[split-required] Split 500-850 LOC files (batch 2)

backend-lehrer (10 files):
- game/database.py (785 → 5), correction_api.py (683 → 4)
- classroom_engine/antizipation.py (676 → 5)
- llm_gateway schools/edu_search already done in prior batch

klausur-service (12 files):
- orientation_crop_api.py (694 → 5), pdf_export.py (677 → 4)
- zeugnis_crawler.py (676 → 5), grid_editor_api.py (671 → 5)
- eh_templates.py (658 → 5), mail/api.py (651 → 5)
- qdrant_service.py (638 → 5), training_api.py (625 → 4)

website (6 pages):
- middleware (696 → 8), mail (733 → 6), consent (628 → 8)
- compliance/risks (622 → 5), export (502 → 5), brandbook (629 → 7)

studio-v2 (3 components):
- B2BMigrationWizard (848 → 3), CleanupPanel (765 → 2)
- dashboard-experimental (739 → 2)

admin-lehrer (4 files):
- uebersetzungen (769 → 4), manager (670 → 2)
- ChunkBrowserQA (675 → 6), dsfa/page (674 → 5)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

klausur-service/backend/mail/api_ai.py

@@ -0,0 +1,69 @@
+"""
+Mail API — AI analysis and response suggestion endpoints.
+"""
+
+import logging
+from typing import List
+
+from fastapi import APIRouter, HTTPException, Query
+
+from .models import (
+    EmailAnalysisResult,
+    ResponseSuggestion,
+)
+from .mail_db import get_email
+from .ai_service import get_ai_email_service
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/mail", tags=["Mail"])
+
+
+@router.post("/analyze/{email_id}", response_model=EmailAnalysisResult)
+async def analyze_email(
+    email_id: str,
+    user_id: str = Query(..., description="User ID"),
+):
+    """Run AI analysis on an email."""
+    email_data = await get_email(email_id, user_id)
+    if not email_data:
+        raise HTTPException(status_code=404, detail="Email not found")
+
+    ai_service = get_ai_email_service()
+    result = await ai_service.analyze_email(
+        email_id=email_id,
+        sender_email=email_data.get("sender_email", ""),
+        sender_name=email_data.get("sender_name"),
+        subject=email_data.get("subject", ""),
+        body_text=email_data.get("body_text"),
+        body_preview=email_data.get("body_preview"),
+    )
+
+    return result
+
+
+@router.get("/suggestions/{email_id}", response_model=List[ResponseSuggestion])
+async def get_response_suggestions(
+    email_id: str,
+    user_id: str = Query(..., description="User ID"),
+):
+    """Get AI-generated response suggestions for an email."""
+    email_data = await get_email(email_id, user_id)
+    if not email_data:
+        raise HTTPException(status_code=404, detail="Email not found")
+
+    ai_service = get_ai_email_service()
+
+    # Use stored analysis if available
+    from .models import SenderType, EmailCategory as EC
+    sender_type = SenderType(email_data.get("sender_type", "unbekannt"))
+    category = EC(email_data.get("category", "sonstiges"))
+
+    suggestions = await ai_service.suggest_response(
+        subject=email_data.get("subject", ""),
+        body_text=email_data.get("body_text", ""),
+        sender_type=sender_type,
+        category=category,
+    )
+
+    return suggestions