Update app.py

app.py

@@ -79,39 +79,40 @@ with gr.Blocks(css=css) as demo:
 
     cmap = matplotlib.colormaps.get_cmap('Spectral_r')
 
-def on_submit(image):
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-
-    original_image = image.copy()
-
-    h, w = image.size  # For PIL images, use .size instead of .shape
-
-    depth = predict_depth(image)
-
-    # Debugging info
-    print("Type of depth:", type(depth))
-    print("Shape of depth:", depth.shape if isinstance(depth, np.ndarray) else "N/A")
-
-    if isinstance(depth, np.ndarray):
-        print("Data type of depth:", depth.dtype)
-
-    # Ensure depth is a NumPy array and has the correct shape and dtype
-    if not isinstance(depth, np.ndarray):
-        raise TypeError("Expected a NumPy array for depth, but got {}".format(type(depth)))
-
-    # Adjust the depth array if needed
-    if depth.ndim == 2:
-        # 2D array: expected for grayscale depth maps
-        depth = depth.astype('uint16')  # Convert to a suitable type
-    elif depth.ndim == 3 and depth.shape[2] == 1:
-        # 3D array with a single channel (e.g., shape (H, W, 1))
-        depth = depth[:, :, 0].astype('uint16')
-    else:
-        raise ValueError("Unsupported depth array shape: {}".format(depth.shape))
-
-    # Now convert to a PIL Image
-    raw_depth = Image.fromarray(depth)        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
+    def on_submit(image):
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+    
+        original_image = image.copy()
+    
+        h, w = image.size  # For PIL images, use .size instead of .shape
+    
+        depth = predict_depth(image)
+    
+        # Debugging info
+        print("Type of depth:", type(depth))
+        print("Shape of depth:", depth.shape if isinstance(depth, np.ndarray) else "N/A")
+    
+        if isinstance(depth, np.ndarray):
+            print("Data type of depth:", depth.dtype)
+    
+        # Ensure depth is a NumPy array and has the correct shape and dtype
+        if not isinstance(depth, np.ndarray):
+            raise TypeError("Expected a NumPy array for depth, but got {}".format(type(depth)))
+    
+        # Adjust the depth array if needed
+        if depth.ndim == 2:
+            # 2D array: expected for grayscale depth maps
+            depth = depth.astype('uint16')  # Convert to a suitable type
+        elif depth.ndim == 3 and depth.shape[2] == 1:
+            # 3D array with a single channel (e.g., shape (H, W, 1))
+            depth = depth[:, :, 0].astype('uint16')
+        else:
+            raise ValueError("Unsupported depth array shape: {}".format(depth.shape))
+    
+        # Now convert to a PIL Image
+        raw_depth = Image.fromarray(depth)        
+        tmp_raw_depth = tempfile.NamedTemporaryFile(suffix='.png', delete=False)
         raw_depth.save(tmp_raw_depth.name)
 
         depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0