app.py

@@ -23,7 +23,8 @@ def inference(file, mode):
         im = cv2.imread(file, cv2.IMREAD_COLOR)
         faceenhancer = FaceEnhancement(size=model['size'], model=model['name'], channel_multiplier=2, device='cpu')
         img, orig_faces, enhanced_faces = faceenhancer.process(im)
-        return img
+        cv2.imwrite(os.path.join("output.png"), img)
+        return os.path.join("output.png")
     else:
         model = {'name':'GPEN-1024-Color', 'size':1024}
         grayf = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
@@ -32,7 +33,8 @@ def inference(file, mode):
         colorf = facecolorizer.process(grayf)
 
         colorf = cv2.resize(colorf, (grayf.shape[1], grayf.shape[0]))
-        return colorf
+        cv2.imwrite(os.path.join("output.png"), colorf)
+        return os.path.join("output.png")
         
 title = "GPEN"
 description = "Gradio demo for GAN Prior Embedded Network for Blind Face Restoration in the Wild. This version of gradio demo includes face colorization from GPEN. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
@@ -42,8 +44,8 @@ article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2105.060
 
 gr.Interface(
     inference, 
-    [gr.inputs.Image(type="filepath", label="Input"),gr.inputs.Radio(["enhance","colorize"], type="value", default="enhance", label="model type")], 
-    gr.outputs.Image(type="numpy", label="Output"),
+    [gr.inputs.Image(type="filepath", label="Input"),gr.inputs.Radio(["enhance","colorize"], type="value", default="enhance", label="Type")], 
+    gr.outputs.Image(type="file", label="Output"),
     title=title,
     description=description,
     article=article,