print image path

app.py

@@ -29,6 +29,9 @@ configs.aligned = False
 sampler_dist = DifIRSampler(configs)
 
 def predict(im_path, background_enhance, face_upsample, upscale, started_timesteps):
+    assert isinstance(im_path, str)
+    print(f'Processing image: {im_path}...')
+
     configs.background_enhance = background_enhance
     configs.face_upsample = face_upsample
     started_timesteps = int(started_timesteps)
@@ -74,11 +77,8 @@ def predict(im_path, background_enhance, face_upsample, upscale, started_timeste
 
     return image_restored, str(save_path)
 
-# im_path = './testdata/whole_imgs/00.jpg'
-# predict(im_path, True, True, 3, 100)
-
 title = "DifFace: Blind Face Restoration with Diffused Error Contraction"
-description = r"""<center><img src='./assets/DifFace_Framework.png' alt='DifFace logo'></center>
+description = r"""<center><img src='https://github.com/zsyOAOA/DifFace/blob/master/assets/DifFace_Framework.png' alt='DifFace logo'></center>
 <b>Official Gradio demo</b> for <a href='https://github.com/zsyOAOA/DifFace' target='_blank'><b>DifFace: Blind Face Restoration with Diffused Error Contraction</b></a>.<br>
 🔥 DifFace is a robust face restoration algorithm for old or corrupted photos.<br>
 """
@@ -114,13 +114,13 @@ demo = gr.Interface(
     predict,
     inputs=[
         gr.Image(type="filepath", label="Input"),
-        gr.Checkbox(default=True, label="Background_Enhance"),
-        gr.Checkbox(default=True, label="Face_Upsample"),
-        gr.Number(default=2, label="Rescaling_Factor (up to 4)"),
+        gr.Checkbox(value=True, label="Background_Enhance"),
+        gr.Checkbox(value=True, label="Face_Upsample"),
+        gr.Number(value=2, label="Rescaling_Factor (up to 4)"),
         gr.Slider(1, 200, value=100, step=10, label='Realism-Fidelity Trade-off')
     ],
     outputs=[
-        gr.outputs.Image(type="numpy", label="Output"),
+        gr.Image(type="numpy", label="Output"),
         gr.outputs.File(label="Download the output")
     ],
     title=title,