Qualify : service IA + endpoint ai-chat + wizard Décider (3 cercles + AI 2-rounds)

- qualify_ai_service.py : stub IA 2-allers-retours (réversibilité + urgence)
- qualify.py router : endpoint POST /ai-chat → AIChatRequest/AIChatResponse
- test_qualifier_ai.py : 11 tests A1-A7 (questions stables, done=True au 2e round)
- decisions/new.vue : wizard 4 étapes — branche mandat (liste + lien demande) /
  hors-mandat (3 cercles textarea), questions IA, résultat + boîte à outils,
  formulaire final

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/services/qualify_ai_service.py

@@ -0,0 +1,205 @@
+"""AI framing service for decision qualification.
+
+Orchestrates a 2-round conversation that clarifies reversibility and urgency
+before producing a final QualificationResult.
+
+Currently a rule-based stub — will be replaced by Qwen3.6 (MacStudio) calls
+once the local LLM endpoint is available. The interface is stable: callers
+always receive AIFrameResponse; the underlying engine is swappable.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+# ---------------------------------------------------------------------------
+# Schemas (dataclasses — no Pydantic dependency in the engine layer)
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class AIMessage:
+    role: str   # "user" | "assistant"
+    content: str
+
+
+@dataclass
+class AIQuestion:
+    id: str
+    text: str
+    options: list[str]
+
+
+@dataclass
+class AIQualifyResult:
+    decision_type: str
+    process: str
+    recommended_modalities: list[str]
+    recommend_onchain: bool
+    onchain_reason: str | None
+    confidence: str
+    collective_available: bool
+    record_in_observatory: bool
+    reasons: list[str]
+
+
+@dataclass
+class AIFrameRequest:
+    within_mandate: bool = False
+    affected_count: int | None = None
+    is_structural: bool = False
+    context: str | None = None
+    messages: list[AIMessage] | None = None
+
+    def __post_init__(self):
+        if self.messages is None:
+            self.messages = []
+
+
+@dataclass
+class AIFrameResponse:
+    done: bool
+    questions: list[AIQuestion]
+    result: AIQualifyResult | None
+    explanation: str | None
+
+
+# ---------------------------------------------------------------------------
+# Standard clarifying questions (stub — same regardless of context)
+# Real Qwen integration will generate context-aware questions
+# ---------------------------------------------------------------------------
+
+
+_CLARIFYING_QUESTIONS: list[AIQuestion] = [
+    AIQuestion(
+        id="reversibility",
+        text="Si cette décision s'avère inappropriée dans 6 mois, peut-on facilement revenir en arrière ?",
+        options=[
+            "Oui, facilement",
+            "Difficilement",
+            "Non, c'est irréversible",
+        ],
+    ),
+    AIQuestion(
+        id="urgency",
+        text="Y a-t-il une contrainte temporelle sur cette décision ?",
+        options=[
+            "Urgente (< 1 semaine)",
+            "Délai raisonnable (quelques semaines)",
+            "Pas d'urgence",
+        ],
+    ),
+]
+
+
+# ---------------------------------------------------------------------------
+# Core function
+# ---------------------------------------------------------------------------
+
+
+def ai_frame(request: AIFrameRequest) -> AIFrameResponse:
+    """Run one round of AI framing.
+
+    Round 1 (messages=[])  → return 2 clarifying questions, done=False
+    Round 2 (messages set) → parse answers, qualify, return result, done=True
+    """
+    messages = request.messages or []
+
+    # ── Round 1: no conversation yet ────────────────────────────────────────
+    if not messages:
+        return AIFrameResponse(
+            done=False,
+            questions=list(_CLARIFYING_QUESTIONS),
+            result=None,
+            explanation=None,
+        )
+
+    # ── Round 2: answers present → qualify ──────────────────────────────────
+    answers = _parse_answers(messages)
+    result = _build_result(request, answers)
+    explanation = _build_explanation(answers)
+
+    return AIFrameResponse(
+        done=True,
+        questions=[],
+        result=result,
+        explanation=explanation,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _parse_answers(messages: list[AIMessage]) -> dict[str, str]:
+    """Extract question answers from the last user message.
+
+    Expected format: "reversibility:<answer>|urgency:<answer>"
+    Anything not matching is treated as free text for context.
+    """
+    answers: dict[str, str] = {}
+    for msg in reversed(messages):
+        if msg.role == "user" and "|" in msg.content and ":" in msg.content:
+            for part in msg.content.split("|"):
+                if ":" in part:
+                    key, _, val = part.partition(":")
+                    answers[key.strip()] = val.strip()
+            break
+    return answers
+
+
+def _build_result(request: AIFrameRequest, answers: dict[str, str]) -> AIQualifyResult:
+    """Produce a qualification result enriched by the AI answers."""
+    from app.engine.qualifier import (
+        QualificationConfig,
+        QualificationInput,
+        qualify,
+    )
+
+    config = QualificationConfig()
+    inp = QualificationInput(
+        within_mandate=request.within_mandate,
+        affected_count=request.affected_count,
+        is_structural=request.is_structural,
+        context_description=request.context,
+    )
+    base = qualify(inp, config)
+
+    reasons = list(base.reasons)
+
+    # Reversibility adjustment
+    reversibility = answers.get("reversibility", "")
+    if "irréversible" in reversibility.lower():
+        reasons.append("Décision irréversible : consensus élevé recommandé.")
+        if not base.recommend_onchain and request.is_structural:
+            pass  # already handled by engine
+
+    # Urgency note
+    urgency = answers.get("urgency", "")
+    if "urgente" in urgency.lower() or "< 1" in urgency:
+        reasons.append("Urgence signalée : privilégier un protocole à délai court.")
+
+    return AIQualifyResult(
+        decision_type=base.decision_type.value,
+        process=base.process,
+        recommended_modalities=base.recommended_modalities,
+        recommend_onchain=base.recommend_onchain,
+        onchain_reason=base.onchain_reason,
+        confidence=base.confidence,
+        collective_available=base.collective_available,
+        record_in_observatory=base.record_in_observatory,
+        reasons=reasons,
+    )
+
+
+def _build_explanation(answers: dict[str, str]) -> str:
+    parts = []
+    rev = answers.get("reversibility", "")
+    urg = answers.get("urgency", "")
+    if rev:
+        parts.append(f"Réversibilité : {rev}.")
+    if urg:
+        parts.append(f"Urgence : {urg}.")
+    return " ".join(parts) if parts else "Qualification basée sur les éléments fournis."