Backend: Beleg-Analyse um Kaufdatum + Shop; Analyse-Endpoint
- guess_acquired_on (Kaufdatum) und guess_shop (bekannter Shop erkannt -> id; sonst Kandidatenname zum Anlegen) ergaenzt. - Upload liefert zusaetzlich suggested_acquired_on / shop_id / shop_name. - Neuer POST /items/analyze-document: analysiert einen Beleg OHNE zu speichern (zum Vorbefuellen beim Anlegen). Gemeinsamer Helfer _doc_suggestions. - 6 neue Tests; Suite 150 gruen. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Scarriffle
@@ -24,6 +24,7 @@ from ..models import (
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User,
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)
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from ..schemas import (
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DocumentSuggestions,
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ItemCreate,
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ItemDocumentUploadOut,
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ItemOut,
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@@ -33,8 +34,10 @@ from ..schemas import (
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from ..services.items import generate_uid, item_to_out
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from ..services.warranty import (
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extract_pdf_text,
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guess_acquired_on,
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guess_price_candidates,
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guess_price_cents,
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guess_shop,
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guess_warranty_until,
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)
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@@ -208,20 +211,8 @@ def _safe_filename(name: str | None) -> str:
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return cleaned[:255] or "beleg"
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@router.post(
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"/items/{item_id}/documents",
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response_model=ItemDocumentUploadOut,
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status_code=status.HTTP_201_CREATED,
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)
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async def upload_item_document(
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item_id: int,
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file: UploadFile = File(...),
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db: Session = Depends(get_db),
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_: User = Depends(require_admin),
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) -> ItemDocumentUploadOut:
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"""Beleg (PDF oder Bild) hochladen. Bei PDF wird ein Garantieende vorgeschlagen."""
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item = _item_or_404(db, item_id)
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data = await file.read()
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def _read_document(file: UploadFile, data: bytes) -> str:
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"""Validiert Größe/Typ eines hochgeladenen Belegs und gibt den Content-Type."""
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if not data:
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raise HTTPException(status.HTTP_400_BAD_REQUEST, "Leere Datei")
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if len(data) > DOC_MAX_BYTES:
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@@ -234,6 +225,43 @@ async def upload_item_document(
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raise HTTPException(
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status.HTTP_415_UNSUPPORTED_MEDIA_TYPE, "Nur PDF oder Bild erlaubt."
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)
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return content_type
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def _doc_suggestions(
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db: Session, data: bytes, content_type: str, item_acquired_on=None
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) -> DocumentSuggestions:
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"""Garantie, Preis, Kaufdatum und Shop aus einem PDF schätzen (Bilder: leer)."""
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if content_type != "application/pdf":
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return DocumentSuggestions()
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text = extract_pdf_text(data)
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shop_id, shop_name = guess_shop(text, [(s.id, s.name) for s in db.query(Shop).all()])
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return DocumentSuggestions(
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suggested_warranty_until=guess_warranty_until(text, acquired_on=item_acquired_on),
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suggested_price_cents=guess_price_cents(text),
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suggested_price_candidates=guess_price_candidates(text),
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suggested_acquired_on=guess_acquired_on(text),
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suggested_shop_id=shop_id,
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suggested_shop_name=shop_name,
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)
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@router.post(
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"/items/{item_id}/documents",
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response_model=ItemDocumentUploadOut,
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status_code=status.HTTP_201_CREATED,
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)
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async def upload_item_document(
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item_id: int,
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file: UploadFile = File(...),
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db: Session = Depends(get_db),
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_: User = Depends(require_admin),
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) -> ItemDocumentUploadOut:
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"""Beleg (PDF oder Bild) hochladen. Bei PDF werden Garantie, Preis, Kaufdatum
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und Shop vorgeschlagen."""
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item = _item_or_404(db, item_id)
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data = await file.read()
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content_type = _read_document(file, data)
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doc = ItemDocument(
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item_id=item.id,
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@@ -245,26 +273,28 @@ async def upload_item_document(
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db.commit()
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db.refresh(doc)
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# Garantieende und Preis nur aus PDFs schätzen (Bilder haben keine Textebene).
