update app

app.py

@@ -11,7 +11,6 @@ from diffusers import FluxKontextPipeline
 from diffusers.utils import load_image
 from huggingface_hub import hf_hub_download
 from aura_sr import AuraSR
-from gradio_imageslider import ImageSlider
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -117,7 +116,7 @@ aura_sr = AuraSR.from_pretrained("fal/AuraSR-v2")
 @spaces.GPU
 def infer(input_image, prompt, lora_adapter, upscale_image, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
     """
-    Perform image editing and optional upscaling, returning a pair for the ImageSlider.
+    Perform image editing and optional upscaling, returning the final image.
     """
     if not input_image:
         raise gr.Error("Please upload an image for editing.")
@@ -156,15 +155,15 @@ def infer(input_image, prompt, lora_adapter, upscale_image, seed=42, randomize_s
         progress(0.8, desc="Upscaling image...")
         image = aura_sr.upscale_4x(image)
 
-    return (original_image, image), seed, gr.Button(visible=True)
+    return image, seed, gr.Button(visible=True)
 
 @spaces.GPU
 def infer_example(input_image, prompt, lora_adapter):
     """
-    Wrapper function for gr.Examples to call the main infer logic for the slider.
+    Wrapper function for gr.Examples.
     """
-    (original_image, generated_image), seed, _ = infer(input_image, prompt, lora_adapter, upscale_image=False)
-    return (original_image, generated_image), seed
+    image, seed, _ = infer(input_image, prompt, lora_adapter, upscale_image=False)
+    return image, seed
 
 css="""
 #col-container {
@@ -174,7 +173,7 @@ css="""
 #main-title h1 {font-size: 2.1em !important;}
 """
 
-with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
+with gr.Blocks() as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown("# **Photo-Mate-i2i**", elem_id="main-title")
@@ -221,7 +220,7 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
                     )
                     
             with gr.Column():
-                output_slider = ImageSlider(label="Before / After", show_label=False, interactive=False)
+                output_image = gr.Image(label="Output Image", interactive=False)
                 reuse_button = gr.Button("Reuse this image", visible=False)
                 
                 with gr.Row():
@@ -246,11 +245,9 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
                 ["unblur/1.jpg", "[photo content], upscale the low-quality image to 4K resolution, enhancing sharpness, clarity, and fine details while preserving the original texture, colors, lighting, and natural appearance. Remove noise, blur, and compression artifacts without over-smoothing or distorting facial or object features. Ensure realistic depth, balanced contrast, and accurate tones, achieving a high-definition, lifelike result that maintains the integrity of the original image.", "Kontext-Unblur-Upscale"],
                 ["pencil/1.png", "[photo content], replicate the image as a pencil illustration, black and white, with sketch-like detailing.", "MonochromePencil"],
                 ["unblur/11.jpg", "[photo content], upscale the low-quality image to 4K resolution, enhancing sharpness, clarity, and fine details while preserving the original texture, colors, lighting, and natural appearance. Remove noise, blur, and compression artifacts without over-smoothing or distorting facial or object features. Ensure realistic depth, balanced contrast, and accurate tones, achieving a high-definition, lifelike result that maintains the integrity of the original image.", "Kontext-Unblur-Upscale"],
-
-            
             ],
             inputs=[input_image, prompt, lora_adapter],
-            outputs=[output_slider, seed],
+            outputs=[output_image, seed],
             fn=infer_example,
             cache_examples="lazy",
             label="Examples"
@@ -260,13 +257,13 @@ with gr.Blocks(css=css, theme=steel_blue_theme) as demo:
         triggers=[run_button.click, prompt.submit],
         fn=infer,
         inputs=[input_image, prompt, lora_adapter, upscale_checkbox, seed, randomize_seed, guidance_scale, steps],
-        outputs=[output_slider, seed, reuse_button]
+        outputs=[output_image, seed, reuse_button]
     )
     
     reuse_button.click(
-        fn=lambda images: images[1] if isinstance(images, (list, tuple)) and len(images) > 1 else images,
-        inputs=[output_slider],
+        fn=lambda x: x,
+        inputs=[output_image],
         outputs=[input_image]
     )
 
-demo.launch(mcp_server=True, ssr_mode=False, show_error=True)
+demo.launch(css=css, theme=steel_blue_theme, mcp_server=True, ssr_mode=False, show_error=True)