import streamlit as st
import pandas as pd
import folium
from folium.plugins import MarkerCluster, HeatMap
from streamlit_plotly_events import plotly_events
import plotly.express as px

# Function to create the map
@st.cache_data
def create_map(df, selected_year, selected_severity=None):
    # Filter data by selected year
    filtered_df = df[df['Year'] == selected_year]
    
    # Filter further by selected severity if provided
    if selected_severity:
        filtered_df = filtered_df[filtered_df['Injuryseverity'] == selected_severity]
    
    # Remove rows with missing latitude or longitude
    filtered_df = filtered_df.dropna(subset=['Latitude', 'Longitude'])
    
    # Create the map
    m = folium.Map(
        location=[33.4255, -111.9400],  # Default location (can be customized)
        zoom_start=12,
        control_scale=True,
        tiles='CartoDB positron'
    )
    
    # Add marker cluster
    marker_cluster = MarkerCluster(name="Accident Locations").add_to(m)
    
    # Add accident markers
    for _, row in filtered_df.iterrows():
        folium.Marker(
            location=[row['Latitude'], row['Longitude']],
            popup=f"Accident at {row['Longitude']}, {row['Latitude']}<br>Date: {row['DateTime']}<br>Severity: {row['Injuryseverity']}",
            icon=folium.Icon(color='red')
        ).add_to(marker_cluster)
    
    # Add heatmap
    heat_data = filtered_df[['Latitude', 'Longitude']].values.tolist()
    HeatMap(heat_data, radius=15, max_zoom=13, min_opacity=0.3, name="Heat Map").add_to(m)
    
    folium.LayerControl().add_to(m)
    return m

# Function to create a bar chart based on accident severity
def create_bar_chart(df, selected_year):
    filtered_df = df[df['Year'] == selected_year]
    severity_count = filtered_df['Injuryseverity'].value_counts().reset_index()
    severity_count.columns = ['Injuryseverity', 'Count']
    
    fig = px.bar(
        severity_count, 
        x='Injuryseverity', 
        y='Count', 
        title="Accidents by Severity",
        labels={'Injuryseverity': 'Severity', 'Count': 'Number of Accidents'}
    )
    fig.update_traces(marker_color='blue')
    fig.update_layout(clickmode='event+select')  # Enable interactivity
    return fig

# Streamlit app
def app():
    # Load your dataset here
    df = pd.read_csv("Crash_Data_Report.csv")  # Replace with your actual dataset
    
    # Sidebar for selection
    st.sidebar.header("Select Filters")
    selected_year = st.sidebar.selectbox("Select Year", df['Year'].unique())
    
    # Initialize session state for selected severity
    if "selected_severity" not in st.session_state:
        st.session_state["selected_severity"] = None
    
    # Bar Chart visualization
    st.subheader("Accidents by Severity")
    bar_chart = create_bar_chart(df, selected_year)
    
    # Capture user interaction
    selected_points = plotly_events(bar_chart, click_event=True, hover_event=False)
    if selected_points:
        # Get the selected severity from the clicked bar
        selected_severity = selected_points[0]['x']
        st.session_state["selected_severity"] = selected_severity
    else:
        selected_severity = st.session_state["selected_severity"]
    
    # Map visualization
    st.subheader("Accidents Map")
    map_obj = create_map(df, selected_year, selected_severity)
    st.components.v1.html(map_obj._repr_html_(), height=600)
    
    # Display current filter
    st.sidebar.write(f"Selected Severity: {selected_severity if selected_severity else 'All'}")

# Run the Streamlit app
if __name__ == "__main__":
    app()