app.py

import os
import io
import re
import time
import requests
import pdfplumber
import numpy as np
import pandas as pd
from bs4 import BeautifulSoup
import folium
from folium.plugins import FloatImage
import gradio as gr


# Configuration
file_name = 'bathing_sites.csv'
url = 'https://eau.gouvernement.lu/fr/domaines-activite/eauxbaignade/sites-de-baignade.html'

def extract_coordinates(url):
    x_match = re.search(r'X=(\d+)', url)
    y_match = re.search(r'Y=(\d+)', url)
    
    x = int(x_match.group(1) if x_match else 0)
    y = int(y_match.group(1) if y_match else 0)
    
    R = 6378137  # Earth's radius in meters
    if x != 0:
        x = (x / R) * (180 / np.pi)
    if y != 0:
        y = (180 / np.pi) * (2 * np.arctan(np.exp(y / R)) - np.pi / 2)

    return pd.Series([x, y])

def get_coordinates(pdf_list):
	sites_list = []
	for lake in pdf_list:
		url_pdf = 'https:' + lake
		response_pdf = requests.get(url_pdf)
		bytes_io = io.BytesIO(response_pdf.content)

		with pdfplumber.open(bytes_io) as pdf:
			page = pdf.pages[0]
			text = page.extract_text()
			site = text.split('\n')[1].split(' ')[-1].split('’')[-1].replace('-', ' ').title().replace('Sure', 'Sûre').strip()
			for page in pdf.pages:
				tables = page.extract_table()
				if tables and ('baignade' in tables[0][0]):
					headers = tables[0]
					headers = headers[:3]
					headers.append('Sector')
					headers.append('Lake')
					i = 1
					for table in tables[1:]:
						table = table[:3]
						if (site == 'Weiswampach') or  (site == 'Remerschen'):
							table.append('Zone' + ' ' + str(i))
						elif site == 'Echternach':
							table.append('Designated Zone')
						else:
							table.append(table[0].split(' ')[1].strip())
						table.append(site)
						sites_list.append(table)
						i  += 1

	df = pd.DataFrame(sites_list, columns = headers)
	df = df.dropna()
	df = df.iloc[:, 1 : ]
	df = df.iloc[:, ::-1]
	df.columns = ['Lake', 'Sector', 'Y', 'X']
	df[['Y', 'X']] = df[['Y', 'X']].apply(pd.to_numeric, errors='coerce')
	df = df.drop_duplicates(subset = ['Lake', 'Sector'], keep = 'last').reset_index(drop = True)
	return df

    
def file_download():
    df = pd.read_html(url)[0]

    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')

    df['images'] = [tag.find("img")["src"] for tag in soup.select("td:has(img)")]
    df['URL coordinates'] = [tag.find("a")["href"] for tag in soup.select("td:has(a)") if 'geoportail' in tag.find("a")["href"]]
    pdf_list = [tag.find("a")["href"] for tag in soup.select("td:has(a)") if 'pdf' in tag.find("a")["href"]]
    df_coord = get_coordinates(pdf_list)
    
    df.columns = ['Lake', 'Sector', 'Water Quality', 'Swimming allowed', 'Reason for ban', 'Traffic lights', 'URL coordinates']

    name_trim = ['Lac de la ', 'Lac de ', 'Etangs de ', 'Lac d\'']
    quality_dict = {'Excellente': 'Excellent', 'Bonne': 'Good', 'Suffisante': 'Adequate', 'Insuffisante': 'Inadequate'}
    df['Water Quality'] = df['Water Quality'].map(quality_dict).fillna(df['Water Quality'])
    df['Lake'] = df['Lake'].str.replace('|'.join(name_trim), '', regex=True)
    df['Lake'] = df['Lake'].str.split('(').str[0].str.strip()
    df['Sector'] = df['Sector'].astype(str).apply(lambda x: 'Designated Zone' if 'baignade' in x else x)
    df['Reason for ban'] = df['Reason for ban'].astype(str).apply(lambda x: 'nan' if '* Les informations ' in x else x)
    df['Reason for ban'] = df['Reason for ban'].replace({'nan': 'No ban'})
    df['Swimming allowed'] = df['Swimming allowed'].astype('string')
    df.loc[df['Traffic lights'].str.contains('greng'), 'Swimming allowed'] = 'Yes'
    df.loc[df['Traffic lights'].str.contains('roud'), 'Swimming allowed'] = 'No'
    df = df.fillna('N/A')
    
