Code Editor for app.py

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

st.set_page_config(layout="wide", page_title="NYC Map Visualization")

# Generate mock data
@st.cache_data
def generate_mock_data():
    # Food truck locations
    np.random.seed(42)
    n_trucks = 100
    trucks = pd.DataFrame({
        'name': [f'Food Truck {i}' for i in range(n_trucks)],
        'lat': np.random.normal(40.7128, 0.05, n_trucks),
        'lon': np.random.normal(-74.0060, 0.05, n_trucks),
        'cuisine': np.random.choice(['Mexican', 'Italian', 'Chinese', 'American', 'Indian'], 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
    n_ped = 1000
    pedestrians = pd.DataFrame({
        'lat': np.random.normal(40.7128, 0.08, n_ped),
        'lon': np.random.normal(-74.0060, 0.08, n_ped),
        'count': np.random.exponential(100, n_ped)
    })

    return trucks, pedestrians

# App layout
st.title('NYC Interactive Map')

# Sidebar controls
st.sidebar.header('Map Controls')

# Time slider
time_range = st.sidebar.slider(
    'Time Range',
    min_value=datetime(2023, 1, 1),
    max_value=datetime(2023, 12, 31),
    value=(datetime(2023, 6, 1), datetime(2023, 6, 30)),
    format='MMM DD'
)

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

# Map style
map_style = st.sidebar.radio('Map Style', ['Streets', 'Satellite'])

# Layer toggles
st.sidebar.header('Layers')
show_trucks = st.sidebar.checkbox('Food Trucks', True)
show_heatmap = st.sidebar.checkbox('Pedestrian Heatmap', True)
show_routes = st.sidebar.checkbox('Animated Routes', False)
show_clusters = st.sidebar.checkbox('Taxi Clusters', False)

# Get data
trucks_df, pedestrians_df = generate_mock_data()

# Filter data
trucks_df = trucks_df[trucks_df['borough'].isin(selected_boroughs)]

# Create map
col1, col2 = st.columns([3, 1])

with col1:
    # Initialize map centered on NYC
    m = folium.Map(
        location=[40.7128, -74.0060],
        zoom_start=12,
        tiles='cartodbpositron' if map_style == 'Streets' else 'Stamen Terrain'
    )

    # Add food truck markers
    if show_trucks:
        for idx, row in trucks_df.iterrows():
            folium.Marker(
                [row['lat'], row['lon']],
                popup=f"<b>{row['name']}</b><br>Cuisine: {row['cuisine']}<br>Rating: {row['rating']:.1f}",
                icon=folium.Icon(color='red', icon='info-sign')
            ).add_to(m)

    # Add heatmap
    if show_heatmap:
        folium.plugins.HeatMap(
            pedestrians_df[['lat', 'lon', 'count']].values.tolist(),
            radius=15,
            blur=10
        ).add_to(m)

    # Display map
    folium_static(m)

# Stats panel
with col2:
    st.header('Statistics')
    
    st.subheader('Food Trucks')
    st.write(f"Total trucks: {len(trucks_df)}")
    st.write("By borough:")
    st.write(trucks_df['borough'].value_counts())
    
    st.subheader('Cuisine Types')
    st.write(trucks_df['cuisine'].value_counts())
    
    st.subheader('Average Ratings')
    st.write(trucks_df.groupby('borough')['rating'].mean().round(2))