Update app.py

app.py

@@ -197,63 +197,62 @@ DEFAULT_VALUES = {
     "color_fix_method": "adain"
 }
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=process_image,
-    inputs=[
-        gr.Image(label="Input Image"),
-        gr.Textbox(
-            label="Prompt", 
-            value=DEFAULT_VALUES["prompt"],
-        ),
-        gr.Textbox(
-            label="Negative Prompt", 
-            value=DEFAULT_VALUES["negative_prompt"],
-        ),
-        gr.Slider(
-            minimum=1.0, 
-            maximum=20.0, 
-            value=DEFAULT_VALUES["guidance_scale"],
-            label="Guidance Scale"
-        ),
-        gr.Slider(
-            minimum=0.1, 
-            maximum=2.0, 
-            value=DEFAULT_VALUES["conditioning_scale"],
-            label="Conditioning Scale"
-        ),
-        gr.Slider(
-            minimum=1, 
-            maximum=50, 
-            value=DEFAULT_VALUES["num_steps"],
-            step=1, 
-            label="Number of Steps"
-        ),
-        gr.Number(
-            label="Seed", 
-            value=DEFAULT_VALUES["seed"],
-        ),
-        gr.Slider(
-            minimum=1, 
-            maximum=4, 
-            value=DEFAULT_VALUES["upscale_factor"],
-            step=1, 
-            label="Upscale Factor"
-        ),
-        gr.Radio(
-            choices=["none", "wavelet", "adain"], 
-            label="Color Fix Method", 
-            value=DEFAULT_VALUES["color_fix_method"],
-        ),
-    ],
-    outputs=gr.Image(label="Generated Image"),
-    title="Controllable Conditional Super-Resolution",
-    description="Upload an image to enhance its resolution using CCSR."
-)
+# Create interface components
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="Input Image")
+            prompt = gr.Textbox(label="Prompt", value=DEFAULT_VALUES["prompt"])
+            negative_prompt = gr.Textbox(label="Negative Prompt", value=DEFAULT_VALUES["negative_prompt"])
+            guidance_scale = gr.Slider(minimum=1.0, maximum=20.0, value=DEFAULT_VALUES["guidance_scale"], label="Guidance Scale")
+            conditioning_scale = gr.Slider(minimum=0.1, maximum=2.0, value=DEFAULT_VALUES["conditioning_scale"], label="Conditioning Scale")
+            num_steps = gr.Slider(minimum=1, maximum=50, value=DEFAULT_VALUES["num_steps"], step=1, label="Number of Steps")
+            seed = gr.Number(label="Seed", value=DEFAULT_VALUES["seed"])
+            upscale_factor = gr.Slider(minimum=1, maximum=4, value=DEFAULT_VALUES["upscale_factor"], step=1, label="Upscale Factor")
+            color_fix_method = gr.Radio(choices=["none", "wavelet", "adain"], label="Color Fix Method", value=DEFAULT_VALUES["color_fix_method"])
+            
+            # Add buttons
+            with gr.Row():
+                clear_btn = gr.Button("Clear")
+                submit_btn = gr.Button("Submit", variant="primary")
 
-if __name__ == "__main__":
-    demo.launch()
+        with gr.Column():
+            output_image = gr.Image(label="Generated Image")
+
+    # Define submit action
+    submit_btn.click(
+        fn=process_image,
+        inputs=[
+            input_image, prompt, negative_prompt, guidance_scale,
+            conditioning_scale, num_steps, seed, upscale_factor,
+            color_fix_method
+        ],
+        outputs=output_image
+    )
+
+    # Define clear action that resets to default values
+    def reset_to_defaults():
+        return [
+            None,  # input_image
+            DEFAULT_VALUES["prompt"],
+            DEFAULT_VALUES["negative_prompt"],
+            DEFAULT_VALUES["guidance_scale"],
+            DEFAULT_VALUES["conditioning_scale"],
+            DEFAULT_VALUES["num_steps"],
+            DEFAULT_VALUES["seed"],
+            DEFAULT_VALUES["upscale_factor"],
+            DEFAULT_VALUES["color_fix_method"]
+        ]
 
+    clear_btn.click(
+        fn=reset_to_defaults,
+        inputs=None,
+        outputs=[
+            input_image, prompt, negative_prompt, guidance_scale,
+            conditioning_scale, num_steps, seed, upscale_factor,
+            color_fix_method
+        ]
+    )
 
 if __name__ == "__main__":
     demo.launch()