Final code

app.py

@@ -17,7 +17,7 @@ def compute(table: np.array):
     vfunc = np.vectorize(lambda s: len(str(s)))
     non_empty_row_mask = (vfunc(table).sum(1) != 0)
     table = table[non_empty_row_mask]
-    empty_mask = table == '(predict)'
+    empty_mask = table == ''
     empty_inds = np.where(empty_mask)
     if table.shape[0] > 1024:
         return "⚠️ **ERROR: TabPFN is not made for datasets with a trainingsize > 1024.**", None, None