Update app.py

app.py

@@ -132,140 +132,5 @@ def main():
         top_violations = get_top_violations(df, selected_age)
         st.table(top_violations)
 
-if __name__ == "__main__":
-    main()import streamlit as st
-import pandas as pd
-import plotly.express as px
-
-def load_and_preprocess_data(file_path):
-    # Read the data
-    df = pd.read_csv(file_path)
-    
-    # Basic preprocessing
-    df = df.drop(['X', 'Y'], axis=1)
-    df.dropna(subset=['Incidentid', 'DateTime', 'Year', 'Latitude', 'Longitude'], inplace=True)
-    
-    # Fill missing values
-    numeric = ['Age_Drv1', 'Age_Drv2']
-    for col in numeric:
-        df[col].fillna(df[col].median(), inplace=True)
-        
-    categorical = ['Gender_Drv1', 'Violation1_Drv1', 'AlcoholUse_Drv1', 'DrugUse_Drv1',
-                  'Gender_Drv2', 'Violation1_Drv2', 'AlcoholUse_Drv2', 'DrugUse_Drv2',
-                  'Unittype_Two', 'Traveldirection_Two', 'Unitaction_Two', 'CrossStreet']
-    for col in categorical:
-        df[col].fillna('Unknown', inplace=True)
-    
-    # Remove invalid ages
-    df = df[
-        (df['Age_Drv1'] <= 90) & 
-        (df['Age_Drv2'] <= 90) & 
-        (df['Age_Drv1'] >= 16) & 
-        (df['Age_Drv2'] >= 16)
-    ]
-    
-    # Create age groups
-    bins = [15, 25, 35, 45, 55, 65, 90]
-    labels = ['16-25', '26-35', '36-45', '46-55', '56-65', '65+']
-    
-    df['Age_Group_Drv1'] = pd.cut(df['Age_Drv1'], bins=bins, labels=labels)
-    df['Age_Group_Drv2'] = pd.cut(df['Age_Drv2'], bins=bins, labels=labels)
-    
-    return df
-
-def create_severity_violation_chart(df, age_group=None):
-    # Apply age group filter if selected
-    if age_group != 'All Ages':
-        df = df[(df['Age_Group_Drv1'] == age_group) | (df['Age_Group_Drv2'] == age_group)]
-    
-    # Combine violations from both drivers
-    violations_1 = df.groupby(['Violation1_Drv1', 'Injuryseverity']).size().reset_index(name='count')
-    violations_2 = df.groupby(['Violation1_Drv2', 'Injuryseverity']).size().reset_index(name='count')
-    
-    violations_1.columns = ['Violation', 'Severity', 'count']
-    violations_2.columns = ['Violation', 'Severity', 'count']
-    
-    violations = pd.concat([violations_1, violations_2])
-    violations = violations.groupby(['Violation', 'Severity'])['count'].sum().reset_index()
-    
-    # Create visualization
-    fig = px.bar(
-        violations,
-        x='Violation',
-        y='count',
-        color='Severity',
-        title=f'Crash Severity Distribution by Violation Type - {age_group}',
-        labels={'count': 'Number of Incidents', 'Violation': 'Violation Type'},
-        height=600
-    )
-    
-    fig.update_layout(
-        xaxis_tickangle=-45,
-        legend_title='Severity Level',
-        barmode='stack'
-    )
-    
-    return fig
-
-def get_top_violations(df, age_group):
-    if age_group == 'All Ages':
-        violations = pd.concat([
-            df['Violation1_Drv1'].value_counts(),
-            df['Violation1_Drv2'].value_counts()
-        ]).groupby(level=0).sum()
-    else:
-        filtered_df = df[
-            (df['Age_Group_Drv1'] == age_group) | 
-            (df['Age_Group_Drv2'] == age_group)
-        ]
-        violations = pd.concat([
-            filtered_df['Violation1_Drv1'].value_counts(),
-            filtered_df['Violation1_Drv2'].value_counts()
-        ]).groupby(level=0).sum()
-    
-    # Convert to DataFrame and format
-    violations_df = violations.reset_index()
-    violations_df.columns = ['Violation Type', 'Count']
-    violations_df['Percentage'] = (violations_df['Count'] / violations_df['Count'].sum() * 100).round(2)
-    violations_df['Percentage'] = violations_df['Percentage'].map('{:.2f}%'.format)
-    
-    return violations_df.head()
-
-def main():
-    st.title('Traffic Crash Analysis')
-    
-    # Load data
-    df = load_and_preprocess_data('1.08_Crash_Data_Report_(detail).csv')
-    
-    # Create simple dropdown for age groups
-    age_groups = ['All Ages', '16-25', '26-35', '36-45', '46-55', '56-65', '65+']
-    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)
-    
-    # Display statistics
-    if selected_age == 'All Ages':
-        total_incidents = len(df)
-    else:
-        total_incidents = len(df[
-            (df['Age_Group_Drv1'] == selected_age) | 
-            (df['Age_Group_Drv2'] == selected_age)
-        ])
-    
-    # Create two columns for statistics
-    col1, col2 = st.columns(2)
-    
-    with col1:
-        st.markdown(f"### Total Incidents")
-        st.markdown(f"**{total_incidents:,}** incidents for {selected_age}")
-    
-    # Display top violations table
-    with col2:
-        st.markdown("### Top Violations")
-        top_violations = get_top_violations(df, selected_age)
-        st.table(top_violations)
-
 if __name__ == "__main__":
     main()