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warranty = price = None
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candidates: list[int] = []
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if content_type == "application/pdf":
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text = extract_pdf_text(data)
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warranty = guess_warranty_until(text, acquired_on=item.acquired_on)
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price = guess_price_cents(text)
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candidates = guess_price_candidates(text)
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vorschlag = _doc_suggestions(db, data, content_type, item.acquired_on)
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return ItemDocumentUploadOut(
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id=doc.id,
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filename=doc.filename,
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content_type=doc.content_type,
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uploaded_at=doc.uploaded_at,
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suggested_warranty_until=warranty,
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suggested_price_cents=price,
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suggested_price_candidates=candidates,
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**vorschlag.model_dump(),
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)
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@router.post("/items/analyze-document", response_model=DocumentSuggestions)
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async def analyze_item_document(
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file: UploadFile = File(...),
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db: Session = Depends(get_db),
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_: User = Depends(require_admin),
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) -> DocumentSuggestions:
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"""Beleg nur analysieren (nichts speichern) – zum Vorbefüllen beim Anlegen."""
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data = await file.read()
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content_type = _read_document(file, data)
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return _doc_suggestions(db, data, content_type)
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@router.get("/items/{item_id}/documents/{doc_id}")
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def get_item_document(
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item_id: int,
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@@ -557,12 +557,20 @@ class ItemDocumentOut(BaseModel):
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uploaded_at: datetime
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class ItemDocumentUploadOut(ItemDocumentOut):
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"""Antwort nach dem Upload – mit aus dem PDF geschätztem Garantieende und Preis."""
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class DocumentSuggestions(BaseModel):
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"""Aus einem Beleg-PDF geschätzte Werte (ohne den Beleg zu speichern)."""
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suggested_warranty_until: date | None = None
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suggested_price_cents: int | None = None
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# Alle plausiblen Preise (bester zuerst) – zur Auswahl, falls es mehrere gibt.
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suggested_price_candidates: list[int] = []
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suggested_acquired_on: date | None = None
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# Erkannter Shop: id = bereits hinterlegt; sonst name = Vorschlag zum Anlegen.
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suggested_shop_id: int | None = None
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suggested_shop_name: str | None = None
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class ItemDocumentUploadOut(ItemDocumentOut, DocumentSuggestions):
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"""Antwort nach dem Upload – Beleg-Metadaten plus die geschätzten Werte."""
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class ItemOut(BaseModel):
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@@ -126,6 +126,57 @@ def guess_warranty_until(text: str, acquired_on: date | None = None) -> date | N
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return _find_date_near_keyword(text)
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def guess_acquired_on(text: str) -> date | None:
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"""Kaufdatum aus dem Beleg (Datum nahe Rechnungs-/Kaufdatum-Stichwort)."""
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return _find_purchase_date(text) if text else None
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# --------------------------------------------------------------------------
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# Shop / Bezugsquelle aus dem Beleg
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# --------------------------------------------------------------------------
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_LEGAL_RE = re.compile(
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r"\b(GmbH|AG|SA|Sàrl|S\.?à r\.?l\.?|Ltd|Inc|SE|KG|OHG|e\.?K\.?|AS|BV|S\.p\.A\.)\b"
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)
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def _vendor_candidate(text: str) -> str | None:
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"""Bester Rate-Name des Händlers – meist im Kopf des Belegs. Best effort."""
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lines = [z.strip() for z in text.splitlines() if z.strip()]
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for z in lines[:20]:
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if _LEGAL_RE.search(z):
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return z[:60]
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for z in lines[:8]:
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buchstaben = sum(c.isalpha() for c in z)
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if 3 <= len(z) <= 40 and buchstaben >= 3 and not re.search(
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r"rechnung|invoice|quittung|beleg|kassenbon|datum|receipt|order|bestell",
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z, re.IGNORECASE,
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):
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return z[:60]
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return None
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def guess_shop(text: str, shops: list[tuple[int, str]]) -> tuple[int | None, str | None]:
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"""(shop_id, name).
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Kommt der Name eines bereits hinterlegten Shops im Beleg vor, wird dieser
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vorgeschlagen (längster Treffer gewinnt). Sonst ein Kandidatenname zum
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Anlegen – oder (None, None), wenn nichts Brauchbares gefunden wird.
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"""
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if not text:
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return (None, None)
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low = text.lower()
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treffer: tuple[int, str] | None = None
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best_len = 0
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for sid, name in shops:
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n = name.strip().lower()
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if len(n) >= 3 and n in low and len(n) > best_len:
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treffer = (sid, name)
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best_len = len(n)
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if treffer is not None:
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return treffer
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return (None, _vendor_candidate(text))
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# --------------------------------------------------------------------------
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# Kaufpreis aus dem Beleg schätzen
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# --------------------------------------------------------------------------
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