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import streamlit as st
from data_manager_bziiit import get_bziiit_data
def display_labelled_companies():
st.markdown("## Entreprises Labellisées (source OPEN DATA bziiit)")
st.markdown("### Découvrez les entreprises engagées avec au moins un label RSE")
# Récupération des données bziiit
bziiit_data = get_bziiit_data()
# Filtrage des entreprises ayant au moins un label
labelled_companies = [brand for brand in bziiit_data if brand['type'] == 'brand' and len(brand.get('labels', [])) > 0]
# Calcul du nombre de labels par entreprise
for brand in labelled_companies:
brand['label_count'] = len(brand['labels'])
# Calcul et affichage du nombre et du pourcentage d'entreprises labellisées
total_companies = len([brand for brand in bziiit_data if brand['type'] == 'brand'])
labelled_companies_count = len(labelled_companies)
percentage_labelled = round((labelled_companies_count / total_companies) * 100)
st.markdown(f"**Nb entreprises labellisées :** {labelled_companies_count} ({percentage_labelled}%)")
# Tri des entreprises par le nombre de labels, ordre décroissant
labelled_companies.sort(key=lambda x: x['label_count'], reverse=True)
# Affichage des entreprises
for i in range(0, len(labelled_companies), 5):
cols = st.columns(5)
image_placeholders = [col.empty() for col in cols]
padding_placeholders = [col.empty() for col in cols]
text_placeholders = [col.empty() for col in cols]
for j in range(5):
if i + j < len(labelled_companies):
company = labelled_companies[i + j]
with cols[j]:
if company['logo_url'] != 'Unknown':
image_placeholders[j].image(company['logo_url'], width=100)
padding_placeholders[j].write("") # This will act as padding
text_placeholders[j].write(company['name'])
text_placeholders[j].write(f"Nb labels : {company['label_count']}")
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