Update app.py

app.py

@@ -1,10 +1,10 @@
 # -*- coding: utf-8 -*-
 """
 Verbatify — Analyse sémantique NPS (Paste-only, NPS inféré)
+Interface simplifiée : toutes les options sont appliquées automatiquement.
 """
 
 import os, re, json, collections, tempfile, zipfile
-from pathlib import Path
 from typing import List, Dict, Optional
 import pandas as pd
 import gradio as gr
@@ -12,28 +12,47 @@ import plotly.express as px
 import plotly.graph_objects as go
 import plotly.io as pio
 
-# ====================== CSS externe (charge verbatim.css OU verbatify.css) ======================
-def load_css() -> str:
-    candidates = [
-        "verbatim.css", "verbatify.css",              # racine
-        "assets/verbatim.css", "assets/verbatify.css" # sous-dossier éventuel
-    ]
-    here = Path(__file__).parent
-    for name in candidates:
-        p = here / name
-        if p.exists():
-            css = p.read_text(encoding="utf-8")
-            print(f"[Verbatify] CSS chargé: {p.name}")
-            return css
-    print("[Verbatify] ⚠️ Aucun fichier CSS trouvé (verbatim.css / verbatify.css).")
-    # filet de sécurité visuel
-    return """
-    html,body,.gradio-container{background:#ffffff!important;color:#0F172A!important}
-    """
-
-VB_CSS = load_css()
-
-# ====================== PLOTLY THEME ======================
+# ====================== CSS (externe si présent, sinon fallback) ======================
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+CSS_FILE = os.path.join(BASE_DIR, "verbatim.css")
+
+VB_CSS_FALLBACK = r"""
+@import url('https://fonts.googleapis.com/css2?family=Manrope:wght@400;500;700;800&display=swap');
+:root{--vb-bg:#F8FAFC;--vb-text:#0F172A;--vb-primary:#7C3AED;--vb-primary-2:#06B6D4;--vb-border:#E2E8F0;}
+*{color-scheme:light !important}
+html,body,.gradio-container{background:var(--vb-bg)!important;color:var(--vb-text)!important;
+  font-family:Manrope,system-ui,-apple-system,'Segoe UI',Roboto,Arial,sans-serif!important}
+.gradio-container{max-width:1120px!important;margin:0 auto!important}
+.vb-hero{display:flex;align-items:center;gap:16px;padding:20px 22px;margin:10px 0 20px;
+  background:linear-gradient(90deg,rgba(124,58,237,.18),rgba(6,182,212,.18));border:1px solid var(--vb-border);
+  border-radius:14px;box-shadow:0 10px 26px rgba(2,6,23,.08)}
+.vb-hero .vb-title{font-size:22px;color:#0F172A;font-weight:500}
+.vb-hero .vb-sub{font-size:13px;color:#0F172A}
+.gradio-container .vb-cta{background:linear-gradient(90deg,var(--vb-primary),var(--vb-primary-2))!important;color:#fff!important;
+  border:0!important;font-weight:700!important;font-size:16px!important;padding:14px 18px!important;border-radius:14px!important;
+  box-shadow:0 10px 24px rgba(124,58,237,.28)}
+.gradio-container .vb-cta:hover{transform:translateY(-2px);filter:brightness(1.05)}
+/* Patch encarts vides & texte noir partout */
+.gradio-container .empty,
+.gradio-container [class*="unpadded_box"],
+.gradio-container [class*="unpadded-box"],
+.gradio-container .empty[class*="box"]{background:#FFFFFF!important;background-image:none!important;border:1px solid transparent!important;box-shadow:none!important}
+.gradio-container .empty *, .gradio-container [class*="unpadded_box"] *{color:#0F172A!important;fill:#0F172A!important}
+"""
+
+VB_CSS = None
+try:
+    if os.path.exists(CSS_FILE):
+        with open(CSS_FILE, "r", encoding="utf-8") as f:
+            VB_CSS = f.read()
+except Exception:
+    VB_CSS = None
+if not VB_CSS:
+    VB_CSS = VB_CSS_FALLBACK
+
+# ====================== Plotly theme ======================
+
 def apply_plotly_theme():
     pio.templates["verbatify"] = go.layout.Template(
         layout=go.Layout(
@@ -53,11 +72,12 @@ LOGO_SVG = """<svg xmlns='http://www.w3.org/2000/svg' width='224' height='38' vi
   <g fill='none' fill-rule='evenodd'>
     <rect x='0' y='7' width='38' height='24' rx='12' fill='url(#g)'/>
     <circle cx='13' cy='19' r='5' fill='#fff' opacity='0.95'/><circle cx='25' cy='19' r='5' fill='#fff' opacity='0.72'/>
-    <text x='46' y='25' font-family='Manrope, system-ui, -apple-system, Segoe UI, Roboto, Arial, sans-serif' font-size='20' font-weight='500' fill='#0F172A' letter-spacing='0.2'>Verbatify</text>
+    <text x='46' y='25' font-family='Manrope, system-ui, -apple-system, Segoe UI, Roboto, Arial, sans-serif' font-size='20' font-weight='800' fill='#0F172A' letter-spacing='0.2'>Verbatify</text>
   </g>
 </svg>"""
 
-# ====================== UNIDECODE (fallback) ======================
+# ====================== unidecode fallback ======================
+
 try:
     from unidecode import unidecode
 except Exception:
@@ -68,7 +88,8 @@ except Exception:
         except Exception:
             return str(x)
 
-# ====================== THÉSAURUS ======================
+# ====================== Thésaurus ASSURANCE ======================
+
 THEMES = {
     "Remboursements santé":[r"\bremboursement[s]?\b", r"\bt[eé]l[eé]transmission\b", r"\bno[eé]mie\b",
         r"\bprise\s*en\s*charge[s]?\b", r"\btaux\s+de\s+remboursement[s]?\b", r"\b(ameli|cpam)\b",
@@ -99,30 +120,42 @@ THEMES = {
     "Agence / Accueil":[r"\bagence[s]?\b", r"\bboutique[s]?\b", r"\baccueil\b", r"\bconseil[s]?\b", r"\battente\b", r"\bcaisse[s]?\b"],
 }
 
-# ====================== SENTIMENT (règles) ======================
-POS_WORDS = {"bien":1.0,"super":1.2,"parfait":1.4,"excellent":1.5,"ravi":1.2,"satisfait":1.0,
-             "rapide":0.8,"efficace":1.0,"fiable":1.0,"simple":0.8,"facile":0.8,"clair":0.8,"conforme":0.8,
-             "sympa":0.8,"professionnel":1.0,"réactif":1.0,"reactif":1.0,"compétent":1.0,"competent":1.0,
-             "top":1.2,"recommande":1.2,"recommandé":1.2,"bon":0.8}
-NEG_WORDS = {"mauvais":-1.2,"horrible":-1.5,"nul":-1.2,"lent":-0.8,"cher":-0.9,"arnaque":-1.5,
-             "déçu":-1.2,"decu":-1.2,"incompétent":-1.3,"bug":-0.9,"bogue":-0.9,"problème":-1.0,
-             "probleme":-1.0,"attente":-0.6,"retard":-0.9,"erreur":-1.0,"compliqué":-0.8,"complique":-0.8,
-             "défectueux":-1.3,"defectueux":-1.3,"non conforme":-1.2,"impossible":-1.0,"difficile":-0.7}
+# ====================== Sentiment (règles) ======================
+
+POS_WORDS = {
+    "bien":1.0,"super":1.2,"parfait":1.4,"excellent":1.5,"ravi":1.2,"satisfait":1.0,
+    "rapide":0.8,"efficace":1.0,"fiable":1.0,"simple":0.8,"facile":0.8,"clair":0.8,"conforme":0.8,
+    "sympa":0.8,"professionnel":1.0,"réactif":1.0,"reactif":1.0,"compétent":1.0,"competent":1.0,
+    "top":1.2,"recommande":1.2,"recommandé":1.2,"bon":0.8
+}
+NEG_WORDS = {
+    "mauvais":-1.2,"horrible":-1.5,"nul":-1.2,"lent":-0.8,"cher":-0.9,"arnaque":-1.5,
+    "déçu":-1.2,"decu":-1.2,"incompétent":-1.3,"bug":-0.9,"bogue":-0.9,"problème":-1.0,
+    "probleme":-1.0,"attente":-0.6,"retard":-0.9,"erreur":-1.0,"compliqué":-0.8,"complique":-0.8,
+    "défectueux":-1.3,"defectueux":-1.3,"non conforme":-1.2,"impossible":-1.0,"difficile":-0.7
+}
 NEGATIONS = [r"\bpas\b", r"\bjamais\b", r"\bplus\b", r"\baucun[e]?\b", r"\brien\b", r"\bni\b", r"\bgu[eè]re\b"]
 INTENSIFIERS = [r"\btr[eè]s\b", r"\bvraiment\b", r"\bextr[eê]mement\b", r"\bhyper\b"]
 DIMINISHERS  = [r"\bun[e]?\s+peu\b", r"\bassez\b", r"\bplut[oô]t\b", r"\bl[eé]g[eè]rement\b"]
 INTENSIFIER_W, DIMINISHER_W = 1.5, 0.7
 
-# ====================== OpenAI (optionnel) ======================
+# ====================== OpenAI (auto) ======================
+
 OPENAI_AVAILABLE = False
 try:
-  from openai import OpenAI
-  if os.getenv("OPENAI_API_KEY"):
-    _client = OpenAI(); OPENAI_AVAILABLE = True
+    from openai import OpenAI
+    if os.getenv("OPENAI_API_KEY"):
+        _client = OpenAI()
+        OPENAI_AVAILABLE = True
 except Exception:
-  OPENAI_AVAILABLE = False
+    OPENAI_AVAILABLE = False
+
+OA_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
+OA_TEMP  = float(os.getenv("OPENAI_TEMP", "0.1"))
+TOP_K    = int(os.getenv("VERBATIFY_TOPK", "10"))
+
+# ====================== Utils ======================
 
-# ====================== UTILS ======================
 def normalize(t:str)->str:
     if not isinstance(t,str): return ""
     return re.sub(r"\s+"," ",t.strip())
@@ -182,18 +215,22 @@ def anonymize(t:str)->str:
     t=re.sub(r"\b(?:\+?\d[\s.-]?){7,}\b","[tel]",t)
     return t
 
-def df_from_pasted(text:str, sep="|", has_score=False) -> pd.DataFrame:
+# --------- Coller du texte → DataFrame (AUTO : détecte "| note" en fin de ligne) ----------
+def df_from_pasted_auto(text:str) -> pd.DataFrame:
     lines = [l.strip() for l in (text or "").splitlines() if l.strip()]
     rows = []
+    pat = re.compile(r"\|\s*(-?\d{1,2})\s*$")
     for i, line in enumerate(lines, 1):
-        if has_score and sep in line:
-            verb, score = line.split(sep, 1)
-            rows.append({"id": i, "comment": verb.strip(), "nps_score": pd.to_numeric(score.strip(), errors="coerce")})
+        m = pat.search(line)
+        if m:
+            verb = line[:m.start()].strip()
+            score = m.group(1)
+            rows.append({"id": i, "comment": verb, "nps_score": pd.to_numeric(score, errors="coerce")})
         else:
-            rows.append({"id": i, "comment": line.strip(), "nps_score": None})
+            rows.append({"id": i, "comment": line, "nps_score": None})
     return pd.DataFrame(rows)
 
-# ---------- OpenAI helpers ----------
+# --------- OpenAI helpers (auto) ----------
 def openai_json(model:str, system:str, user:str, temperature:float=0.0) -> Optional[dict]:
     if not OPENAI_AVAILABLE: return None
     try:
@@ -207,26 +244,26 @@ def openai_json(model:str, system:str, user:str, temperature:float=0.0) -> Optio
     except Exception:
         return None
 
-def oa_sentiment(comment:str, model:str, temperature:float=0.0) -> Optional[dict]:
+def oa_sentiment(comment:str) -> Optional[dict]:
     system = "Tu es un classifieur FR. Réponds strictement en JSON."
     user = f'Texte: {comment}\nDonne "label" parmi ["positive","neutre","negatif"] et "score" entre -4 et 4. JSON.'
-    return openai_json(model, system, user, temperature)
+    return openai_json(OA_MODEL, system, user, OA_TEMP)
 
-def oa_themes(comment:str, model:str, temperature:float=0.0) -> Optional[dict]:
+def oa_themes(comment:str) -> Optional[dict]:
     system = "Tu maps le texte client vers un thésaurus assurance. Réponds strictement en JSON."
     user = f"Texte: {comment}\nThésaurus: {json.dumps(list(THEMES.keys()), ensure_ascii=False)}\nRetourne {{'themes': [...], 'counts': {{...}}}}"
-    return openai_json(model, system, user, temperature)
+    return openai_json(OA_MODEL, system, user, OA_TEMP)
 
-def oa_summary(nps:Optional[float], dist:Dict[str,int], themes_df:pd.DataFrame, model:str, temperature:float=0.2) -> Optional[str]:
+def oa_summary(nps:Optional[float], dist:Dict[str,int], themes_df:pd.DataFrame) -> Optional[str]:
     system = "Tu es un analyste CX FR. Donne une synthèse courte et actionnable en Markdown."
     top = [] if themes_df is None else themes_df.head(6).to_dict(orient="records")
     user = f"Données: NPS={None if nps is None else round(nps,1)}, Répartition={dist}, Thèmes={json.dumps(top, ensure_ascii=False)}"
-    j = openai_json(model, system, user, temperature)
+    j = openai_json(OA_MODEL, system, user, 0.2)
     if isinstance(j, dict) and "text" in j: return j["text"]
     if isinstance(j, dict): return ' '.join(str(v) for v in j.values())
     return None
 
-# ---------- HF sentiment (optionnel) ----------
+# --------- HF sentiment (optionnel)
 def make_hf_pipe():
     try:
         from transformers import pipeline
@@ -236,7 +273,7 @@ def make_hf_pipe():
     except Exception:
         return None
 
-# ---------- Inférence NPS ----------
+# --------- Inférence NPS depuis le sentiment ----------
 def infer_nps_from_sentiment(label: str, score: float) -> int:
     scaled = int(round((float(score) + 4.0) * 1.25))  # -4 -> 0, 0 -> 5, +4 -> 10
     scaled = max(0, min(10, scaled))
@@ -246,7 +283,8 @@ def infer_nps_from_sentiment(label: str, score: float) -> int:
         return min(6, scaled)
     return 8 if score >= 0 else 7
 
-# ====================== GRAPHIQUES ======================
+# ====================== Graphiques ======================
+
 def fig_nps_gauge(nps: Optional[float]) -> go.Figure:
     v = 0.0 if nps is None else float(nps)
     return go.Figure(go.Indicator(mode="gauge+number", value=v,
@@ -271,53 +309,49 @@ def fig_theme_balance(themes_df: pd.DataFrame, k: int) -> go.Figure:
     fig = px.bar(d2, x="theme", y="count", color="type", barmode="stack", title=f"Top {k} thèmes — balance Pos/Neg")
     fig.update_layout(xaxis_tickangle=-30); return fig
 
-# ====================== ANALYSE ======================
-def analyze_text(pasted_txt, has_sc, sep_chr,
-                 do_anonymize, use_oa_sent, use_oa_themes, use_oa_summary,
-                 oa_model, oa_temp, top_k):
+# ====================== Analyse principale (AUTO) ======================
 
-    df = df_from_pasted(pasted_txt or "", sep=sep_chr or "|", has_score=bool(has_sc))
+def analyze_text(pasted_txt: str):
+    # 1) Parse auto + anonymisation
+    df = df_from_pasted_auto(pasted_txt or "")
     if df.empty:
         raise gr.Error("Colle au moins un verbatim (une ligne).")
+    df["comment"] = df["comment"].apply(anonymize)
 
-    if do_anonymize:
-        df["comment"]=df["comment"].apply(anonymize)
-
-    if (use_oa_sent or use_oa_themes or use_oa_summary) and not OPENAI_AVAILABLE:
+    # 2) Pipes
+    use_oa_sent = use_oa_themes = use_oa_summary = True
+    if not OPENAI_AVAILABLE:
         use_oa_sent = use_oa_themes = use_oa_summary = False
-
     hf_pipe = make_hf_pipe()
-    def hf_sent(text:str):
-        if hf_pipe is None or not text.strip(): return None
-        try:
-            res=hf_pipe(text); lab=str(res[0]["label"]).lower(); p=float(res[0].get("score",0.5))
-            if "1" in lab or "2" in lab: return {"label":"negatif","score":-4*p}
-            if "3" in lab:               return {"label":"neutre","score":0.0}
-            return {"label":"positive","score":4*p}
-        except Exception:
-            return None
 
+    # 3) Boucle verbatims
     rows=[]; theme_agg=collections.defaultdict(lambda:{"mentions":0,"pos":0,"neg":0})
     used_hf=False; used_oa=False; any_inferred=False
 
     for idx, r in df.iterrows():
         cid=r.get("id", idx+1); comment=normalize(str(r["comment"]))
 
-        # Sentiment
+        # Sentiment: OpenAI -> HF -> règles
         sent=None
         if use_oa_sent:
-            sent=oa_sentiment(comment, oa_model, float(oa_temp or 0.0)); used_oa = used_oa or bool(sent)
-        if not sent:
-            hf=hf_sent(comment)
-            if hf: sent=hf; used_hf=True
+            sent=oa_sentiment(comment); used_oa = used_oa or bool(sent)
+        if not sent and hf_pipe is not None and comment.strip():
+            try:
+                res=hf_pipe(comment); lab=str(res[0]["label"]).lower(); p=float(res[0].get("score",0.5))
+                if "1" in lab or "2" in lab: sent = {"label":"negatif","score":-4*p}
+                elif "3" in lab:           sent = {"label":"neutre","score":0.0}
+                else:                      sent = {"label":"positive","score":4*p}
+                used_hf=True
+            except Exception:
+                sent=None
         if not sent:
             s=float(lexical_sentiment_score(comment))
             sent={"label":lexical_sentiment_label(s),"score":s}
 
-        # Thèmes
+        # Thèmes: regex (+ fusion OpenAI)
         themes, counts = detect_themes_regex(comment)
         if use_oa_themes:
-            tjson=oa_themes(comment, oa_model, float(oa_temp or 0.0))
+            tjson=oa_themes(comment)
             if isinstance(tjson, dict):
                 used_oa=True
                 for th, c in (tjson.get("counts",{}) or {}).items():
@@ -325,7 +359,7 @@ def analyze_text(pasted_txt, has_sc, sep_chr,
                         counts[th] = max(counts.get(th, 0), int(c))
                 themes = [th for th, c in counts.items() if c > 0]
 
-        # NPS (existant ou inféré)
+        # Note NPS existante ou inférée
         given = r.get("nps_score", None)
         try:
             given = int(given) if given is not None and str(given).strip() != "" else None
@@ -367,7 +401,7 @@ def analyze_text(pasted_txt, has_sc, sep_chr,
     themes_df=pd.DataFrame(trs).sort_values(["total_mentions","net_sentiment"],ascending=[False,False])
 
     # Synthèse
-    method = "OpenAI + HF + règles" if (use_oa_sent and used_hf) else ("OpenAI + règles" if use_oa_sent else ("HF + règles" if used_hf else "Règles"))
+    method = "OpenAI + HF + règles" if (OPENAI_AVAILABLE and used_hf) else ("OpenAI + règles" if OPENAI_AVAILABLE else ("HF + règles" if used_hf else "Règles"))
     nps_label = "NPS global (inféré)" if any_inferred else "NPS global"
     lines=[ "# Synthèse NPS & ressentis clients",
             f"- **Méthode** : {method}",
@@ -381,8 +415,8 @@ def analyze_text(pasted_txt, has_sc, sep_chr,
             lines.append(f"- **{th}** : {m} occurrence(s)")
     summary_md="\n".join(lines)
 
-    if use_oa_summary:
-        md = oa_summary(nps, dist, themes_df, oa_model, float(oa_temp or 0.0))
+    if OPENAI_AVAILABLE:
+        md = oa_summary(nps, dist, themes_df)
         if md: summary_md = md + "\n\n---\n" + summary_md
 
     # Exports
@@ -426,7 +460,7 @@ def analyze_text(pasted_txt, has_sc, sep_chr,
     ench_md, irr_md, reco_md = make_panels(themes_df)
     fig_gauge = fig_nps_gauge(nps)
     fig_emots = fig_sentiment_bar(dist)
-    k = max(1, int(top_k or 10))
+    k = max(1, int(TOP_K))
     fig_top   = fig_top_themes(themes_df, k)
     fig_bal   = fig_theme_balance(themes_df, k)
 
@@ -434,48 +468,41 @@ def analyze_text(pasted_txt, has_sc, sep_chr,
             ench_md, irr_md, reco_md, fig_gauge, fig_emots, fig_top, fig_bal)
 
