' for item in lst[:20]])
def categorize(row):
if 50 <= row['pouvoir'] <= 100 and 0 <= row['influence'] < 50:
return 'Rendre satisfait'
elif 50 <= row['pouvoir'] <= 100 and 50 <= row['influence'] <= 100:
return 'Gérer étroitement'
elif 0 <= row['pouvoir'] < 50 and 0 <= row['influence'] < 50:
return 'Suivre de près'
elif 0 <= row['pouvoir'] < 50 and 50 <= row['influence'] <= 100:
return 'Tenir informé'
else:
return 'Non catégorisé'
@st.cache_data
def convert_pp_to_csv(pp_grouped):
if pp_grouped is None or len(pp_grouped) == 0:
st.error("Aucune partie prenante n'a été définie")
return None
pp_df = pd.DataFrame(pp_grouped)
pp_df.index.name = 'N°'
pp_df.rename(columns={"name": "parties prenantes", "x": "influence", "y": "pouvoir"}, inplace=True)
pp_df.drop(columns=['color'], inplace=True)
# Apply the function to the DataFrame to create a new column 'categorie'
pp_df['categorie'] = pp_df.apply(categorize, axis=1)
pp_df = pp_df[["parties prenantes","categorie", "pouvoir", "influence"]]
pp_df.rename_axis('N°', axis=1)
return pp_df.to_csv(index=True,encoding="utf-8")
@st.cache_data
def create_pdf_from_markdown(logo_path, conversation,summary,brand_name,graph_html,app_url,list_pps,used_models=None):
# Convertir la conversation en markdown
markdown_text = "\n".join([f"### {entry['speaker']}:\n {entry['text']}\n ---" for entry in conversation])
if not used_models:
used_models = ["Aucun modèle IA n'a été utilisé"]
html_used_models = "".join([f"
{model}
" for model in used_models])
markdown_summary = f"{summary}\n --- \n ---"
markdown_list_pps = list_to_markdown(list_pps)
# Convertir le markdown en HTML
html_content = markdown.markdown(markdown_text,extensions=['markdown.extensions.tables'])
html_summary = markdown2.markdown(markdown_summary)
html_list_pps = markdown2.markdown(markdown_list_pps)
html_table = get_table_empreintes_detailed().to_html()
analysis_date = datetime.now().strftime("%Y-%m-%d")
# image_base64 = base64.b64encode(image_path).decode('utf-8')
graph_html.update_layout(showlegend=False)
img_bytes = PlotlyScope().transform(
figure=graph_html,
format="png",
)
fig1 = f"data:image/png;base64,{base64.b64encode(img_bytes).decode('utf8')}"
# Créer le HTML complet avec les images et le texte
html_template = f"""
Cartographie des parties prenantes {brand_name}
Cartographie des parties prenantes "{brand_name}"
Date de l'analyse IA RSE : {analysis_date}
IA utilisées :
{html_used_models}
{html_list_pps}
RESUME
{html_summary}
Historique de la Conversation
{html_content}
Emission de CO2
{html_table}
{get_carbon_footprint_html()}
"""
with open("temp.html", "w",encoding="utf-8") as f:
f.write(html_template)
# Create the footer HTML with the logo and app_url
footer_html = f"""
"""
# Save the footer HTML to a temporary file
with open("footer.html", "w",encoding="utf-8") as f:
f.write(footer_html)
# Convert HTML to PDF with header and footer
pdf = pdfkit.from_file("temp.html", options={
'footer-html': 'footer.html',
'footer-right': '[page]/[toPage]',
'footer-font-size': '10',
'footer-spacing': '5',
'footer-line': True,
'margin-top': '5',
})
return pdf
def get_conversation():
conversation = []
for message in st.session_state.chat_history:
if isinstance(message, AIMessage):
conversation.append({"speaker": "AI", "text": message.content})
elif isinstance(message, HumanMessage):
conversation.append({"speaker": "Moi", "text": message.content})
return conversation
def export_conversation(summary,used_models=None):
brand_name = st.session_state["Nom de la marque"]
app_url = "https://huggingface.co/spaces/bziiit/OpenData-Bordeaux-RSE"
logo_path = "https://static.wixstatic.com/media/d7d3da_b69e03ae99224f7d8b6e358918e60071~mv2.png/v1/crop/x_173,y_0,w_1906,h_938/fill/w_242,h_119,al_c,q_85,usm_0.66_1.00_0.01,enc_auto/BZIIIT_LOGO-HORIZ-COULEUR.png" # Replace with your image path
list_pps = st.session_state['pp_grouped']
with st.spinner("Génération du PDF..."):
conversation = get_conversation()
image_path = "newplot.png"
try:
graph = construct_plot()
# graph = graph.to_html(full_html=False, include_plotlyjs='cdn')
except Exception as e:
st.error("Erreur lors de la génération de la cartographie")
graph = ""
try:
pdf = create_pdf_from_markdown(logo_path=logo_path, conversation=conversation,summary=summary,brand_name=brand_name,graph_html=graph,app_url=app_url,list_pps=list_pps,used_models=used_models)
except Exception as e:
pdf = None
if pdf:
st.success("PDF généré avec succès!}")
else:
st.error("Erreur lors de la génération du PDF")
return pdf