Code Editor for app.py

import streamlit as st
import pydeck as pdk
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import plotly.express as px

# Page config
st.set_page_config(layout="wide", page_title="NYC Interactive Map")

# Generate mock data
@st.cache_data
def generate_mock_data():
    # Food truck locations
    np.random.seed(42)
    n_trucks = 100
    truck_data = pd.DataFrame({
        'latitude': np.random.normal(40.7128, 0.05, n_trucks),
        'longitude': np.random.normal(-74.0060, 0.05, n_trucks),
        'name': [f'Food Truck #{i}' for i in range(n_trucks)],
        'rating': np.random.uniform(3.0, 5.0, n_trucks),
        'cuisine': np.random.choice(['Mexican', 'Italian', 'Asian', 'American'], n_trucks),
        'borough': np.random.choice(['Manhattan', 'Brooklyn', 'Queens', 'Bronx', 'Staten Island'], n_trucks)
    })

    # Pedestrian activity
    n_points = 1000
    ped_data = pd.DataFrame({
        'latitude': np.random.normal(40.7128, 0.08, n_points),
        'longitude': np.random.normal(-74.0060, 0.08, n_points),
        'weight': np.random.exponential(100, n_points)
    })

    return truck_data, ped_data

# Sidebar controls
st.sidebar.title('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.selectbox(
    'Map Style',
    ['mapbox://styles/mapbox/light-v9', 
     'mapbox://styles/mapbox/dark-v9',
     'mapbox://styles/mapbox/satellite-v9']
)

# 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_routes = st.sidebar.checkbox('Animated Routes', value=False)
show_clusters = st.sidebar.checkbox('Taxi Clusters', value=False)

# Get data
truck_data, ped_data = generate_mock_data()

# Filter data based on boroughs
truck_data = truck_data[truck_data['borough'].isin(selected_boroughs)]

# Create layers
layers = []

if show_heatmap:
    heatmap_layer = pdk.Layer(
        'HeatmapLayer',
        ped_data,
        opacity=0.6,
        get_position=['longitude', 'latitude'],
        get_weight='weight',
        threshold=0.3,
    )
    layers.append(heatmap_layer)

if show_trucks:
    truck_layer = pdk.Layer(
        'ScatterplotLayer',
        truck_data,
        get_position=['longitude', 'latitude'],
        get_color=[255, 0, 0, 160],
        get_radius=100,
        pickable=True,
    )
    layers.append(truck_layer)

# Create the deck
view_state = pdk.ViewState(
    latitude=40.7128,
    longitude=-74.0060,
    zoom=11,
    pitch=45
)

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

# Layout
st.title('NYC Interactive Map')

# Map column and stats column
col1, col2 = st.columns([3, 1])

with col1:
    st.pydeck_chart(deck)

with col2:
    st.subheader('Statistics')
    
    # Food truck stats
    st.write('Food Trucks by Borough')
    borough_counts = truck_data['borough'].value_counts()
    fig = px.pie(values=borough_counts.values, 
                 names=borough_counts.index, 
                 title='Distribution by Borough')
    st.plotly_chart(fig, use_container_width=True)
    
    # Cuisine stats
    st.write('Cuisine Types')
    cuisine_counts = truck_data['cuisine'].value_counts()
    fig = px.bar(x=cuisine_counts.index, 
                 y=cuisine_counts.values,
                 title='Distribution by Cuisine')
    st.plotly_chart(fig, use_container_width=True)
    
    # Average rating
    st.metric('Average Rating', f"{truck_data['rating'].mean():.2f}⭐")
    
    # Total trucks
    st.metric('Total Food Trucks', len(truck_data))