Update src/streamlit_app.py

src/streamlit_app.py

@@ -57,6 +57,7 @@ st.write(f"Total records: {df.shape[0]}  |  Total columns: {df.shape[1]}")
 st.dataframe(df.head())
 
 # Pie Chart 1: Top 10 Crime Types
+st.markdown("<div class='title'><h1> Top 10 Crime Type </h1></div>", unsafe_allow_html=True)
 years = sorted(df["year"].dropna().astype(int).unique())
 
 # Year filter (shorter, above chart)
@@ -82,6 +83,24 @@ top_crimes = (
 )
 top_crimes["Percentage"] = top_crimes["Count"] / top_crimes["Count"].sum()
 
+#Key Metrics
+st.markdown("### Key Metrics")
+col1, col2, col3 = st.columns(3)
+col1.metric(
+    label="Total Incidents",
+    value=f"{len(filtered):,}"
+)
+col2.metric(
+    label="Unique Crime Types",
+    value=f"{filtered['crm_cd_desc'].nunique():,}"
+)
+# compute share of the top crime
+top_share = top_crimes.iloc[0]["Percentage"]  
+col3.metric(
+    label=f"Share of Top Crime ({top_crimes.iloc[0]['Crime Type']})",
+    value=f"{top_share:.1%}"
+)
+
 # Plotly donut chart ──
 fig = px.pie(
     top_crimes,
@@ -107,21 +126,3 @@ fig.update_layout(
 )
 
 st.plotly_chart(fig, use_container_width=True)
-
-#Key Metrics
-st.markdown("### Key Metrics")
-col1, col2, col3 = st.columns(3)
-col1.metric(
-    label="Total Incidents",
-    value=f"{len(filtered):,}"
-)
-col2.metric(
-    label="Unique Crime Types",
-    value=f"{filtered['crm_cd_desc'].nunique():,}"
-)
-# compute share of the top crime
-top_share = top_crimes.iloc[0]["Percentage"]  
-col3.metric(
-    label=f"Share of Top Crime ({top_crimes.iloc[0]['Crime Type']})",
-    value=f"{top_share:.1%}"
-)