Update app.py

app.py

@@ -131,20 +131,54 @@ description = "Gradio Demo for PASD Real-ISR. To use it, simply upload your imag
 article = "<p style='text-align: center'><a href='https://github.com/yangxy/PASD' target='_blank'>Github Repo Pytorch</a></p>"
 #examples=[['samples/27d38eeb2dbbe7c9.png'],['samples/629e4da70703193b.png']]
 
-demo = gr.Interface(
-    fn=inference, 
-    inputs=[gr.Image(type="pil", sources=["upload"]),
-            gr.Textbox(label="Prompt", value="Asian"),
-            gr.Textbox(label="Added Prompt", value='clean, high-resolution, 8k, best quality, masterpiece'),
-            gr.Textbox(label="Negative Prompt",value='dotted, noise, blur, lowres, oversmooth, longbody, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'),
-            gr.Slider(label="Denoise Steps", minimum=10, maximum=50, value=20, step=1),
-            gr.Slider(label="Upsample Scale", minimum=1, maximum=4, value=2, step=1),
-            gr.Slider(label="Conditioning Scale", minimum=0.5, maximum=1.5, value=1.1, step=0.1),
-            gr.Slider(label="Classier-free Guidance", minimum=0.1, maximum=10.0, value=7.5, step=0.1),
-            gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)],
-    outputs=[ImageSlider(position=0.5), gr.File()],
-    title=title,
-    description=description,
-    article=article).queue()
-
-demo.launch()
+css = """
+#col-container{
+    margin: 0 auto;
+    max-width: 720px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        with gr.HTML(f"""
+        <h2 style="text-align: center;>
+            {title}
+        </h2>
+        <p style="text-align: center;>
+            {description} <br />
+            {article}
+        </p>
+        
+        """)
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(type="pil", sources=["upload"])
+                prompt_in = gr.Textbox(label="Prompt", value="Asian")
+                with gr.Accordion(label="Advanced settings", open=False):
+                    added_prompt = gr.Textbox(label="Added Prompt", value='clean, high-resolution, 8k, best quality, masterpiece'),
+                    neg_promp = gr.Textbox(label="Negative Prompt",value='dotted, noise, blur, lowres, oversmooth, longbody, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'),
+                    denoise_steps = gr.Slider(label="Denoise Steps", minimum=10, maximum=50, value=20, step=1),
+                    upsample_scale = gr.Slider(label="Upsample Scale", minimum=1, maximum=4, value=2, step=1),
+                    condition_scale = gr.Slider(label="Conditioning Scale", minimum=0.5, maximum=1.5, value=1.1, step=0.1),
+                    classifier_free_guidance = gr.Slider(label="Classier-free Guidance", minimum=0.1, maximum=10.0, value=7.5, step=0.1),
+                    seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)]
+                submit_btn = gr.Button("Submit")
+            with gr.Column():
+                b_a_slider = ImageSlider(label="B/A result", position=0.5)
+                file_output = gr.File(label="Downloadable image result")
+
+    submit_btn.click(
+        fn = inference,
+        inputs = [
+            input_image, prompt_in,
+            added_prompt, neg_prompt,
+            denoise_steps,
+            upsample_scale, condition_scale,
+            clasifier_free_guidance, seed
+        ],
+        outputs = [
+            b_a_slider,
+            file_output
+        ]
+    )
+demo.queue().launch()