Update Gradio app with multiple files

app.py

@@ -49,75 +49,38 @@ optimize_pipeline_(pipe, image=[Image.new("RGB", (1024, 1024)), Image.new("RGB",
 
 MAX_SEED = np.iinfo(np.int32).max
 
-def build_light_restoration_prompt(brightness, contrast, exposure, saturation):
-    prompt_parts = []
-
-    # Brightness adjustment
-    if brightness > 0:
-        prompt_parts.append(f"Increase brightness by {brightness}%")
-    elif brightness < 0:
-        prompt_parts.append(f"Decrease brightness by {abs(brightness)}%")
-
-    # Contrast adjustment
-    if contrast > 0:
-        prompt_parts.append(f"Increase contrast by {contrast}%")
-    elif contrast < 0:
-        prompt_parts.append(f"Decrease contrast by {abs(contrast)}%")
-
-    # Exposure adjustment
-    if exposure > 0:
-        prompt_parts.append(f"Increase exposure by {exposure}%")
-    elif exposure < 0:
-        prompt_parts.append(f"Decrease exposure by {abs(exposure)}%")
-
-    # Saturation adjustment
-    if saturation > 0:
-        prompt_parts.append(f"Increase saturation by {saturation}%")
-    elif saturation < 0:
-        prompt_parts.append(f"Decrease saturation by {abs(saturation)}%")
-
-    final_prompt = ", ".join(prompt_parts).strip()
-    return final_prompt if final_prompt else "Restore image lighting"
+def build_light_restoration_prompt():
+    return "Remove the shadows and use soft lighting to relight the image"
 
 
 @spaces.GPU
 def infer_light_restoration(
     image,
-    brightness,
-    contrast,
-    exposure,
-    saturation,
     seed,
     randomize_seed,
     true_guidance_scale,
     num_inference_steps,
     height,
     width,
-    prev_output = None,
     progress=gr.Progress(track_tqdm=True)
 ):
-    prompt = build_light_restoration_prompt(brightness, contrast, exposure, saturation)
+    prompt = build_light_restoration_prompt()
     print(f"Generated Prompt: {prompt}")
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device=device).manual_seed(seed)
 
-    # Choose input image (prefer uploaded, else last output)
+    # Choose input image
     pil_images = []
     if image is not None:
         if isinstance(image, Image.Image):
             pil_images.append(image.convert("RGB"))
         elif hasattr(image, "name"):
             pil_images.append(Image.open(image.name).convert("RGB"))
-    elif prev_output:
-        pil_images.append(prev_output.convert("RGB"))
 
     if len(pil_images) == 0:
         raise gr.Error("Please upload an image first.")
-
-    if prompt == "Restore image lighting":
-        return image, seed, prompt
     
     result = pipe(
         image=pil_images,
@@ -162,8 +125,7 @@ css = '''
 }
 '''
 
-def reset_all():
-    return [0, 0, 0, 0]
+
 
 def update_dimensions_on_upload(image):
     if image is None:
@@ -189,9 +151,9 @@ def update_dimensions_on_upload(image):
 
 with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("# ✨ Light Restoration Studio")
+        gr.Markdown("# ✨ Shadow Removal & Relighting")
         gr.Markdown("""
-            Professional image light restoration powered by Qwen Image Edit 2509<br>
+            Remove shadows and apply soft lighting to your images<br>
             Using [dx8152's Light Restoration LoRA](https://huggingface.co/dx8152/Qwen-Image-Edit-2509-Light_restoration) 
             and [Phr00t/Qwen-Image-Edit-Rapid-AIO](https://huggingface.co/Phr00t/Qwen-Image-Edit-Rapid-AIO) for fast inference 💨<br>
             Built with [anycoder](https://huggingface.co/spaces/akhaliq/anycoder)
@@ -200,18 +162,9 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
 
         with gr.Row():
             with gr.Column(scale=1):
-                image = gr.Image(label="📸 Upload Image", type="pil", height=400)
-                prev_output = gr.Image(value=None, visible=False)
-
-                gr.Markdown("### 🎨 Light Adjustments")
-                brightness = gr.Slider(label="☀️ Brightness", minimum=-50, maximum=50, step=5, value=0)
-                contrast = gr.Slider(label="🌓 Contrast", minimum=-50, maximum=50, step=5, value=0)
-                exposure = gr.Slider(label="💡 Exposure", minimum=-50, maximum=50, step=5, value=0)
-                saturation = gr.Slider(label="🎨 Saturation", minimum=-50, maximum=50, step=5, value=0)
+                image = gr.Image(label="📸 Input Image", type="pil", height=500)
                 
-                with gr.Row():
-                    reset_btn = gr.Button("🔄 Reset", size="lg")
-                    run_btn = gr.Button("✨ Restore", variant="primary", size="lg")
+                run_btn = gr.Button("✨ Remove Shadows & Relight", variant="primary", size="lg")
 
                 with gr.Accordion("⚙️ Advanced Settings", open=False):
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
@@ -222,44 +175,27 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
                     width = gr.Slider(label="Width", minimum=256, maximum=2048, step=8, value=1024)
 
             with gr.Column(scale=1):
-                result = gr.Image(label="✨ Restored Image", interactive=False, height=400)
-                prompt_preview = gr.Textbox(label="Generated Prompt", interactive=False)
+                result = gr.Image(label="✨ Output Image", interactive=False, height=500)
+                prompt_preview = gr.Textbox(label="Prompt Used", interactive=False, value="Remove the shadows and use soft lighting to relight the image")
                     
     inputs = [
-        image, brightness, contrast, exposure, saturation,
-        seed, randomize_seed, true_guidance_scale, num_inference_steps, height, width, prev_output
+        image,
+        seed, randomize_seed, true_guidance_scale, num_inference_steps, height, width
     ]
     outputs = [result, seed, prompt_preview]
 
-    # Reset behavior
-    reset_btn.click(
-        fn=reset_all,
-        inputs=None,
-        outputs=[brightness, contrast, exposure, saturation],
-        queue=False
-    )
-
     # Manual generation
     run_btn.click(
         fn=infer_light_restoration, 
         inputs=inputs, 
         outputs=outputs
-    ).then(lambda img, *_: img, inputs=[result], outputs=[prev_output])
+    )
     
-    # Image upload triggers dimension update and control reset
+    # Image upload triggers dimension update
     image.upload(
         fn=update_dimensions_on_upload,
         inputs=[image],
         outputs=[width, height]
-    ).then(
-        fn=reset_all,
-        inputs=None,
-        outputs=[brightness, contrast, exposure, saturation],
-        queue=False
     )
 
-    # Live updates
-    for control in [brightness, contrast, exposure, saturation]:
-        control.release(fn=infer_light_restoration, inputs=inputs, outputs=outputs)
-
 demo.launch()

requirements.txt

@@ -7,4 +7,4 @@ dashscope
 kernels
 torchvision
 peft
-torchao==0.11.0
+torchao==0.11.0