Adds precise docstrings to main func and enables MCP server mode

app.py

@@ -146,10 +146,26 @@ def run_with_upload(
     cy_norm: float,
 ):
     """
-    - upscale_option: "1x" / "2x" / "4x"
-    - cx_norm, cy_norm: normalized center coordinates in [0,1]
-    The underlying `recursive_multiscale_sr` expects a list of centers
-    of length rec_num (default 4). We replicate (cx_norm, cy_norm) four times.
+    Perform chain-of-zoom super-resolution on a given image, using recursive multi-scale upscaling centered on a specific point.
+
+    This function enhances a given image by progressively zooming into a specific point, using a recursive deep super-resolution model.
+    
+    Args:
+        uploaded_image_path (str): Path to the input image file on disk.
+        upscale_option (str): The desired upscale factor as a string. Valid options are "1x", "2x", and "4x".
+            - "1x" means no upscaling.
+            - "2x" means 2× enlargement per zoom step.
+            - "4x" means 4× enlargement per zoom step.
+        cx_norm (float): Normalized X-coordinate (0 to 1) of the zoom center.
+        cy_norm (float): Normalized Y-coordinate (0 to 1) of the zoom center.
+
+    Returns:
+        list[PIL.Image.Image]: A list of progressively zoomed-in and super-resolved images at each recursion step (typically 4),
+        centered around the user-specified point.
+        
+    Note:
+        The center point is repeated for each recursion level to maintain consistency during zooming.
+        This function uses a modified version of the `recursive_multiscale_sr` pipeline for inference.
     """
     if uploaded_image_path is None:
         return []
@@ -317,4 +333,4 @@ with gr.Blocks(css=css) as demo:
 demo.queue(default_concurrency_limit=1,        # ≤ 1 worker per event
            max_size=20)                        # optional: allow 20 waiting jobs
 
-demo.launch(share=True)
+demo.launch(share=True, mcp_server=True)