app.py

import os
import pycountry
import numpy as np
import pandas as pd
import gradio as gr
from datetime import datetime
import plotly.graph_objects as go

from Request_OECD_Data import oecd_data

master_file = 'OECD_DF_CITIES_coord.csv'

def reload_data(master_file):
    if os.path.isfile(master_file):
        now = datetime.now()
        fdate = datetime.fromtimestamp(os.path.getmtime(master_file))
        delta = (now - fdate).days
        if delta > 30:
            df = oecd_data()
        else:
            df = pd.read_csv(master_file)
    else:
        df = oecd_data()
    return df

def feat_list():

    global feats
    global feat_simp
    global feat_dict

    df = reload_data(master_file)
    non_feat = ['METRO_ID', 'Metropolitan areas', 'coordinates']
    feats = [f for f in df.columns.tolist() if f not in non_feat]
    feat_simp = [f.split(' (')[0] for f in feats]
    feat_dict = dict(zip(feat_simp, feats))

    return feat_simp

feat_list()

def feat_chooser(feat):
    feat_simp = feat_list()
    index = feat_simp.index(feat)
    choosen_feat = '<h2>' + feat_dict[feat] + '</h2>'
    features = gr.Dropdown(choices=feat_simp, label='Select the economic descriptor', value=feat_simp[index], interactive=True)
    fig = plot_feat(feat)
    return choosen_feat, features, fig

def plot_feat(feat):

    feat = feat_dict[feat]

    print(feat)

    df = reload_data(master_file)
    df = df.dropna(subset=[feat])

    df = df.sort_values(by=feat, ascending=False)
    df = df.reset_index(drop=True).reset_index().rename(columns={'index': 'Rank'})
    df['Rank'] = df['Rank'] + 1
    df['digit'] = df['Rank'].astype(str).str.len().astype(int) + 2
    df['pad'] = '&nbsp;'
    df['pad']= df['pad'].str.repeat(df['digit'])

    df['Latitude'] = df['coordinates'].str.split(',').str[1].str.replace(r'\)', '', regex=True)
    df['Longitude'] = df['coordinates'].str.split(',').str[0].str.replace(r'\(', '', regex=True)

    df['ISO2'] = df['METRO_ID'].apply(lambda s: ''.join(c for c in s if c.isalpha()))
    def iso_to_iso(row):
        if len(row) > 2:
            row = pycountry.countries.get(alpha_3=row).alpha_2
        else:
            if row == 'EL':
                row = 'GR'
            elif row == 'UK':
                row = 'GB'
            else:
                row = row
        return row

    df['Country Code'] = df['ISO2'].apply(lambda x: iso_to_iso(x))
    df['Flag'] = df['Country Code'].apply(lambda x: pycountry.countries.get(alpha_2=x).flag)

    fig = go.Figure()

    fig.add_trace(go.Choropleth())

    fig.add_trace(go.Scattergeo(
        lon = df['Longitude'],
        lat = df['Latitude'],
        text = df['Rank'].astype(str) + '. ' + '<b>' + df['Metropolitan areas'] + '</b>' + '&nbsp;' + df['Flag'] + '<br>' + df['pad'] + '<b>' + '$' + df[feat].astype(str) + 'K' + '</b>',
        hoverinfo='text',
        name = '',
        mode = 'markers',
        marker = dict(
            size = df[feat],
            #sizemin=4,
            sizeref=2.*max(df[feat])/(40.**2),
            sizemode = 'area',
            color = df[feat],
            colorscale='Portland',
            showscale=True,
        ),

    ))

    fig.update_geos(projection_type='orthographic', projection_rotation = {'lat': 49.6112768, 'lon': 6.129799, 'roll': 0}, projection_scale = 1)
    fig.update_layout(height=1080, hoverlabel=dict(font_size=14,font_family='Courier New, monospace'))

    return fig

### Gradio app ###
with gr.Blocks() as demo:
    gr.HTML('<h1>OECD Functional Urban Areas</h1>')
    features = gr.Dropdown(choices=feat_simp, label='Select the economic descriptor', value=feat_simp[0], interactive=True)
    choosen_feat = gr.HTML(feat_dict[feat_simp[0]])
    world_map = gr.Plot()
    features.input(fn=feat_chooser, inputs=features, outputs=[choosen_feat, features, world_map])
    demo.load(plot_feat, [features], world_map)

port = int(os.environ.get('PORT', 7860))
demo.launch(server_name="0.0.0.0", server_port=port)