Update app.py

app.py

@@ -648,43 +648,23 @@ def main():
     st.markdown("# FP2 Conclusion")
     
     st.markdown("""
-    In FP2, we created interactive visualizations to analyze traffic accident data, focusing on trends, contributing factors, and safety implications. 
-    Each visualization provides specific insights and helps users make data-driven decisions to improve road safety.
+    In FP2, we created interactive visualizations to analyze traffic accident data, focusing on trends, contributing factors, and safety implications. Each visualization provides specific insights and helps users make data-driven decisions to improve road safety.
+
+    - **Crash Trend Over Time**: An interactive line chart showing annual crash patterns with an optional weather filter, helping identify trends and weather-related correlations.
+
+    - **Severity of Violations Across Age Groups**: Visualizes crash severities by violation types and driver age groups, aiding targeted safety campaigns and interventions.
+
+    - **Distribution of Incidents by Collision Manner**: A bar chart linking traffic incidents with factors like surface conditions and gender, offering insights into injury severity trends.
+
+    - **Injuries and Fatalities Trends**: Displays monthly injuries and fatalities, highlighting seasonal variations and high-risk periods, with unit-type filtering for detailed analysis.
+
+    - **Traffic Crash Location Map**: Combines marker clusters and heatmaps to reveal accident hotspots and severity patterns, guiding safety improvements and urban planning.
+
+    In Part 3, we plan to enhance interactivity by linking visualizations, such as dynamically updating a map and bar chart for a more seamless data exploration experience.
+
+    These tools empower stakeholders to address risks, implement safety measures, and prioritize infrastructure upgrades for safer roads.
     """)
-    
-    # Create columns for different visualizations' conclusions
-    con1, con2, con3 = st.columns(3)
-    
-    with con1:
-        st.markdown("""
-        ### Crash Trend Over Time
-        An interactive line chart showing annual crash patterns with an optional weather filter, helping identify:
-        * Long-term accident trends
-        * Weather-related correlations
-        * Seasonal patterns in crash frequencies
-        * Peak accident periods
-        """)
-    
-    with con2:
-        st.markdown("""
-        ### Severity of Violations Across Age Groups
-        Visualizes crash severities by violation types and driver age groups:
-        * Age-specific violation patterns
-        * High-risk behavior identification
-        * Severity distribution analysis
-        * Targeted intervention opportunities
-        """)
-    
-    with con3:
-        st.markdown("""
-        ### Distribution of Incidents
-        A comprehensive analysis of incidents by various factors:
-        * Collision manner analysis
-        * Surface condition impacts
-        * Gender-based patterns
-        * Environmental factor effects
-        * Time-based distribution
-        """)
+
 
 
 if __name__ == "__main__":