    df[['long', 'lat']] = df['URL coordinates'].apply(extract_coordinates)
    df[['long', 'lat']] = df[['long', 'lat']].apply(pd.to_numeric, errors='coerce')
	
    df = df.reset_index(drop = True)
    df = pd.merge(left=df, right=df_coord, how='left', left_on=['Lake', 'Sector'], right_on=['Lake', 'Sector'])
    
    df.loc[df['long']==0, 'long'] = np.nan
    df.loc[df['lat']==0, 'lat'] = np.nan
    df['long'] = df['long'].fillna(df['X'])
    df['lat'] = df['lat'].fillna(df['Y'])

    df.drop(columns=['Traffic lights', 'URL coordinates', 'X', 'Y'], inplace=True)

    df.to_csv(file_name, index=False)
    return df

def load_data(force_refresh=False):
    if force_refresh or (not os.path.exists(file_name)) or ((time.time() - os.path.getmtime(file_name)) > 3600):
        return file_download()
    return pd.read_csv(file_name)

def create_map(force_refresh=False):
    df = load_data(force_refresh)
    
    # Create base map with Luxembourg coordinates
    if df.empty:
        m = folium.Map(location=[49.8153, 6.1296], zoom_start=9)
    else:
        m = folium.Map(location=[df['lat'].mean(), df['long'].mean()], zoom_start=9)
        
        # Add markers
        for _, row in df.iterrows():
            color = 'green' if row['Swimming allowed'] == 'Yes' else \
                    'red' if row['Swimming allowed'] == 'No' else 'gray'
            
            popup_text = f"""
            <b>Lake:</b> {row['Lake']}<br>
            <b>Sector:</b> {row['Sector']}<br>
            <b>Latitude:</b> {row['lat']:.6f}<br>
            <b>Longitude:</b> {row['long']:.6f}<br>
            <b>Water Quality:</b> {row['Water Quality']}<br>
            <b>Swimming allowed:</b> {row['Swimming allowed']}<br>
            <b>Reason for ban:</b> {row['Reason for ban']}
            """
            
            folium.CircleMarker(
                location=[row['lat'], row['long']],
                radius=8,
                color=color,
                fill=True,
                fill_color=color,
                fill_opacity=0.7,
                popup=folium.Popup(popup_text, max_width=300)
            ).add_to(m)
    
    # Use OpenStreetMap tiles
    folium.TileLayer('openstreetmap').add_to(m)
    
    # Remove attribution completely
    m.get_root().html.add_child(folium.Element("""
        <style>
            .leaflet-control-attribution {
                display: none !important;
            }
        </style>
    """))
    
    # Return HTML representation
    return m._repr_html_()

# Create Gradio interface
with gr.Blocks(title="LuxSplash") as app:
    gr.Markdown("# 🏊‍♂️ LuxSplash")
    gr.Markdown("[Freedom Luxembourg](https://freeletz.lu)")
    
    with gr.Row():
        refresh_btn = gr.Button("Refresh Data", variant="primary")
    
    map_html = gr.HTML()
    
    # Initial load
    app.load(fn=lambda: create_map(False), inputs=None, outputs=map_html)
    
    # Refresh functionality
    refresh_btn.click(
        fn=lambda: create_map(True),
        inputs=None,
        outputs=map_html
    )
    gr.Markdown(
        "Data sourced from the official Luxembourg government website, the only authoritative source for bathing site information: "
        "[eau.gouvernement.lu](https://eau.gouvernement.lu/fr/domaines-activite/eauxbaignade/sites-de-baignade.html )"
    )


if __name__ == "__main__":
    app.launch()