backup-app.py

import streamlit as st
import pandas as pd
import altair as alt

# Define list of largest hospitals with latitude and longitude
largest_hospitals = [
    {
        'name': 'Florida Hospital Orlando',
        'city': 'Orlando',
        'state': 'FL',
        'zip_code': '32803',
        'bed_count': 2411,
        'lat': 28.562229,
        'lng': -81.362976
    },
    {
        'name': 'Cleveland Clinic',
        'city': 'Cleveland',
        'state': 'OH',
        'zip_code': '44195',
        'bed_count': 1730,
        'lat': 41.501669,
        'lng': -81.621275
    },
    {
        'name': 'Mayo Clinic',
        'city': 'Rochester',
        'state': 'MN',
        'zip_code': '55905',
        'bed_count': 1372,
        'lat': 44.020634,
        'lng': -92.463476
    },
    {
        'name': 'NewYork-Presbyterian Hospital-Columbia and Cornell',
        'city': 'New York',
        'state': 'NY',
        'zip_code': '10032',
        'bed_count': 2332,
        'lat': 40.840886,
        'lng': -73.942184
    },
    {
        'name': 'UCHealth University of Colorado Hospital',
        'city': 'Aurora',
        'state': 'CO',
        'zip_code': '80045',
        'bed_count': 672,
        'lat': 39.742401,
        'lng': -104.834694
    },
    {
        'name': 'Houston Methodist Hospital',
        'city': 'Houston',
        'state': 'TX',
        'zip_code': '77030',
        'bed_count': 1063,
        'lat': 29.710292,
        'lng': -95.399262
    },
    {
        'name': 'Johns Hopkins Hospital',
        'city': 'Baltimore',
        'state': 'MD',
        'zip_code': '21287',
        'bed_count': 1293,
        'lat': 39.297082,
        'lng': -76.590726
    },
    {
        'name': 'Massachusetts General Hospital',
        'city': 'Boston',
        'state': 'MA',
        'zip_code': '02114',
        'bed_count': 1032,
        'lat': 42.363371,
        'lng': -71.068635
    },
    {
        'name': 'University of Michigan Hospitals-Michigan Medicine',
        'city': 'Ann Arbor',
        'state': 'MI',
        'zip_code': '48109',
        'bed_count': 1145,
        'lat': 42.282531,
        'lng': -83.728376
    },
    {
        'name': 'Mount Sinai Hospital',
        'city': 'New York',
        'state': 'NY',
        'zip_code': '10029',
        'bed_count': 1168,
        'lat': 40.789866,
        'lng': -73.952348
    }
]

# Convert list of largest hospitals to a Pandas DataFrame
largest_hospitals_df = pd.DataFrame(largest_hospitals)

# Define chart functions
def map_chart():
    chart = alt.Chart(largest_hospitals_df).mark_circle().encode(
        longitude='lng:Q',
        latitude='lat:Q',
        size=alt.Size('bed_count:Q', title='Bed Count'),
        color=alt.Color('state:N', legend=alt.Legend(title='State')),
        tooltip=['name', 'bed_count', 'city', 'state']
    ).properties(
        width=700,
        height=500,
        title='Location of Largest Hospitals in the US'
    )
    st.altair_chart(chart)

# Define chart buttons
st.sidebar.header('Select a Chart')
chart_options = ['Map Chart']
chart_choice = st.sidebar.selectbox('', chart_options)

# Call chart functions based on user input
if chart_choice == 'Map Chart':
    map_chart()