Update app.py

app.py

@@ -243,16 +243,18 @@ def train_regressor(dataset_key, feature_columns, test_size, n_estimators, max_d
     y = train_df["meyer_wallach"]
 
     X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=test_size, random_state=random_state
+        X, y, test_size=test_size, random_state=int(random_state)
     )
 
+    max_depth_value = int(max_depth) if max_depth is not None else None
+
     model = Pipeline([
         ("imputer", SimpleImputer()),
         ("scaler", StandardScaler()),
         ("regressor", RandomForestRegressor(
-            n_estimators=n_estimators,
-            max_depth=max_depth,
-            random_state=random_state,
+            n_estimators=int(n_estimators),
+            max_depth=max_depth_value,
+            random_state=int(random_state),
             n_jobs=-1
         ))
     ])
@@ -307,7 +309,7 @@ with gr.Blocks(title=APP_TITLE) as demo:
 
             test_size = gr.Slider(0.1, 0.4, value=0.2, label="Test split")
             n_estimators = gr.Slider(50, 300, value=150, label="Trees")
-            max_depth = gr.Slider(2, 20, value=10, label="Max depth")
+            max_depth = gr.Slider(2, 20, value=10, step=1, label="Max depth")
             seed = gr.Number(value=42, label="Random seed")
 
             run_btn = gr.Button("Train & Evaluate", variant="primary")