Update app.py

app.py

@@ -19,7 +19,7 @@ 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"Displayed Rows From Row Number {row_number} to {row_number+15}"
+    Statement = f"Displaying rows from row {row_number} to {row_number+15}"
     return Statement, temp_df
 
 
@@ -153,7 +153,7 @@ def Plot_featue_importance(test_split):
     except:
         # return blank figures
         fig = go.Figure()
-        fig.update_layout(title="Train Your Model to See Plots")
+        fig.update_layout(title="Train a model to see the plots")
         return fig, fig
 
     sorted_idx = np.argsort(feature_importance)
@@ -190,12 +190,12 @@ def Plot_featue_importance(test_split):
 with gr.Blocks() as demo:
     gr.Markdown("# Gradient Boosting regression")
     gr.Markdown(
-        "Demo is Based on [Gradient Boosting regression](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html) Example.This example demonstrates Gradient Boosting 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."
+        "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"):
-        gr.Markdown("## Below is the Diabetes Dataset used in this Demo")
-        gr.Markdown("### Feel free to change the number of rows to display")
+        gr.Markdown("### Below is the diabetes dataset used in this demo 👇 ")
+        gr.Markdown("### You can change the interval of rows to display.")
         gr.Markdown(
             "The diabetes dataset consists of ten baseline variables, age, sex, body mass index (BMI), average blood pressure (BP), and six blood serum measurements for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline."
         )
@@ -207,12 +207,12 @@ with gr.Blocks() as demo:
         rows_number.change(
             fn=display_table,
             inputs=[rows_number],
-            outputs=[gr.Text(label="Rows Information"), gr.DataFrame()],
+            outputs=[gr.Text(label="Row"), gr.DataFrame()],
         )
 
-    with gr.Tab("Trian Your Model"):
+    with gr.Tab("Train the model"):
         gr.Markdown(
-            "# Play with the parameters to see how the model Performance changes"
+            "# Play with the parameters to see how the model performance changes"
         )
 
         gr.Markdown(
@@ -256,7 +256,7 @@ with gr.Blocks() as demo:
 
         model_btn = gr.Button("Train Model")
         gr.Markdown(
-            "### Finally, we will visualize the results. To do that we will first compute the test set deviance and then plot it against boosting iterations."
+            "### Finally, we will visualize the results. To do that, we will first compute the test set deviance and then plot it against boosting iterations."
         )
         model_btn.click(
             fn=train_model,