Update src/streamlit_app.py

src/streamlit_app.py

@@ -127,9 +127,9 @@ fig.update_traces(
 fig.update_layout(
     legend_title_text="Crime Type",
     margin=dict(t=60, b=20, l=20, r=20),
-    height=600,
+    height=700,
     title_x=0.5
 )
 
 st.plotly_chart(fig, use_container_width=True)
-st.markdown(""" The donut chart shows the share of the ten most frequent crime categories in the selected year. At the center, you can see that Vehicle ­– Stolen is the single largest slice, accounting for roughly 27% of all incidents, followed by Theft – Grand (18%) and Vehicle, Stolen – Other (motorized scooters, bikes, etc.) at about 18%. Battery – Simple Assault (12%) and Robbery (5%) round out the next largest wedges. The remaining five categories each represent between 3%–5% of total incidents—these include miscellaneous crimes, criminal threats, assault with a deadly weapon, burglary, and minor vandalism. By displaying both slice size and percentage labels, the chart makes it easy to compare how dominant property‐related offenses are, versus violent or lesser‐common crimes, in that year’s LAPD data. """)
+st.markdown(""" The donut chart shows the share of the ten most frequent crime categories in the selected year. At the center, you can see that Vehicle ­– Stolen is the single largest slice, accounting for roughly 18.7% of all incidents, The remaining five categories each represent between 3%–5% of total incidents—these include miscellaneous crimes, criminal threats, assault with a deadly weapon, burglary, and minor vandalism. By displaying both slice size and percentage labels, the chart makes it easy to compare how dominant property‐related offenses are, versus violent or lesser‐common crimes, in that year’s LAPD data. """)