b4613e26f3
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>
70 lines
2.0 KiB
Python
70 lines
2.0 KiB
Python
"""
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Mail API — AI analysis and response suggestion endpoints.
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"""
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import logging
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from typing import List
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from fastapi import APIRouter, HTTPException, Query
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from .models import (
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EmailAnalysisResult,
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ResponseSuggestion,
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)
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from .mail_db import get_email
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from .ai_service import get_ai_email_service
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v1/mail", tags=["Mail"])
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@router.post("/analyze/{email_id}", response_model=EmailAnalysisResult)
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async def analyze_email(
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email_id: str,
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user_id: str = Query(..., description="User ID"),
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):
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"""Run AI analysis on an email."""
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email_data = await get_email(email_id, user_id)
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if not email_data:
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raise HTTPException(status_code=404, detail="Email not found")
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ai_service = get_ai_email_service()
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result = await ai_service.analyze_email(
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email_id=email_id,
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sender_email=email_data.get("sender_email", ""),
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sender_name=email_data.get("sender_name"),
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subject=email_data.get("subject", ""),
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body_text=email_data.get("body_text"),
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body_preview=email_data.get("body_preview"),
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)
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return result
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@router.get("/suggestions/{email_id}", response_model=List[ResponseSuggestion])
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async def get_response_suggestions(
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email_id: str,
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user_id: str = Query(..., description="User ID"),
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):
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"""Get AI-generated response suggestions for an email."""
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email_data = await get_email(email_id, user_id)
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if not email_data:
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raise HTTPException(status_code=404, detail="Email not found")
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ai_service = get_ai_email_service()
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# Use stored analysis if available
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from .models import SenderType, EmailCategory as EC
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sender_type = SenderType(email_data.get("sender_type", "unbekannt"))
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category = EC(email_data.get("category", "sonstiges"))
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suggestions = await ai_service.suggest_response(
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subject=email_data.get("subject", ""),
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body_text=email_data.get("body_text", ""),
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sender_type=sender_type,
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category=category,
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)
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return suggestions
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