Update app.py

app.py

@@ -338,7 +338,7 @@ def main():
     - Hangyue Zhang ([email protected])
     - Andrew Nam ([email protected])
     - Nirmal Attarde ([email protected])
-    - Maanas Sandeep Agrawa ([email protected])
+    - Maanas Sandeep Agrawal ([email protected])
     """)
     
 
@@ -399,7 +399,6 @@ def main():
         
         with map_col:
             # Create and display map
-            st.markdown("### Crash Location Map")
             map_placeholder = st.empty()
             with map_placeholder:
                 m = create_map(df, selected_year)
@@ -440,7 +439,6 @@ def main():
         trend_col, desc_col = st.columns([7, 3])
         
         with trend_col:
-            st.markdown("### Crash Trend Over Time")
             trend_fig = create_crash_trend_chart(df, selected_weather)
             # Update the figure layout for larger size
             trend_fig.update_layout(
@@ -555,7 +553,6 @@ def main():
         chart_col, desc_col = st.columns([7, 3])
         
         with chart_col:
-            st.markdown(f"### Distribution of Incidents by {selected_category}")
             distribution_chart = create_category_distribution_chart(df, selected_category, selected_year)
             # Update the figure layout for larger size
             distribution_chart.update_layout(
@@ -566,8 +563,8 @@ def main():
             st.plotly_chart(distribution_chart, use_container_width=True)
 
         with desc_col:
-            st.markdown("""
-            ## Distribution by Category
+            st.markdown(f"""
+            ## Distribution of Incidents by {selected_category}
             This visualization explores the distribution of traffic incidents across various categories, such as Collision Manner, Weather, Surface Condition, Alcohol Use, and Driver Gender. Each bar represents a specific category value (e.g., "Male" or "Female" for Gender), and the bars are divided into segments based on Injury Severity (e.g., Minor, Moderate, Serious, Fatal).
     
             **Key features include:**