Update app.py

app.py

@@ -19,6 +19,10 @@ def compute(table: np.array):
     table = table[non_empty_row_mask]
     empty_mask = table == '(predict)'
     empty_inds = np.where(empty_mask)
+    if len(table.index) > 1024:
+        return "⚠️ **ERROR: TabPFN is not made for datasets with a trainingsize > 1024.**", None, None
+    if len(table.columns) > 100:
+        return "⚠️ **ERROR: TabPFN is not made for datasets with a feature size > 100.**", None, None
     if not len(empty_inds[0]):
         return "⚠️ **ERROR: Please leave at least one field blank for prediction.**", None, None
     if not np.all(empty_inds[1][0] == empty_inds[1]):
@@ -116,10 +120,10 @@ def update_table(table):
 
 headers = []
 
-gr.Markdown("""This demo allows you to play with the **TabPFN**.
-            The TabPFN will classify the values for all empty cells in the label column.
-            Please, provide everything but the label column as numeric values.
-            You can also upload datasets to fill the table automatically.
+gr.Markdown("""This demo allows you to experiment with the **TabPFN** model for tabular data.
+
+            If you remove values in the target column, TabPFN will make predictions on them after clicking on the Button. The first 10 target values were already removed for this example dataset, so TabPFN will predict the first 10 classes.
+            Please, provide everything but the targets as numeric values and only remove values in one column (the target column).
                 """)
 
 with gr.Blocks() as demo: