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import streamlit as st |
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import markdown2 |
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import pdfkit |
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from io import BytesIO |
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from IPython.display import display, FileLink |
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import base64 |
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from langchain_core.messages import AIMessage, HumanMessage |
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from datetime import datetime |
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from download_chart import construct_plot |
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from kaleido.scopes.plotly import PlotlyScope |
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import pandas as pd |
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import markdown |
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from comparateur import get_table_empreintes_detailed |
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from empreinte_export import get_carbon_footprint_html |
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def colored_circle(color): |
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return f'<span style="display: inline-block; width: 15px; height: 15px; border-radius: 50%; background-color: {color};"></span>' |
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def list_to_markdown(lst): |
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return "\n".join([f'<p>{colored_circle(item["color"])} <b>{item["name"]}</b>: Pouvoir:{item["y"]}% Influence:{item["x"]}%</p>' for item in lst[:20]]) |
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def categorize(row): |
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if 50 <= row['pouvoir'] <= 100 and 0 <= row['influence'] < 50: |
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return 'Rendre satisfait' |
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elif 50 <= row['pouvoir'] <= 100 and 50 <= row['influence'] <= 100: |
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return 'Gérer étroitement' |
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elif 0 <= row['pouvoir'] < 50 and 0 <= row['influence'] < 50: |
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return 'Suivre de près' |
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elif 0 <= row['pouvoir'] < 50 and 50 <= row['influence'] <= 100: |
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return 'Tenir informé' |
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else: |
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return 'Non catégorisé' |
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@st.cache_data |
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def convert_pp_to_csv(pp_grouped): |
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if pp_grouped is None or len(pp_grouped) == 0: |
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st.error("Aucune partie prenante n'a été définie") |
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return None |
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pp_df = pd.DataFrame(pp_grouped) |
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pp_df.index.name = 'N°' |
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pp_df.rename(columns={"name": "parties prenantes", "x": "influence", "y": "pouvoir"}, inplace=True) |
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pp_df.drop(columns=['color'], inplace=True) |
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pp_df['categorie'] = pp_df.apply(categorize, axis=1) |
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pp_df = pp_df[["parties prenantes","categorie", "pouvoir", "influence"]] |
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pp_df.rename_axis('N°', axis=1) |
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return pp_df.to_csv(index=True,encoding="utf-8") |
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@st.cache_data |
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def create_pdf_from_markdown(logo_path, conversation,summary,brand_name,graph_html,app_url,list_pps,used_models=None): |
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markdown_text = "\n".join([f"### {entry['speaker']}:\n {entry['text']}\n ---" for entry in conversation]) |
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if not used_models: |
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used_models = ["Aucun modèle IA n'a été utilisé"] |
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html_used_models = "".join([f"<p>{model}</p>" for model in used_models]) |
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markdown_summary = f"{summary}\n --- \n ---" |
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markdown_list_pps = list_to_markdown(list_pps) |
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html_content = markdown.markdown(markdown_text,extensions=['markdown.extensions.tables']) |
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html_summary = markdown2.markdown(markdown_summary) |
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html_list_pps = markdown2.markdown(markdown_list_pps) |
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analysis_date = datetime.now().strftime("%Y-%m-%d") |
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graph_html.update_layout(showlegend=False) |
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img_bytes = PlotlyScope().transform( |
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figure=graph_html, |
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format="png", |
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) |
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fig1 = f"data:image/png;base64,{base64.b64encode(img_bytes).decode('utf8')}" |
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html_template = f""" |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<meta charset="UTF-8"> |
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<title>Cartographie des parties prenantes {brand_name}</title> |
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<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap" rel="stylesheet"> |
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<style> |
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body {{ |
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font-family: 'Roboto', sans-serif; |
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margin: 20px; |
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}} |
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h1, h2, h3, h4, h5, h6 {{ |
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font-weight: bold; |
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}} |
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.page-break {{ |
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page-break-before: always; |
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margin: 50px; |
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height: 50px; |
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}} |
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</style> |
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</head> |
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<body> |
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<div style="text-align: center;"> |
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<h1>Cartographie des parties prenantes "{brand_name}"</h1> |
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<p>Date de l'analyse IA RSE : {analysis_date}</p> |
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<p>IA utilisées :</p> |
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{html_used_models} |
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<img src="{logo_path}" alt="Logo" style="width: 150px;"/> |
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</div> |
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<div class="page-break"></div> |
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<div style="text-align: center; margin-top: 20px;"> |
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<img src="{fig1}"> |
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</div> |
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{html_list_pps} |
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<div class="page-break"></div> |
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<h2>RESUME</h2> |
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{html_summary} |
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<div class="page-break"></div> |
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<h2>Historique de la Conversation</h2> |
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{html_content} |
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<div class="page-break"></div> |
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{get_carbon_footprint_html()} |
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</body> |
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</html> |
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""" |
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with open("temp.html", "w",encoding="utf-8") as f: |
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f.write(html_template) |
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footer_html = f""" |
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<!DOCTYPE html> |
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<html lang="en"> |
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<head> |
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<link href="https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap" rel="stylesheet"> |
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<meta charset="UTF-8"> |
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<style> |
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body {{ |
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font-family: 'Roboto', sans-serif; |
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margin-top: 20px; |
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}} |
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.footer {{ |
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width: 100%; |
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font-size: 16px; |
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display: flex; |
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align-items: center; |
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justify-content: space-between; |
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padding: 10px 20px; |
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}} |
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.footer img {{ |
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width: 100px; |
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vertical-align: middle; |
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margin-bottom: 0px; |
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padding-bottom: 0px; |
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}} |
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.footer .center-text {{ |
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text-align: center; |
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}} |
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.footer .page-number {{ |
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text-align: right; |
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}} |
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.footer a {{ |
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color: #0000EE; |
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text-decoration: none; |
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}} |
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.page {{ |
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font-weight: bold; |
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font-size: 10px; |
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margin-bottom: 0px; |
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padding-bottom: 0px; |
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}} |
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</style> |
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</head> |
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<body> |
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<div class="footer"> |
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<img src="{logo_path}" alt="Logo" /> |
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<div class="center-text"> |
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bziiit | Open data & IA RSE | <a href="{app_url}">{app_url}</a> |
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</div> |
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<div class="page-number"> |
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<span class="page"></span> |
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</div> |
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</div> |
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</body> |
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</html> |
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""" |
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with open("footer.html", "w",encoding="utf-8") as f: |
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f.write(footer_html) |
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pdf = pdfkit.from_file("temp.html", options={ |
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'footer-html': 'footer.html', |
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'footer-right': '[page]/[toPage]', |
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'footer-font-size': '10', |
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'footer-spacing': '5', |
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'footer-line': True, |
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'margin-top': '5', |
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}) |
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return pdf |
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def get_conversation(): |
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conversation = [] |
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for message in st.session_state.chat_history: |
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if isinstance(message, AIMessage): |
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conversation.append({"speaker": "AI", "text": message.content}) |
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elif isinstance(message, HumanMessage): |
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conversation.append({"speaker": "Moi", "text": message.content}) |
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return conversation |
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def export_conversation(summary,used_models=None): |
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brand_name = st.session_state["Nom de la marque"] |
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app_url = "https://huggingface.co/spaces/bziiit/OpenData-Bordeaux-RSE" |
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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" |
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list_pps = st.session_state['pp_grouped'] |
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with st.spinner("Génération du PDF..."): |
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conversation = get_conversation() |
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image_path = "newplot.png" |
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try: |
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graph = construct_plot() |
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except Exception as e: |
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st.error("Erreur lors de la génération de la cartographie") |
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graph = "" |
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try: |
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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) |
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except Exception as e: |
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pdf = None |
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if pdf: |
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st.success("PDF généré avec succès!}") |
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else: |
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st.error("Erreur lors de la génération du PDF") |
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return pdf |
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