revert predict wrapper

app.py

@@ -10,19 +10,9 @@ X, y = load_linnerud(return_X_y=True, as_frame=True)
 regr = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y)
 # example usage: regr.predict(X.iloc[[0]])
 
-
-def predict(X):
-    max_rows = 100000
-    if X.shape[0] > max_rows:
-        raise ValueError(
-            f"Too many rows ({X.shape[0]}), please use less than {max_rows} rows."
-        )
-    return regr.predict(X)
-
-
 iface = gr.Interface(
     title="MultiOutputRegressor Example",
-    fn=predict,
+    fn=regr.predict,
     inputs=gr.Dataframe(
         value=X.head(1),
         headers=list(X.columns),
@@ -38,3 +28,13 @@ iface = gr.Interface(
     ),
 )
 iface.launch()
+
+# %% Code Graveyard
+
+# def predict(X):
+#     max_rows = 100000
+#     if X.shape[0] > max_rows:
+#         raise ValueError(
+#             f"Too many rows ({X.shape[0]}), please use less than {max_rows} rows."
+#         )
+#     return regr.predict(X)