Update src/streamlit_app.py

src/streamlit_app.py

@@ -231,46 +231,47 @@ relying on specific numbers. By pairing each slice with its label, the chart pro
 volume and which are comparatively rare, helping stakeholders focus on the offenses that matter most.</div>""",unsafe_allow_html=True)
 
 # -------------------------------- Plot 6: Bubble Map of Incident Counts by Region --------------------------------
-st.markdown("<div class='sectionheader'>Crime Hotspots by Region</div>", unsafe_allow_html=True)
+# st.markdown("<div class='sectionheader'>Crime Hotspots by Region</div>", unsafe_allow_html=True)
 
-# 1. Aggregate counts and centroids
-region_stats = (
-    df
-      .groupby("RegionName")
-      .agg(
-          Count   = pd.NamedAgg(column="crm_cd_desc", aggfunc="size"),
-          Latitude  = pd.NamedAgg(column="lat", aggfunc="mean"),
-          Longitude = pd.NamedAgg(column="lon", aggfunc="mean")
-      )
-      .reset_index()
-)
+# # 1. Aggregate counts and centroids
+# region_stats = (
+#     df
+#       .groupby("RegionName")
+#       .agg(
+#           Count   = pd.NamedAgg(column="crm_cd_desc", aggfunc="size"),
+#           Latitude  = pd.NamedAgg(column="lat", aggfunc="mean"),
+#           Longitude = pd.NamedAgg(column="lon", aggfunc="mean")
+#       )
+#       .reset_index()
+# )
 
-# 2. Build the bubble map
-fig = px.scatter_mapbox(
-    region_stats,
-    lat="Latitude",
-    lon="Longitude",
-    size="Count",                       # bubble size ~ incident volume
-    color="Count",                      # color gradient for emphasis
-    hover_name="RegionName",
-    hover_data={"Count":True, "Latitude":False, "Longitude":False},
-    size_max=30,                        # max bubble diameter
-    zoom=10,                            # adjust to focus your city
-    mapbox_style="open-street-map",
-    title="Crime Volume by Region (Bubble Map)"
-)
+# # 2. Build the bubble map
+# fig = px.scatter_mapbox(
+#     region_stats,
+#     lat="Latitude",
+#     lon="Longitude",
+#     size="Count",                       # bubble size ~ incident volume
+#     color="Count",                      # color gradient for emphasis
+#     hover_name="RegionName",
+#     hover_data={"Count":True, "Latitude":False, "Longitude":False},
+#     size_max=30,                        # max bubble diameter
+#     zoom=10,                            # adjust to focus your city
+#     mapbox_style="open-street-map",
+#     title="Crime Volume by Region (Bubble Map)"
+# )
 
-# 3. Tidy layout
-fig.update_layout(
-    margin=dict(t=50, b=0, l=0, r=0),
-    legend_title_text="Incident Count",
-    title_x=0.5
-)
+# # 3. Tidy layout
+# fig.update_layout(
+#     margin=dict(t=50, b=0, l=0, r=0),
+#     legend_title_text="Incident Count",
+#     title_x=0.5
+# )
 
-# 4. Render
-st.plotly_chart(fig, use_container_width=True)
+# # 4. Render
+# st.plotly_chart(fig, use_container_width=True)
 
 # -------------------------------- Plot 7: Line Chart for Incident Counts by Region --------------------------------
+st.markdown("<div class='sectionheader'>Incidents Trends over Time </div>", unsafe_allow_html=True)
 # 1. Aggregate total incidents by year
 yearly_region = (
     df
@@ -292,10 +293,7 @@ fig = px.line(
     x="year",
     y="Count",
     color="RegionName",
-    title=(
-        f"Incident Trends Over Time" +
-        (f" – {sel_region}" if sel_region!="All" else "")
-    ),
+    title=(" "),
     labels={"year":"Year", "Count":"Incident Count"}
 )
 
@@ -305,6 +303,7 @@ for trace in fig.data:
 
 st.plotly_chart(fig, use_container_width=True)
 # -------------------------------- Plot 8: Line Chart for Incident Counts by Region --------------------------------
+st.markdown("<div class='sectionheader'>Crime Composition by Region: Top 5 Offenses </div>", unsafe_allow_html=True)
 # 1. Compute counts per region and crime
 counts = (
     df