Improve formatting

app.py

@@ -75,8 +75,9 @@ with gr.Blocks(title=title) as demo:
     gr.Markdown(f"## {title}")
     gr.Markdown("""  
     This app demonstrates comparison of random forest with multi-output meta estimator for multi-output regression.
-    A random forest regressor is trained on randomly generated data which is used as baseline and compared with multi-output meta estimator trained on the same dataset 
-    The predicted outputs from each model are visualized in the plot together with the actual data. The maximum depth and number of estimator of the random forest can be adjusted and the effect can be seen in the resulting plot.
+    A random forest regressor is trained on randomly generated data which is used as baseline and compared with multi-output meta estimator trained on the same dataset. 
+    The predicted outputs from each model are visualized in the plot together with the actual data. 
+    The maximum depth and number of estimator of the random forest can be adjusted and the effect can be seen in the resulting plot.
     This app is developed based on [scikit-learn example](https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)
     """)