Show results from the other outputs of the model as well

app.py

@@ -81,8 +81,13 @@ def face2hero(
     else:
         input = torch.Tensor(aligned_img)
         results = model(input)
-        output = array_to_image(results[0].detach().numpy())
-        output = img_concat_h(array_to_image(aligned_img), output)
+        d1 = array_to_image(results[0].detach().numpy())
+        d2 = array_to_image(results[1].detach().numpy())
+        d3 = array_to_image(results[2].detach().numpy())
+        img_with_d2 = img_concat_h(array_to_image(aligned_img), d2)
+        d1_with_d3 = img_concat_h(d1, d3)
+        output = [img_with_d2, d1_with_d3]
+        del results
 
     return output
     
@@ -103,8 +108,8 @@ def inference(img):
       
   
 title = "Comics hero U2Net"
-description = "Turn a face into the face of a \"Comics hero\". Upload an image with a face, or click on one of the examples below. If a face could not be detected, an image will still be created."
+description = "Style transfer a face into one of a \"Comics hero\". Upload an image with a face, or click on one of the examples below. If a face could not be detected, an image will still be created."
 article = "<hr><p style='text-align: center'>See the <a href='https://github.com/Norod/U-2-Net-StyleTransfer' target='_blank'>Github Repo</a></p><p style='text-align: center'>samples: <img src='https://hf.space/gradioiframe/Norod78/ComicsHeroU2Net/file/Sample00001.jpg' alt='Sample00001'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroU2Net/file/Sample00002.jpg' alt='Sample00002'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroU2Net/file/Sample00003.jpg' alt='Sample00003'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroU2Net/file/Sample00004.jpg' alt='Sample00004'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroU2Net/file/Sample00005.jpg' alt='Sample00005'/></p><p>The \"Comics Hero (U2Net)\" model was trained by <a href='https://linktr.ee/Norod78' target='_blank'>Doron Adler</a></p>"
 
 examples=[['Example00001.jpg'],['Example00002.jpg'],['Example00003.jpg'],['Example00004.jpg'],['Example00005.jpg'], ['Example00006.jpg']]
-gr.Interface(inference, gr.inputs.Image(type="pil",shape=(1024,1024)), gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples,enable_queue=True,allow_flagging=False).launch()
+gr.Interface(inference, gr.inputs.Image(type="pil",shape=(1024,1024)), [gr.outputs.Image(type="pil"),gr.outputs.Image(type="pil")],title=title,description=description,article=article,examples=examples,enable_queue=True,allow_flagging=False).launch()