Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import pydeck as pdk
from folium import Map, LayerControl
from streamlit_folium import folium_static
import datetime

# Set page config
st.set_page_config(layout="wide", page_title="NYC Map Visualization")

# Generate mock data
@st.cache_data
def generate_mock_data():
    # Food truck locations around NYC
    n_trucks = 100
    trucks_df = pd.DataFrame({
        'name': [f'Food Truck #{i}' for i in range(n_trucks)],
        'lat': np.random.normal(40.7128, 0.1, n_trucks),
        'lon': np.random.normal(-74.0060, 0.1, n_trucks),
        'cuisine': np.random.choice(['Hot Dogs', 'Tacos', 'Halal', 'Ice Cream', 'Pizza'], n_trucks),
        'rating': np.random.uniform(3.0, 5.0, n_trucks),
        'borough': np.random.choice(['Manhattan', 'Brooklyn', 'Queens', 'Bronx', 'Staten Island'], n_trucks)
    })
    
    # Pedestrian activity (heatmap)
    n_points = 1000
    heatmap_df = pd.DataFrame({
        'lat': np.random.normal(40.7128, 0.1, n_points),
        'lon': np.random.normal(-74.0060, 0.1, n_points),
        'intensity': np.random.exponential(1, n_points)
    })
    
    return trucks_df, heatmap_df

# Initialize data
trucks_df, heatmap_df = generate_mock_data()

# Sidebar controls
st.sidebar.title("Map Controls")

# Time slider
time_range = st.sidebar.slider(
    "Time Range",
    min_value=datetime.time(0, 0),
    max_value=datetime.time(23, 59),
    value=(datetime.time(9, 0), datetime.time(17, 0))
)

# Borough filter
boroughs = st.sidebar.multiselect(
    "Borough Filter",
    ['Manhattan', 'Brooklyn', 'Queens', 'Bronx', 'Staten Island'],
    default=['Manhattan']
)

# Map style
map_style = st.sidebar.selectbox(
    "Map Style",
    ['streets-v11', 'satellite-v9', 'light-v10', 'dark-v10']
)

# Layer toggles
st.sidebar.subheader("Layers")
show_trucks = st.sidebar.checkbox("Food Trucks", value=True)
show_heatmap = st.sidebar.checkbox("Pedestrian Heatmap", value=True)
show_clusters = st.sidebar.checkbox("Taxi Clusters", value=False)

# Heatmap settings
if show_heatmap:
    st.sidebar.subheader("Heatmap Settings")
    heatmap_radius = st.sidebar.slider("Radius", 10, 100, 50)
    heatmap_intensity = st.sidebar.slider("Intensity", 0.1, 2.0, 1.0)

# Main content
st.title("NYC Interactive Map")

# Create columns for map and stats
col1, col2 = st.columns([7, 3])

with col1:
    # Initial view state
    initial_view_state = pdk.ViewState(
        latitude=40.7128,
        longitude=-74.0060,
        zoom=11,
        pitch=0
    )

    # Define layers
    layers = []

    if show_trucks:
        trucks_layer = pdk.Layer(
            'ScatterplotLayer',
            data=trucks_df[trucks_df['borough'].isin(boroughs)],
            get_position=['lon', 'lat'],
            get_radius=100,
            get_fill_color=[255, 0, 0, 140],
            pickable=True
        )
        layers.append(trucks_layer)

    if show_heatmap:
        heatmap_layer = pdk.Layer(
            'HeatmapLayer',
            data=heatmap_df,
            get_position=['lon', 'lat'],
            get_weight='intensity',
            radiusPixels=heatmap_radius,
            intensity=heatmap_intensity
        )
        layers.append(heatmap_layer)

    # Create deck
    deck = pdk.Deck(
        map_style=f'mapbox://{map_style}',
        initial_view_state=initial_view_state,
        layers=layers,
        tooltip={
            'html': '<b>Food Truck:</b> {name}<br/>'
                   '<b>Cuisine:</b> {cuisine}<br/>'
                   '<b>Rating:</b> {rating:.1f}⭐',
            'style': {
                'backgroundColor': 'steelblue',
                'color': 'white'
            }
        }
    )

    st.pydeck_chart(deck)

with col2:
    # Stats panel
    st.subheader("Statistics")
    
    # Food truck stats
    st.write("Food Trucks")
    filtered_trucks = trucks_df[trucks_df['borough'].isin(boroughs)]
    st.metric("Total Trucks", len(filtered_trucks))
    st.metric("Average Rating", f"{filtered_trucks['rating'].mean():.1f}⭐")
    
    # Top cuisines
    st.write("Top Cuisines")
    cuisine_counts = filtered_trucks['cuisine'].value_counts()
    st.bar_chart(cuisine_counts)
    
    # Borough distribution
    st.write("Borough Distribution")
    borough_counts = filtered_trucks['borough'].value_counts()
    st.bar_chart(borough_counts)

# Footer
st.markdown("---")
st.markdown("Data is simulated for demonstration purposes.")