map_page.py

import streamlit as st
import geopandas
import pandas as pd
import folium
import random
from streamlit_folium import st_folium
import time

st.set_page_config(layout="wide")

st.title("Clinic Demand Estimator")

@st.cache_data
def read_data():
    lsoa_demographics = geopandas.read_file("lsoa_demand_demographics.geojson")
    return lsoa_demographics

@st.fragment
def mapping():

    lsoa_demographics = read_data()
    lsoa_demographics["Region"] = lsoa_demographics["LSOA21NM"].str.replace("( \d{3})\w", "", regex=True).str.strip()

    new_col = True

    df_display = lsoa_demographics.drop(
        columns = ["BNG_E", "BNG_N", "LONG", "LAT", "GlobalID", "geometry"]
    )

    df_display.insert(loc=2, column='Include', value=new_col)

    selected_regions = st.multiselect(
        "Select Regions to Include lalala",
        lsoa_demographics["Region"].drop_duplicates().sort_values().tolist(),
        default=["Exeter"]
    )

    edited_df = st.data_editor(
        df_display[df_display["Region"].isin(selected_regions)]
        )

    lsoa_demographics = pd.merge(
        lsoa_demographics,
        edited_df[edited_df["Include"] == True][["LSOA21CD"]],
        how="inner"
        )

    demand_calls = lsoa_demographics['Projected Average Daily Demand'].sum()*0.2
    demand_walkins = lsoa_demographics['Projected Average Daily Demand'].sum()*0.8

    iat_calls = 480/(lsoa_demographics['Projected Average Daily Demand'].sum()*0.2)
    iat_walkins = 480/(lsoa_demographics['Projected Average Daily Demand'].sum()*0.8)

    st.write(f"Projected Daily Demand - Calls: {demand_calls:.1f}")
    st.write(f"Average IAT: {iat_calls:.1f} minutes (assuming 480 minute day)")

    st.write(f"Projected Daily Demand - Walk-ins: {demand_walkins:.1f}")
    st.write(f"Average IAT - Walk-ins: {iat_walkins:.1f} minutes (assuming 480 minute day)")

    #create base map
    demand_demographic_map_interactive = folium.Map(
        location=[50.71671, -3.50668],
        zoom_start=9,
        tiles='cartodbpositron'
        )

    # create and add choropleth map
    choropleth = folium.Choropleth(
        geo_data=lsoa_demographics, # dataframe with geometry in it
        data=lsoa_demographics, # dataframe with data in - may be the same dataframe or a different one
        columns=['LSOA21CD', 'Projected Average Daily Demand'], # [key (field for geometry), field to plot]
        key_on='feature.properties.LSOA21CD',
        fill_color='OrRd',
        fill_opacity=0.4,
        line_weight=0.3,
        legend_name='Projected Average Daily Demand',
        highlight=True, # highlight the LSOA shape when mouse pointer enters it
        smooth_factor=0
        )

    choropleth = choropleth.add_to(demand_demographic_map_interactive)

    choropleth = choropleth.geojson.add_child(
        folium.features.GeoJsonTooltip(
            ['LSOA21CD', 'Projected Average Daily Demand'],
            labels=True
            )
    )



    st_folium(demand_demographic_map_interactive,
            use_container_width=True)


mapping()

st.divider()


st.subheader("Complex Calculation Unrelated to the Map!")

st.write("Long-running calculation being calculated...")

time.sleep(10)

st.write("Long-running calculation complete!")

st.write(f"The answer is {random.randint(100, 500)}")