feat: cockpit

backend/services/ai_analyzer.py

@@ -1747,7 +1747,7 @@ def ai_score_geo_risk(news: List[Dict], algo_score: Dict, log_meta: Optional[Dic
     if not get_client() or not news:
         return {
             "score": algo_score.get("score", 0), "level": algo_score.get("level", "low"),
-            "rationale": "IA indisponible — score algorithmique utilisé tel quel.",
+            "rationale": "AI unavailable — algorithmic score used as-is.",
             "top_risks": [],
         }
 
@@ -1757,31 +1757,31 @@ def ai_score_geo_risk(news: List[Dict], algo_score: Dict, log_meta: Optional[Dic
         for n in top_news
     ]
 
-    user = f"""Evalue le niveau de risque geopolitique global actuel pour les marches financiers, sur une echelle de 0 a 100.
+    user = f"""Assess the current overall geopolitical risk level for financial markets, on a scale of 0 to 100.
 
-Score algorithmique de reference (formule ponderee par categorie, a titre indicatif seulement — exerce ton propre jugement, ne le recopie pas mecaniquement) : {algo_score.get('score')}/100 ({algo_score.get('level')}).
-Repartition par categorie : {json.dumps(algo_score.get('breakdown', {}), ensure_ascii=False)}
+Reference algorithmic score (category-weighted formula, indicative only — use your own judgment, don't just copy it): {algo_score.get('score')}/100 ({algo_score.get('level')}).
+Breakdown by category: {json.dumps(algo_score.get('breakdown', {}), ensure_ascii=False)}
 
-Top actualites (triees par impact) :
+Top news (sorted by impact):
 {json.dumps(compact, ensure_ascii=False)}
 
-Consignes :
-- Un score eleve doit refleter un risque REEL et actuel pour les marches (escalade militaire active, rupture commerciale majeure, crise politique...), pas juste un volume de news.
-- Un evenement de desescalade/resolution doit FAIRE BAISSER le score meme si son impact brut est eleve.
-- Ne t'ancre pas mecaniquement sur le score algorithmique si le contexte reel (titres) justifie un score different.
-- rationale : 2-3 phrases en francais expliquant precisement pourquoi ce score, en citant les evenements les plus determinants.
-- top_risks : 3 a 5 items les plus determinants pour ce score (titres courts).
+Instructions:
+- A high score must reflect a REAL, current risk to markets (active military escalation, major trade rupture, political crisis...), not just a volume of news.
+- A de-escalation/resolution event should LOWER the score even if its raw impact is high.
+- Don't mechanically anchor on the algorithmic score if the real context (headlines) justifies a different one.
+- rationale: 2-3 sentences in English precisely explaining why this score, citing the most determining events.
+- top_risks: the 3 to 5 most determining items for this score (short titles).
 
 JSON: {{"score": <0-100 float>, "level": "low"|"medium"|"high"|"extreme", "rationale": "...", "top_risks": ["...", ...]}}"""
 
     result = _chat(
-        "Tu es un analyste geopolitique senior qui evalue le risque marche global, pas evenement par evenement.",
+        "You are a senior geopolitical analyst assessing overall market risk, not event by event.",
         user, model="gpt-4o", json_mode=True, max_tokens=700, log_meta=log_meta,
     )
     if not result:
         return {
             "score": algo_score.get("score", 0), "level": algo_score.get("level", "low"),
-            "rationale": "Erreur IA — score algorithmique utilisé tel quel.",
+            "rationale": "AI error — algorithmic score used as-is.",
             "top_risks": [],
         }