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/routers/qualify.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 from dataclasses import asdict
 
 from fastapi import APIRouter, Depends
+from pydantic import BaseModel
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -18,10 +19,59 @@ from app.schemas.qualification import (
     QualifyResponse,
 )
 from app.services.auth_service import get_current_identity
+from app.services.qualify_ai_service import (
+    AIFrameRequest,
+    AIFrameResponse,
+    AIMessage,
+    AIQuestion,
+    AIQualifyResult,
+    ai_frame,
+)
 
 router = APIRouter()
 
 
+# ── Pydantic wrappers for AI chat (FastAPI needs Pydantic, not dataclasses) ──
+
+
+class AIMessagePayload(BaseModel):
+    role: str
+    content: str
+
+
+class AIChatRequest(BaseModel):
+    within_mandate: bool = False
+    affected_count: int | None = None
+    is_structural: bool = False
+    context: str | None = None
+    messages: list[AIMessagePayload] = []
+
+
+class AIQuestionOut(BaseModel):
+    id: str
+    text: str
+    options: list[str]
+
+
+class AIQualifyResultOut(BaseModel):
+    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]
+
+
+class AIChatResponse(BaseModel):
+    done: bool
+    questions: list[AIQuestionOut] = []
+    result: AIQualifyResultOut | None = None
+    explanation: str | None = None
+
+
 async def _load_config(db: AsyncSession) -> QualificationConfig:
     """Load the active QualificationProtocol from DB, or fall back to defaults."""
     result = await db.execute(
@@ -61,6 +111,32 @@ async def qualify_decision(
     return QualifyResponse(**asdict(result))
 
 
+@router.post("/ai-chat", response_model=AIChatResponse)
+async def ai_chat(payload: AIChatRequest) -> AIChatResponse:
+    """Run one round of AI-assisted qualification framing.
+
+    Round 1 (messages=[]) → returns 2 clarifying questions.
+    Round 2 (messages set) → returns final qualification result.
+
+    No auth required — advisory endpoint.
+    """
+    req = AIFrameRequest(
+        within_mandate=payload.within_mandate,
+        affected_count=payload.affected_count,
+        is_structural=payload.is_structural,
+        context=payload.context,
+        messages=[AIMessage(role=m.role, content=m.content) for m in payload.messages],
+    )
+    resp = ai_frame(req)
+
+    return AIChatResponse(
+        done=resp.done,
+        questions=[AIQuestionOut(id=q.id, text=q.text, options=q.options) for q in resp.questions],
+        result=AIQualifyResultOut(**asdict(resp.result)) if resp.result else None,
+        explanation=resp.explanation,
+    )
+
+
 @router.get("/protocol", response_model=QualificationProtocolOut | None)
 async def get_active_protocol(
     db: AsyncSession = Depends(get_db),