 # ====================== UI ======================
+
 apply_plotly_theme()
 
-with gr.Blocks(title="Verbatify, révélez la voix de vos assurés, simplement", css=VB_CSS) as demo:
+with gr.Blocks(title="Verbatify — Analyse NPS", css=VB_CSS) as demo:
+    # Header
     gr.HTML(
         "<div class='vb-hero'>"
         f"{LOGO_SVG}"
         "<div><div class='vb-title'>Verbatify — Analyse NPS</div>"
-        "<div class='vb-sub'>Émotions • Thématiques • Occurrences • Synthèse • NPS</div></div>"
+        "<div class='vb-sub'>Émotions • Thématiques • Occurrences • Synthèse</div></div>"
         "</div>"
     )
 
-    with gr.Column():
+    # Entrée minimale + bouton
+    with gr.Row():
         pasted = gr.Textbox(
             label="Verbatims (un par ligne)", lines=10,
             placeholder="Exemple :\nRemboursement rapide, télétransmission OK | 10\nImpossible de joindre un conseiller | 3\nEspace client : bug à la connexion | 4",
+            scale=4
         )
-        with gr.Row():
-            has_score = gr.Checkbox(label="J’ai un score NPS par ligne", value=True)  # déjà coché
-            sep = gr.Textbox(label="Séparateur score", value="|", scale=1)
-
-    with gr.Row():
-        anon=gr.Checkbox(label="Anonymiser emails / téléphones", value=True)
-        use_oa_sent=gr.Checkbox(label="OpenAI pour le sentiment", value=True)
-        use_oa_themes=gr.Checkbox(label="OpenAI pour les thèmes", value=True)
-        use_oa_summary=gr.Checkbox(label="OpenAI pour la synthèse", value=True)
-
-    with gr.Row():
-        oa_model=gr.Textbox(label="Modèle OpenAI", value="gpt-4o-mini")
-        oa_temp=gr.Slider(label="Température", minimum=0.0, maximum=1.0, value=0.1, step=0.1)
-        top_k=gr.Slider(label="Top thèmes (K) pour les graphes", minimum=5, maximum=20, value=10, step=1)
-        run=gr.Button("Lancer l'analyse", elem_classes=["vb-cta"])
+        run = gr.Button("Lancer l'analyse", elem_classes=["vb-cta"], scale=1)
 
+    # Panneaux
     with gr.Row():
         ench_panel=gr.Markdown()
         irr_panel=gr.Markdown()
         reco_panel=gr.Markdown()
 
+    # Résultats + téléchargements
     summary=gr.Markdown(label="Synthèse NPS & ressentis clients")
     themes_table=gr.Dataframe(label="Thèmes — statistiques")
     enriched_table=gr.Dataframe(label="Verbatims enrichis (aperçu)")
     files_out=gr.Files(label="Téléchargements (CSV & ZIP)")
 
+    # Graphes
     with gr.Row():
         plot_nps = gr.Plot(label="NPS — Jauge")
         plot_sent= gr.Plot(label="Répartition des émotions")
@@ -483,9 +510,10 @@ with gr.Blocks(title="Verbatify, révélez la voix de vos assurés, simplement",
         plot_top  = gr.Plot(label="Top thèmes — occurrences")
         plot_bal  = gr.Plot(label="Top thèmes — balance Pos/Neg")
 
+    # Lancer
     run.click(
         analyze_text,
-        inputs=[pasted, has_score, sep, anon, use_oa_sent, use_oa_themes, use_oa_summary, oa_model, oa_temp, top_k],
+        inputs=[pasted],
         outputs=[summary, themes_table, enriched_table, files_out,
                  ench_panel, irr_panel, reco_panel,
                  plot_nps, plot_sent, plot_top, plot_bal]
@@ -493,7 +521,7 @@ with gr.Blocks(title="Verbatify, révélez la voix de vos assurés, simplement",
 
     gr.HTML(
         '<div class="vb-footer">© Verbatify.com — Construit par '
-        '<a href="https://jeremy-lagache.fr/" target="_blank" rel="dofollow">Jérémy Lagache</a></div>'
+        '<a href="https://jeremy-lagache.fr/" target="_blank" rel="noopener">Jérémy Lagache</a></div>'
     )
 
 if __name__ == "__main__":