(type="numpy", label="Mask/Segmentation used"), gr.outputs.Image(type="numpy", label="Generated - Conditioned Images")]
+output = [gr.outputs.Image(type="numpy", 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"