Update app.py

app.py

@@ -22,27 +22,20 @@ def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5
     for contextual image editing tasks.
     
     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.
+        input_image (PIL.Image.Image): The path to the input image to be edited.
+        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. 
+            Must be between 0 and MAX_SEED (2^31 - 1). Defaults to 42.
+        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).
     
     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
+        The modified image and seed used for generation.
     
     Example:
         >>> edited_image, used_seed, button_update = infer(