Code Editor for app.py

import streamlit as st
import pandas as pd
import pydeck as pdk
import numpy as np

st.set_page_config(layout="wide", page_title="Yakima Hop Farms Visualization")

# Generate mock data for hop farms
np.random.seed(42)
N_FARMS = 100

# Center coordinates for Yakima Valley hop growing region
YAKIMA_LAT = 46.6021
YAKIMA_LON = -120.5059

# Generate random farm locations in Yakima Valley area
lat_spread = 0.2
lon_spread = 0.3

data = pd.DataFrame({
    'farm_id': range(N_FARMS),
    'lat': YAKIMA_LAT + np.random.uniform(-lat_spread, lat_spread, N_FARMS),
    'lon': YAKIMA_LON + np.random.uniform(-lon_spread, lon_spread, N_FARMS),
    'acres': np.random.uniform(10, 100, N_FARMS),
    'borough': np.random.choice(['Moxee', 'Toppenish', 'Granger', 'Zillah'], N_FARMS),
    'hop_variety': np.random.choice(['Cascade', 'Centennial', 'Citra', 'Mosaic'], N_FARMS),
    'year_established': np.random.randint(1950, 2023, N_FARMS)
})

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

# Time filter
year_range = st.sidebar.slider(
    'Year Established',
    min_value=int(data['year_established'].min()),
    max_value=int(data['year_established'].max()),
    value=(1950, 2023)
)

# Borough filter
boroughs = st.sidebar.multiselect(
    'Boroughs',
    options=sorted(data['borough'].unique()),
    default=sorted(data['borough'].unique())
)

# Map style
map_style = st.sidebar.selectbox(
    'Map Style',
    options=['mapbox://styles/mapbox/light-v9', 
             'mapbox://styles/mapbox/dark-v9',
             'mapbox://styles/mapbox/satellite-v9']
)

# Visualization type
viz_type = st.sidebar.selectbox(
    'Visualization Type',
    options=['Scatter', 'Heatmap', 'Cluster']
)

# Filter data based on selections
filtered_data = data[
    (data['year_established'].between(year_range[0], year_range[1])) &
    (data['borough'].isin(boroughs))
]

# Main content
st.title('Yakima Valley Hop Farms')

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

with col1:
    # Create the map layer based on visualization type
    if viz_type == 'Scatter':
        layer = pdk.Layer(
            'ScatterplotLayer',
            filtered_data,
            get_position=['lon', 'lat'],
            get_radius='acres',
            radius_scale=20,
            radius_min_pixels=5,
            radius_max_pixels=30,
            get_fill_color=[255, 140, 0, 140],
            pickable=True
        )
    elif viz_type == 'Heatmap':
        layer = pdk.Layer(
            'HeatmapLayer',
            filtered_data,
            get_position=['lon', 'lat'],
            get_weight='acres',
            pickable=True
        )
    else:  # Cluster
        layer = pdk.Layer(
            'ColumnLayer',
            filtered_data,
            get_position=['lon', 'lat'],
            get_elevation='acres',
            elevation_scale=10,
            radius=50,
            pickable=True
        )

    # Create the map view
    view_state = pdk.ViewState(
        latitude=YAKIMA_LAT,
        longitude=YAKIMA_LON,
        zoom=10,
        pitch=45
    )

    # Render the map
    r = pdk.Deck(
        layers=[layer],
        initial_view_state=view_state,
        map_style=map_style,
        tooltip={
            'html': '<b>Farm ID:</b> {farm_id}<br/>'
                   '<b>Borough:</b> {borough}<br/>'
                   '<b>Acres:</b> {acres:.1f}<br/>'
                   '<b>Hop Variety:</b> {hop_variety}<br/>'
                   '<b>Established:</b> {year_established}',
            'style': {
                'backgroundColor': 'steelblue',
                'color': 'white'
            }
        }
    )

    st.pydeck_chart(r)

with col2:
    st.subheader('Summary Statistics')
    
    # Display summary stats
    st.write('**Total Farms:**', len(filtered_data))
    st.write('**Total Acreage:**', f"{filtered_data['acres'].sum():.1f}")
    
    st.subheader('Borough Distribution')
    borough_counts = filtered_data['borough'].value_counts()
    st.bar_chart(borough_counts)
    
    st.subheader('Hop Variety Distribution')
    variety_counts = filtered_data['hop_variety'].value_counts()
    st.bar_chart(variety_counts)

    st.subheader('Average Farm Size by Borough')
    avg_size = filtered_data.groupby('borough')['acres'].mean().round(1)
    st.bar_chart(avg_size)