update

AzA march 2026/workforce_planner/api/routes_ai.py

@@ -0,0 +1,119 @@
+# -*- coding: utf-8 -*-
+"""API-Routen für KI-Services (Transkription, KG-Erstellung, Chat)."""
+
+from typing import Optional
+
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, File, Form
+from pydantic import BaseModel
+
+from ..core.models import Employee
+from .auth import get_current_user
+from ..ai import service as ai_service
+
+router = APIRouter(prefix="/ai", tags=["KI-Service"])
+
+
+# ── Transkription ──────────────────────────────
+
+class TranscribeResponse(BaseModel):
+    text: str
+    tokens_estimated: int
+
+
+@router.post("/transcribe", response_model=TranscribeResponse)
+async def transcribe(
+    file: UploadFile = File(...),
+    language: str = Form("de"),
+    _user: Employee = Depends(get_current_user),
+):
+    if not file.filename or not file.filename.lower().endswith(".wav"):
+        raise HTTPException(400, "Nur WAV-Dateien erlaubt")
+
+    wav_bytes = await file.read()
+    if len(wav_bytes) > 50 * 1024 * 1024:
+        raise HTTPException(413, "Datei zu gross (max 50 MB)")
+
+    try:
+        result = ai_service.transcribe_audio(wav_bytes, language)
+        return TranscribeResponse(**result)
+    except RuntimeError as e:
+        raise HTTPException(503, str(e))
+    except Exception as e:
+        raise HTTPException(500, f"Transkription fehlgeschlagen: {e}")
+
+
+# ── KG-Erstellung ─────────────────────────────
+
+class SummarizeRequest(BaseModel):
+    transcript: str
+    system_prompt: str = ""
+    model: str = "gpt-4o"
+
+
+class SummarizeResponse(BaseModel):
+    text: str
+    tokens_used: int
+    model: str
+
+
+@router.post("/summarize", response_model=SummarizeResponse)
+def summarize(
+    data: SummarizeRequest,
+    _user: Employee = Depends(get_current_user),
+):
+    try:
+        result = ai_service.summarize_transcript(
+            data.transcript, data.system_prompt, data.model
+        )
+        return SummarizeResponse(**result)
+    except RuntimeError as e:
+        raise HTTPException(503, str(e))
+    except Exception as e:
+        raise HTTPException(500, f"Zusammenfassung fehlgeschlagen: {e}")
+
+
+# ── KG-Merge ──────────────────────────────────
+
+class MergeRequest(BaseModel):
+    existing_kg: str
+    full_transcript: str
+    system_prompt: str = ""
+    model: str = "gpt-4o"
+
+
+@router.post("/merge", response_model=SummarizeResponse)
+def merge_kg(
+    data: MergeRequest,
+    _user: Employee = Depends(get_current_user),
+):
+    try:
+        result = ai_service.merge_kg(
+            data.existing_kg, data.full_transcript,
+            data.system_prompt, data.model,
+        )
+        return SummarizeResponse(**result)
+    except RuntimeError as e:
+        raise HTTPException(503, str(e))
+    except Exception as e:
+        raise HTTPException(500, f"Merge fehlgeschlagen: {e}")
+
+
+# ── Generischer Chat ──────────────────────────
+
+class ChatRequest(BaseModel):
+    messages: list[dict]
+    model: str = "gpt-4o"
+
+
+@router.post("/chat", response_model=SummarizeResponse)
+def chat_completion(
+    data: ChatRequest,
+    _user: Employee = Depends(get_current_user),
+):
+    try:
+        result = ai_service.chat_completion(data.messages, data.model)
+        return SummarizeResponse(**result)
+    except RuntimeError as e:
+        raise HTTPException(503, str(e))
+    except Exception as e:
+        raise HTTPException(500, f"Chat fehlgeschlagen: {e}")