Update app.py

app.py

@@ -18,8 +18,8 @@ X, y = diabetes.data, diabetes.target
 def display_table(row_number):
     X, y = diabetes.data, diabetes.target
     XX = pd.concat([X, y], axis=1)
-    temp_df = XX[row_number : row_number + 15]
-    Statement = f"Displaying rows from row {row_number} to {row_number+15}"
+    temp_df = XX[row_number : row_number + 5]
+    Statement = f"Displaying rows from row {row_number} to {row_number+5}"
     return Statement, temp_df
 
 
@@ -193,7 +193,7 @@ with gr.Blocks() as demo:
         "This demo is based on [gradient boosting regression example of scikit-learn](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html) Example.This example demonstrates gradient goosting to produce a predictive model from an ensemble of weak predictive models. Gradient boosting can be used for regression and classification problems. Here, we will train a model to tackle a diabetes regression task."
     )
 
-    with gr.Tab("Data"):
+    with gr.Tab("Train the model"):
         gr.Markdown("### Below is the diabetes dataset used in this demo 👇 ")
         gr.Markdown("### You can change the interval of rows to display.")
         gr.Markdown(
@@ -201,7 +201,7 @@ with gr.Blocks() as demo:
         )
         total_rows = X.shape[0]
         rows_number = gr.Slider(
-            0, total_rows, label="Displaying Rows", value=15, step=15
+            0, total_rows, label="Displaying Rows", value=5, step=5
         )
 
         rows_number.change(
@@ -210,7 +210,6 @@ with gr.Blocks() as demo:
             outputs=[gr.Text(label="Row"), gr.DataFrame()],
         )
 
-    with gr.Tab("Train the model"):
         gr.Markdown(
             "# Play with the parameters to see how the model performance changes"
         )