Update app.py

app.py

@@ -6,6 +6,17 @@ import folium
 from folium.plugins import HeatMap, MarkerCluster
 from streamlit_folium import st_folium
 
+# To fix the color scheme in crash stats plot (asked ChatGPT for appropriate colors)
+severity_colors = {
+    "No Injury": "#1f77b4",
+    "Possible Injury": "#aec7e8",
+    "Non Incapacitating Injury": "#ff7f0e",
+    "Incapacitating Injury": "#ffbb78",
+    "Suspected Minor Injury": "#2ca02c",
+    "Suspected Serious Injury": "#98df8a",
+    "Fatal": "#d62728",
+}
+
 @st.cache_data
 def load_and_preprocess_data(file_path):
     # Read the data
@@ -69,7 +80,8 @@ def create_severity_violation_chart(df, age_group=None):
         color='Severity',
         title=f'Crash Severity Distribution by Violation Type - {age_group}',
         labels={'count': 'Number of Incidents', 'Violation': 'Violation Type'},
-        height=600
+        height=600,
+        color_discrete_map=severity_colors, # --> for part 3
     )
     
     fig.update_layout(
@@ -78,7 +90,8 @@ def create_severity_violation_chart(df, age_group=None):
         barmode='stack'
     )
     
-    return fig
+    # return fig
+    return fig, violations
 
 def get_top_violations(df, age_group):
     if age_group == 'All Ages':
@@ -104,6 +117,23 @@ def get_top_violations(df, age_group):
     
     return violations_df.head()
 
+# added interactivity pie plot for part3 (linked with crash stats visualization)
+def create_interactive_pie_chart(violations, selected_violation):
+    # Filter data based on selected violation
+    filtered_data = violations[violations['Violation'] == selected_violation]
+    
+    # Create a pie chart for severity distribution of the selected violation type
+    fig = px.pie(
+        filtered_data,
+        names='Severity',
+        values='count',
+        title=f'Severity Level Distribution for Violation: {selected_violation}',
+        height=400,
+        color_discrete_map=severity_colors
+    )
+    
+    return fig
+    
 @st.cache_data
 def create_map(df, selected_year):
     filtered_df = df[df['Year'] == selected_year]
@@ -171,10 +201,10 @@ def create_injuries_fatalities_chart(crash_data, unit_type):
 
     # Reshape the data for easier plotting
     injuries = monthly_sum[['Month', 'Totalinjuries']].rename(columns={'Totalinjuries': 'Value'})
-    injuries['Measure'] = 'Total Injuries'
+    injuries['Type'] = 'Total Injuries'
 
     fatalities = monthly_sum[['Month', 'Totalfatalities']].rename(columns={'Totalfatalities': 'Value'})
-    fatalities['Measure'] = 'Total Fatalities'
+    fatalities['Type'] = 'Total Fatalities'
 
     combined_data = pd.concat([injuries, fatalities])
 
@@ -221,8 +251,8 @@ def create_injuries_fatalities_chart(crash_data, unit_type):
     line_chart = alt.Chart(combined_data).mark_line(point=True).encode(
         x=alt.X('Month:N', sort=month_order, title='Month'),
         y=alt.Y('Value:Q', title='Total Injuries & Fatalities'),
-        color=alt.Color('Measure:N', title='', scale=alt.Scale(domain=['Total Injuries', 'Total Fatalities'], range=['blue', 'red'])),
-        tooltip=['Month', 'Measure:N', 'Value:Q']
+        color=alt.Color('Type:N', title='', scale=alt.Scale(domain=['Total Injuries', 'Total Fatalities'], range=['blue', 'red'])),
+        tooltip=['Month', 'Type:N', 'Value:Q']
     ).properties(
         title=f'Total Injuries and Fatalities by Month for Unit Type Pair: {unit_type}',
         width=600,
@@ -356,6 +386,10 @@ def main():
 
     if 'Weather' not in df.columns:
         df['Weather'] = 'Unknown'
+
+    # Initialize session state to store selected violation --> added for part3 (interactive pie chart)
+    if 'selected_violation' not in st.session_state:
+        st.session_state['selected_violation'] = None
     
     # Create tabs for different visualizations
     tab1, tab2, tab3, tab4, tab5 = st.tabs(["Crash Statistics", "Crash Map", "Crash Trend", "Crash Injuries/Fatalities","Distribution by Category"])
@@ -366,8 +400,23 @@ def main():
         selected_age = st.selectbox('Select Age Group:', age_groups)
         
         # Create and display chart
-        fig = create_severity_violation_chart(df, selected_age)
-        st.plotly_chart(fig, use_container_width=True)
+        # fig = create_severity_violation_chart(df, selected_age)
+        fig, violations = create_severity_violation_chart(df, selected_age) # --> for part3 (interactive pie chart)
+        # st.plotly_chart(fig, use_container_width=True)
+
+        # Display the first bar chart and capture click events using plotly_events
+        clicked_points = plotly_events(fig, click_event=True, override_height=600, override_width="100%") # added for part3 (interactive pie chart)
+        
+        # If a bar is clicked, update the selected_violation in session_state --> added for part3 (interactive pie chart)
+        if clicked_points:
+            selected_violation = clicked_points[0]['x']
+            if selected_violation != st.session_state['selected_violation']:
+                st.session_state['selected_violation'] = selected_violation
+
+        # If a violation is selected, display the pie chart --> added for part3 (interactive pie chart)
+        if st.session_state['selected_violation']:
+            pie_chart = create_interactive_pie_chart(violations, st.session_state['selected_violation'])
+            st.plotly_chart(pie_chart, use_container_width=True)
         
         # Display statistics
         if selected_age == 'All Ages':