remove to numpy image function

app.py

@@ -29,7 +29,6 @@ import traceback
 from inference import (
     load_curia_dataset,
     load_id_to_labels,
-    to_numpy_image,
     infer_image,
 )
 
@@ -363,7 +362,7 @@ def sample_dataset_example(
     dataset = load_curia_dataset(subset)
     index = pick_random_indices(dataset, target_id)
     record = dataset[index]
-    image = to_numpy_image(record["image"])
+    image = np.array(record["image"]).astype(np.float32)
     mask_array = record.get("mask")
 
     meta = {
@@ -467,7 +466,10 @@ def handle_upload_preview(
         return None, "Please upload an image.", "", pd.DataFrame(), gr.update(visible=False), None
 
     try:
-        np_image = to_numpy_image(image)
+        np_image = np.array(image).astype(np.float32)
+        if np_image.ndim == 3: # RGB image
+            # convert to grayscale
+            np_image = np_image.mean(axis=-1)
 
         # Apply windowing only for display, keep raw image for model inference
         display = to_display_image(np_image)

inference.py

@@ -143,25 +143,6 @@ def load_curia_dataset(subset: str) -> Any:
         return ds["test"]
     return ds
 
-
-def to_numpy_image(image: Any) -> np.ndarray:
-    """Convert dataset or user-provided imagery to a float32 numpy array."""
-
-    if isinstance(image, np.ndarray):
-        arr = image
-    elif isinstance(image, Image.Image):
-        arr = np.array(image)
-    else:
-        # Some datasets provide nested dicts or lists – attempt to coerce.
-        arr = np.array(image)
-
-    if arr.ndim == 3 and arr.shape[-1] == 3:
-        # Convert RGB to grayscale by averaging channels
-        arr = arr.mean(axis=-1)
-
-    return arr.astype(np.float32)
-
-
 def infer_image(
     image: np.ndarray,
     head: str,