feat: check for min>=max

app.py

@@ -679,6 +679,7 @@ def _():
         step=0.05,
         value=0.25,
         show_value=True,
+        include_input=True,
         label="Minimum proportion of samples feature appears in",
     )
     max_df_setting = mo.ui.slider(
@@ -687,6 +688,7 @@ def _():
         step=0.05,
         value=0.8,
         show_value=True,
+        include_input=True,
         label="Maximum proportion of samples feature appears in",
     )
     max_features_setting = mo.ui.slider(
@@ -695,6 +697,7 @@ def _():
         step=1,
         value=100,
         show_value=True,
+        include_input=True,
         label="Maximum number of features to use",
     )
 
@@ -711,6 +714,15 @@ def _():
     return max_df_setting, max_features_setting, min_df_setting
 
 
+@app.cell
+def _(max_df_setting, min_df_setting):
+    min_max_check = None
+    if max_df_setting.value <= min_df_setting.value:
+        min_max_check = mo.md(f"**Error**: minimum value {min_df_setting.value} must be smaller then maximum value {max_df_setting.value}.\n\nChange the sliders so that the min is smaller than the max.").callout(kind="danger")
+    min_max_check
+    return (min_max_check,)
+
+
 @app.cell
 def stopword_switch():
     stop_filter = mo.ui.switch(label="Enable stop-word filtering?", value=False)
@@ -781,10 +793,13 @@ def _(
     max_df_setting,
     max_features_setting,
     min_df_setting,
+    min_max_check,
     sw: set[str],
     texts,
     train_scikit_cached,
 ):
+    mo.stop(min_max_check is not None)
+
     scikit_corpus, tfidf_X, vectorizer, chunk_cats, chunk_fnames = train_scikit_cached(
         texts,
         cats,