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 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
    trucks = pd.DataFrame({
        'name': [f'Food Truck #{i}' for i in range(n_trucks)],
        'latitude': np.random.uniform(40.65, 40.85, n_trucks),
        'longitude': np.random.uniform(-74.1, -73.9, n_trucks),
        'borough': np.random.choice(['Manhattan', 'Brooklyn', 'Queens', 'Bronx', 'Staten Island'], n_trucks),
        'cuisine': np.random.choice(['Hot Dogs', 'Tacos', 'Halal', 'Ice Cream', 'BBQ'], n_trucks),
        'rating': np.random.uniform(3.0, 5.0, n_trucks)
    })
    
    # Pedestrian activity (heatmap)
    n_ped = 1000
    pedestrians = pd.DataFrame({
        'latitude': np.random.normal(40.75, 0.1, n_ped),
        'longitude': np.random.normal(-73.98, 0.1, n_ped),
        'weight': np.random.exponential(1, n_ped)
    })
    
    return trucks, pedestrians

# Load data
trucks_df, pedestrians_df = generate_mock_data()

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

# Time controls
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="MM/DD/YY"
)

# Borough filter
selected_boroughs = st.sidebar.multiselect(
    "Borough Filter",
    options=['All'] + list(trucks_df['borough'].unique()),
    default=['All']
)

# Map style
map_style = st.sidebar.selectbox(
    "Map Style",
    options=['light', 'dark', 'satellite'],
    index=0
)

# Layer toggles
st.sidebar.subheader("Layers")
show_trucks = st.sidebar.checkbox("Show Food Trucks", True)
show_heatmap = st.sidebar.checkbox("Show Pedestrian Heatmap", True)

# 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=45
    )

    # Create layers
    layers = []
    
    if show_trucks:
        trucks_layer = pdk.Layer(
            "ScatterplotLayer",
            data=trucks_df,
            get_position=['longitude', 'latitude'],
            get_color=[255, 0, 0, 160],
            get_radius=100,
            pickable=True,
            opacity=0.8,
            stroked=True,
            filled=True
        )
        layers.append(trucks_layer)
    
    if show_heatmap:
        heatmap_layer = pdk.Layer(
            "HeatmapLayer",
            data=pedestrians_df,
            get_position=['longitude', 'latitude'],
            get_weight='weight',
            radiusPixels=60,
            opacity=0.6
        )
        layers.append(heatmap_layer)

    # Create deck
    deck = pdk.Deck(
        map_style=f"mapbox://styles/mapbox/{map_style}-v9",
        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": "white",
                "color": "black"
            }
        }
    )

    st.pydeck_chart(deck)

with col2:
    st.subheader("Statistics")
    
    # Food truck stats
    st.write("Food Trucks by Borough")
    borough_counts = trucks_df['borough'].value_counts()
    fig = px.pie(values=borough_counts.values, 
                 names=borough_counts.index, 
                 hole=0.3)
    st.plotly_chart(fig, use_container_width=True)
    
    # Cuisine distribution
    st.write("Cuisine Distribution")
    cuisine_counts = trucks_df['cuisine'].value_counts()
    fig = px.bar(x=cuisine_counts.index, 
                 y=cuisine_counts.values)
    st.plotly_chart(fig, use_container_width=True)
    
    # Average ratings
    st.write("Average Ratings by Cuisine")
    avg_ratings = trucks_df.groupby('cuisine')['rating'].mean().round(2)
    st.dataframe(avg_ratings)

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