Update src/streamlit_app.py

src/streamlit_app.py

@@ -325,6 +325,7 @@ fig = px.bar(
     x='RegionName',
     y='Count',
     color='crm_cd_desc',
+    color_discrete_sequence=px.colors.sequential.Agsunset,
     title='Top 5 Crimes by Region',
     labels={'crm_cd_desc': 'Crime Type'},
     height=600
@@ -463,6 +464,7 @@ the fluctuations and overall trajectories of these major crime categories across
 
 
 # -------------------------------- Plot 4: Map --------------------------------
+st.markdown("<div class='sectionheader'> Explore LA Crime Patterns: An Interactive Folium Map </div>", unsafe_allow_html=True)
 # Load the data.
 with open(GEOJSON_PATH, "r", encoding="utf-8") as f:
     geojson_data = json.load(f)
@@ -529,6 +531,7 @@ This visualization uses Folium to build an interactive map of crime distribution
 </div>""",unsafe_allow_html=True)
 
 # -------------------------------- Plot 4: Stacked Bar Chart --------------------------------
+st.markdown("<div class='sectionheader'>Trends in Top 10 Crime Types (2020–2024)</div>", unsafe_allow_html=True)
 # Group by crime type and year.
 stacked_year_df = df_top.groupby(['year', 'crm_cd_desc']).size().reset_index(name='count')
     
@@ -555,6 +558,7 @@ By observing the plot, we can find out that 2022 had the most crimes, the year h
 </div>""",unsafe_allow_html=True)
 
 # -------------------------------- Plot 5: Bar Chart --------------------------------
+st.markdown("<div class='sectionheader'>Crime Rankings for Selected Year</div>", unsafe_allow_html=True)
 # Group by crime type and year.
 heatmap1_df = df_top.groupby(['crm_cd_desc', 'year']).size().reset_index(name='count')