edit the wording

app.py

@@ -119,10 +119,10 @@ def main(data_path: str, enc_path: str):
   venice_fig = plot_venice_blvd(df, encoding_labels)
 
   # Display the plots with Streamlit
-  st.write('# Example of what DBSCAN does')
-  st.write("""As an example of a typical DBSCAN result, I've clustered the
+  st.write('# Cluster the stops by their position')
+  st.write("""First, I clustered the
 stops by their geographic location.
-The algorithm finds three clusters.
+The DBSCAN algorithm finds three clusters.
 Points labeled `-1` aren't part of any cluster.
 Clicking on `-1` in the legend will turn off those points.""")
   st.plotly_chart(example_fig, use_container_width=True)
@@ -132,10 +132,14 @@ Clicking on `-1` in the legend will turn off those points.""")
 I then clustered those encodings so that I could group the stops based on their names
 instead of their geographic position.
 As I expected, stops on the same road end up close enough to each other that DBSCAN can cluster them together.
+
+
 Sometimes, however, a stop has a name that means something to the encoder.
 When that happens, the encoding ends up too far away from the rest of the stops on that road.
 For example, the stops on Venice Blvd get clustered together, 
 but the stop "Venice / Lincoln" ends up somewhere else.
+
+
 I assume it ends up somewhere else because the encoder recognizes "Lincoln" 
 and that meaning overpowers the "Venice" meaning enough that the encoding 
 is too far away from the rest of the "Venice" stops.