import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np

def load_and_preprocess_data(file_path):
    # Read the data
    df = pd.read_csv(file_path)
    
    # Drop redundant columns
    df = df.drop(['X', 'Y'], axis=1)
    
    # Handle missing values
    df.dropna(subset=['Incidentid', 'DateTime', 'Year', 'Latitude', 'Longitude'], inplace=True)
    
    # Fill numeric values
    numeric = ['Age_Drv1', 'Age_Drv2']
    for col in numeric:
        df[col].fillna(df[col].median(), inplace=True)
    
    # Fill categorical values
    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 for both drivers
    df['Age_Group_Drv1'] = pd.cut(
        df['Age_Drv1'],
        bins=[15, 25, 35, 45, 55, 65, 90],
        labels=['16-25', '26-35', '36-45', '46-55', '56-65', '65+']
    )
    
    df['Age_Group_Drv2'] = pd.cut(
        df['Age_Drv2'],
        bins=[15, 25, 35, 45, 55, 65, 90],
        labels=['16-25', '26-35', '36-45', '46-55', '56-65', '65+']
    )
    
    return df

def create_severity_violation_chart(df, selected_age_group=None):
    # Filter by age group if selected
    if selected_age_group:
        df = df[
            (df['Age_Group_Drv1'] == selected_age_group) | 
            (df['Age_Group_Drv2'] == selected_age_group)
        ]
    
    # Create violation categories for both drivers
    violations_drv1 = df.groupby(['Violation1_Drv1', 'Injuryseverity']).size().reset_index(name='count')
    violations_drv2 = df.groupby(['Violation1_Drv2', 'Injuryseverity']).size().reset_index(name='count')
    
    # Combine violations from both drivers
    violations_drv1.columns = ['Violation', 'Severity', 'count']
    violations_drv2.columns = ['Violation', 'Severity', 'count']
    violations_combined = pd.concat([violations_drv1, violations_drv2])
    
    # Aggregate the combined violations
    violations_agg = violations_combined.groupby(['Violation', 'Severity'])['count'].sum().reset_index()
    
    # Create the stacked bar chart
    fig = px.bar(
        violations_agg,
        x='Violation',
        y='count',
        color='Severity',
        title=f'Distribution of Crash Severity by Violation Type {selected_age_group if selected_age_group else ""}',
        labels={'count': 'Number of Incidents', 'Violation': 'Violation Type'},
        height=600
    )
    
    # Customize the layout
    fig.update_layout(
        xaxis_tickangle=-45,
        legend_title='Severity',
        barmode='stack',
        showlegend=True
    )
    
    return fig

def main():
    st.title('Traffic Crash Analysis Dashboard')
    
    # Load data
    df = load_and_preprocess_data('1.08_Crash_Data_Report_(detail).csv')
    
    # Create age group selector
    st.sidebar.header('Filters')
    age_groups = ['All'] + list(df['Age_Group_Drv1'].unique())
    selected_age_group = st.sidebar.selectbox('Select Age Group', age_groups)
    
    # Create and display the chart
    if selected_age_group == 'All':
        fig = create_severity_violation_chart(df)
    else:
        fig = create_severity_violation_chart(df, selected_age_group)
    
    st.plotly_chart(fig, use_container_width=True)
    
    # Add additional insights
    st.subheader('Analysis Insights')
    
    # Calculate and display some statistics
    if selected_age_group == 'All':
        total_crashes = len(df)
    else:
        total_crashes = len(df[
            (df['Age_Group_Drv1'] == selected_age_group) | 
            (df['Age_Group_Drv2'] == selected_age_group)
        ])
    
    st.write(f"Total number of crashes: {total_crashes:,}")
    
    # Show top violations
    st.subheader('Top Violations')
    if selected_age_group == 'All':
        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'] == selected_age_group) | 
            (df['Age_Group_Drv2'] == selected_age_group)
        ]
        violations = pd.concat([
            filtered_df['Violation1_Drv1'].value_counts(),
            filtered_df['Violation1_Drv2'].value_counts()
        ]).groupby(level=0).sum()
    
    st.write(violations.head())

if __name__ == "__main__":
    main()