Update app.py

app.py

@@ -134,7 +134,8 @@ title = "GFPGAN: Practical Face Restoration Algorithm"
 description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
 It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
 To use it, simply upload your image.<br>
-If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
+If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊<br>
+This demo was forked by [vicalloy](https://github.com/vicalloy), add `face blur` param to optimize painting face enhance.
 """
 article = r"""
 
@@ -147,26 +148,31 @@ If you have any question, please email 📧 `[email protected]` or `xintao
 <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
 <center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center>
 """
-demo = gr.Interface(
-    inference, [
-        gr.inputs.Image(type="filepath", label="Input"),
-        # gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
-        gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", default='v1.4', label='version'),
-        gr.inputs.Number(label="Rescaling factor", default=2),
-        gr.inputs.Number(label="Blur face", default=25),
-        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
-    ], [
-        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
-        gr.outputs.File(label="Download the output image"),
-        gr.Gallery(label="Input faces").style(grid=[2], height="auto"),
-        gr.Gallery(label="Output faces").style(grid=[2], height="auto")
-    ],
-    title=title,
-    description=description,
-    article=article,
-    # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
-    #           ['10045.png', 'v1.4', 2, 50]]).launch()
-    examples=[['HanamichiSakuragi.jpg', 'v1.4', 2, 31], ['LiShiming.jpg', 'v1.4', 2, 3], ['QianLong.jpg', 'v1.4', 2, 3],
-              ['10045.png', 'v1.4', 2, 0]])
+with gr.Blocks() as demo:
+    gr.Markdown("<center><h1>%s</h1></center>" % title)
+    gr.Markdown(description)
+    with gr.Row(equal_height=False):
+        with gr.Column():
+            file_path = gr.components.Image(type="filepath", label="Input")
+            version = gr.components.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version')
+            rescaling_factor = gr.components.Number(label="Rescaling factor", value=2)
+            blur_face = gr.components.Number(label="Blur face", value=25)
+            submit = gr.Button("Submit")
+        with gr.Column():
+            output_img = gr.components.Image(type="numpy", label="Output (The whole image)")
+            download = gr.components.File(label="Download the output image")
+    with gr.Row():
+        with gr.Column():
+            input_faces = gr.Gallery(label="Input faces").style(grid=[2], height="auto")
+        with gr.Column():
+            output_faces = gr.Gallery(label="Output faces").style(grid=[2], height="auto")
+    gr.Examples([['HanamichiSakuragi.jpg', 'v1.4', 2, 31], ['LiShiming.jpg', 'v1.4', 2, 3], ['QianLong.jpg', 'v1.4', 2, 3],
+              ['10045.png', 'v1.4', 2, 0]], [file_path, version, rescaling_factor, blur_face])
+    gr.Markdown(article)
+    submit.click(
+        inference,
+        inputs=[file_path, version, rescaling_factor, blur_face],
+        outputs=[output_img, download, input_faces, output_faces]
+    )
 demo.queue(concurrency_count=4)
 demo.launch()