Refactor app.py: improve code formatting and simplify docstring

- Add proper line breaks and formatting throughout the file
- Simplify the infer() function docstring for better clarity
- Improve code style consistency (spacing, line breaks)
- No functional changes, only formatting improvements

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

app.py

@@ -10,56 +10,46 @@ from diffusers.utils import load_image
 
 MAX_SEED = np.iinfo(np.int32).max
 
-pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
+pipe = FluxKontextPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16
+).to("cuda")
+
 
 @spaces.GPU
-def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(
+    input_image,
+    prompt,
+    seed=42,
+    randomize_seed=False,
+    guidance_scale=2.5,
+    steps=28,
+    progress=gr.Progress(track_tqdm=True),
+):
     """
-    Perform image editing using the FLUX.1 Kontext pipeline.
-    
-    This function takes an input image and a text prompt to generate a modified version
-    of the image based on the provided instructions. It uses the FLUX.1 Kontext model
-    for contextual image editing tasks.
-    
+    Edit an image using AI based on text instructions.
+
     Args:
-        input_image (PIL.Image.Image): The input image to be edited. Will be converted
-            to RGB format if not already in that format.
-        prompt (str): Text description of the desired edit to apply to the image.
-            Examples: "Remove glasses", "Add a hat", "Change background to beach".
-        seed (int, optional): Random seed for reproducible generation. Defaults to 42.
-            Must be between 0 and MAX_SEED (2^31 - 1).
-        randomize_seed (bool, optional): If True, generates a random seed instead of
-            using the provided seed value. Defaults to False.
-        guidance_scale (float, optional): Controls how closely the model follows the
-            prompt. Higher values mean stronger adherence to the prompt but may reduce
-            image quality. Range: 1.0-10.0. Defaults to 2.5.
-        steps (int, optional): Controls how many steps to run the diffusion model for.
-            Range: 1-30. Defaults to 28.
-        progress (gr.Progress, optional): Gradio progress tracker for monitoring
-            generation progress. Defaults to gr.Progress(track_tqdm=True).
-    
+        input_image (optional): Path to the image file to edit (if None, generates from text only)
+        prompt (required): Text describing what to change (e.g. "remove glasses", "add a hat", "change background to beach")
+        seed (optional): Random seed for reproducibility (default: 42)
+        randomize_seed (optional): Use random seed instead of fixed seed (default: False)
+        guidance_scale (optional): How closely to follow the prompt, 1.0-10.0 (default: 2.5)
+        steps (optional): Number of generation steps, 1-30 (default: 28)
+        progress (optional): Gradio progress tracker (automatically provided)
+
     Returns:
-        tuple: A 3-tuple containing:
-            - PIL.Image.Image: The generated/edited image
-            - int: The seed value used for generation (useful when randomize_seed=True)
-            - gr.update: Gradio update object to make the reuse button visible
-    
+        tuple: (edited_image, seed_used, gradio_update)
+
     Example:
-        >>> edited_image, used_seed, button_update = infer(
-        ...     input_image=my_image,
-        ...     prompt="Add sunglasses",
-        ...     seed=123,
-        ...     randomize_seed=False,
-        ...     guidance_scale=2.5
-        ... )
+        infer(input_image="/path/to/photo.jpg", prompt="Add sunglasses")
     """
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    
+
     if input_image:
         input_image = input_image.convert("RGB")
         image = pipe(
-            image=input_image, 
+            image=input_image,
             prompt=prompt,
             guidance_scale=guidance_scale,
             num_inference_steps=steps,
@@ -74,7 +64,8 @@ def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5
         ).images[0]
     return image, seed, gr.update(visible=True)
 
-css="""
+
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 960px;
@@ -82,11 +73,13 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
+
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 Kontext [dev]
+        gr.Markdown(
+            f"""# FLUX.1 Kontext [dev]
 Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro], [[blog]](https://bfl.ai/announcements/flux-1-kontext-dev) [[model]](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)
-        """)
+        """
+        )
         with gr.Row():
             with gr.Column():
                 input_image = gr.Image(label="Upload the image for editing", type="pil")
@@ -100,7 +93,7 @@ Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro
                     )
                     run_button = gr.Button("Run", scale=0)
                 with gr.Accordion("Advanced Settings", open=False):
-                    
+
                     seed = gr.Slider(
                         label="Seed",
                         minimum=0,
@@ -108,40 +101,31 @@ Image editing and manipulation model guidance-distilled from FLUX.1 Kontext [pro
                         step=1,
                         value=0,
                     )
-                    
+
                     randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                    
+
                     guidance_scale = gr.Slider(
                         label="Guidance Scale",
                         minimum=1,
                         maximum=10,
                         step=0.1,
                         value=2.5,
-                    )       
-                    
+                    )
+
                     steps = gr.Slider(
-                        label="Steps",
-                        minimum=1,
-                        maximum=30,
-                        value=28,
-                        step=1
+                        label="Steps", minimum=1, maximum=30, value=28, step=1
                     )
-                    
+
             with gr.Column():
                 result = gr.Image(label="Result", show_label=False, interactive=False)
                 reuse_button = gr.Button("Reuse this image", visible=False)
-        
-        
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [input_image, prompt, seed, randomize_seed, guidance_scale, steps],
-        outputs = [result, seed, reuse_button]
-    )
-    reuse_button.click(
-        fn = lambda image: image,
-        inputs = [result],
-        outputs = [input_image]
+        fn=infer,
+        inputs=[input_image, prompt, seed, randomize_seed, guidance_scale, steps],
+        outputs=[result, seed, reuse_button],
     )
+    reuse_button.click(fn=lambda image: image, inputs=[result], outputs=[input_image])
 
-demo.launch(mcp_server=True)
+demo.launch(mcp_server=True)