Choropleth loading slow on clickData

Hi, I am creating a first Dash app and I have an issue with a slow-loading choropleth map. The relevant code + datafiles can be found here (GitHub - DataCasE/Dash_testing: public test code Dash)
I have linked two choropleth maps to each other using the clickData attribute. Map 1 shows some (dummy) data at the municipality-level for the Netherlands; Map 2 shows the same type of data at the neighborhood-level, but for only one municipality at a time. By clicking any municipality in Map 1, Map 2 will show (and center on) that municipality to show more detailed information by neighborhood. I have the functionality working with the code below, however the loading of Map 2 after clicking Map 1 takes a very long time – several seconds. I think this is due to how β€œdef create_choro2” is written but I have no idea how to try out alternatives for this.

Does anyone have examples of creating something similar with Dash, or have suggestions how to improve the code for faster loading?

from jupyter_dash import JupyterDash
import json
import dash
import pandas as pd
from dash import Dash, dcc, html, Input, Output, dash_table
import plotly
import plotly.express as px
import dash_bootstrap_components as dbc

# import data
df = pd.read_csv('dummy_data_test.csv')

with open('geo_gemeenten.json') as json_data:
     geo_muni = json.load(json_data)
with open('geo_wijken.json') as json_data:
     geo_neigh = json.load(json_data)
        
# mapbox token
token = 'pk.eyJ1IjoiY2FzcGFyLWVnYXMiLCJhIjoiY2poc3QwazFkMDNiaTNxbG1vMmJvZmVwcCJ9.Yy65IKfEEM015SvKt8OBqw'

app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])

colors = {'background': '#979797','text': '#ffffff'}

# primary choro_figure with dummy data on municipality level
choro_figure = px.choropleth_mapbox(
    df, geojson=geo_muni, color='prevalentie', color_continuous_scale=px.colors.sequential.Viridis_r,
    locations="GM_NAAM", featureidkey="properties.GM_NAAM",
    center={"lat": 52.0907, "lon": 5.1214}, 
    zoom=6, range_color=[0, 131])

choro_figure.update_layout(
    margin={"r": 0, "t": 0, "l": 0, "b": 0},
    plot_bgcolor=colors['background'],
    paper_bgcolor=colors['background'],
    font_color=colors['text'],
    mapbox = dict(
        accesstoken = token,
        style = 'mapbox://styles/caspar-egas/cl1yy8qvz001o15mujg2i3kzr'))

choro_layout = html.Div(style={'backgroundColor': colors['background']}, children=[
    dcc.Graph(id="choro_graph", figure=choro_figure, config={'displayModeBar':False}),
])

# secondary choro_figure with dummy data on neighborhood level 
def create_choro2(df_function):
    municipal_code = df_function[df_function["geo"] == "GEM"].iloc[0]["code"]
    municipal_feature = next((x for x in geo_muni["features"] if x["properties"]["GM_CODE"] == municipal_code), None)
    
    choro_figure2 = px.choropleth_mapbox(
        df_function, geojson=geo_neigh, color='prevalentie', color_continuous_scale=px.colors.sequential.Viridis_r,
        locations="WK_NAAM", featureidkey="properties.WK_NAAM",
        center= {"lat": municipal_feature["properties"]["centroid_y"], "lon": municipal_feature["properties"]["centroid_x"]},
        zoom=9.5, range_color=[0, 600])

    choro_figure2.update_layout(
        margin={"r": 0, "t": 0, "l": 0, "b": 0},
        plot_bgcolor=colors['background'],
        paper_bgcolor=colors['background'],
        font_color=colors['text'],
        mapbox = dict(
            accesstoken = token,
            style = 'mapbox://styles/caspar-egas/cl1yy8qvz001o15mujg2i3kzr'))
    
    return choro_figure2

default_selection = df[df['GM_NAAM'] == 'Utrecht']

choro_layout2 = html.Div(style={'backgroundColor': colors['background']}, children=[
    dcc.Graph(id="choro_graph2", figure=create_choro2(default_selection), config={'displayModeBar':False}),
])

@app.callback(
    Output('choro_graph2', 'figure'),
    Input('choro_graph', 'clickData'))
def update_choro2(clickData):
    if clickData:
        location = clickData['points'][0]['location']
        dff = df[df['GM_NAAM'] == location]
    else:
        dff = df[df['GM_NAAM'] == 'Utrecht']
    return create_choro2(dff)

dashlayout = dbc.Container(
    [
        dbc.Row(
            [
                dbc.Col(html.Div(children=[choro_layout]),style={'height': '400px'}, width=6),
                dbc.Col(html.Div(children=[choro_layout2]),style={'height': '400px'}, width=6),
            ],
            className="g-0",
            style={"height": "100vh", "background-color": colors['background']},
                ),
    ],
            
)


app.layout = dashlayout

if __name__ == '__main__':
    app.run_server(debug=True, use_reloader=False)