Update app.py

app.py

@@ -96,10 +96,33 @@ with col3:
 # Visualizations
 if viz_type == "Complaint Types":
     st.subheader("Interactive Complaint Types Pie Chart")
+    
+    # Prepare data
     complaint_counts = filtered_data['Type of Complaint'].value_counts().reset_index()
     complaint_counts.columns = ['Complaint Type', 'Count']
-    fig = px.pie(complaint_counts, names='Complaint Type', values='Count', hole=0.4)
+    
+    # Create an interactive pie chart with hover tooltips
+    fig = px.pie(
+        complaint_counts,
+        names='Complaint Type',
+        values='Count',
+        title="Distribution of Complaint Types",
+        color_discrete_sequence=px.colors.qualitative.Set2,
+        labels={"Count": "Number of Complaints", "Complaint Type": "Type of Complaint"},
+        hover_data=['Count']
+    )
+    fig.update_traces(textinfo='percent+label', hovertemplate='<b>%{label}</b><br>Complaints: %{value}<br>Percentage: %{percent}')
+    
+    # Display chart
     st.plotly_chart(fig, use_container_width=True)
+    
+    # Updated write-up
+    st.write("""
+    **Write-up:** This visualization displays the distribution of complaint types using an interactive pie chart. 
+    Hovering over each segment reveals the complaint type, the number of complaints, and its percentage of the total. 
+    The vibrant colors highlight differences between complaint categories, providing an accessible and informative view of the data.
+    """)
+
 
 elif viz_type == "Geographic Distribution":
     st.subheader("Clustered Heatmap of Complaints")