mirror of
https://github.com/denizsafak/abogen.git
synced 2026-09-20 19:50:59 +02:00
refactor(domain): deduplicate metadata helpers across 3 layers
Extracted 8 metadata functions from service.py, exporters.py, and audiobookshelf.py into domain/metadata_helpers.py: - normalize_metadata_casefold, split_people_field, split_simple_list - first_nonempty, extract_year, normalize_series_sequence - build_audiobookshelf_metadata, load_audiobookshelf_chapters service.py, exporters.py, and audiobookshelf.py now import from domain instead of maintaining separate copies. Thin wrappers adapt to layer interfaces (Job objects, etc.). Net -231 lines. 1006 tests pass.
This commit is contained in:
Artem Akymenko
@@ -9,6 +9,17 @@ from typing import Any, Dict, List, Optional, Mapping, Sequence
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import static_ffmpeg
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from abogen.domain.metadata_helpers import (
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normalize_metadata_casefold,
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split_people_field,
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split_simple_list,
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first_nonempty,
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extract_year,
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normalize_series_sequence,
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build_audiobookshelf_metadata as _build_abs_metadata,
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load_audiobookshelf_chapters as _load_abs_chapters,
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_SERIES_SEQUENCE_TAG_KEYS,
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)
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from abogen.epub3.exporter import build_epub3_package
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from abogen.integrations.audiobookshelf import (
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AudiobookshelfClient,
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@@ -305,125 +316,20 @@ class ExportService:
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def build_audiobookshelf_metadata(self, job: Any) -> Dict[str, Any]:
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"""Build Audiobookshelf metadata from job."""
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tags = self._normalize_metadata_casefold(getattr(job, "metadata_tags", {}))
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filename = Path(getattr(job, "original_filename", "") or "").stem or "Audiobook"
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title = self._first_nonempty(
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tags.get("title"),
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tags.get("book_title"),
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tags.get("name"),
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tags.get("album"),
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filename,
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return _build_abs_metadata(
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getattr(job, "metadata_tags", {}),
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language=getattr(job, "language", "") or "",
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filename=filename,
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)
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authors = self._split_people_field(
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tags.get("authors")
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or tags.get("author")
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or tags.get("album_artist")
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or tags.get("artist")
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)
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narrators = self._split_people_field(tags.get("narrators") or tags.get("narrator"))
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description = self._first_nonempty(
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tags.get("description"), tags.get("summary"), tags.get("comment")
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)
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genres = self._split_simple_list(tags.get("genre"))
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keywords = self._split_simple_list(tags.get("tags") or tags.get("keywords"))
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language = self._first_nonempty(tags.get("language"), tags.get("lang")) or getattr(job, "language", "") or ""
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series_name = self._first_nonempty(
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tags.get("series"),
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tags.get("series_name"),
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tags.get("seriesname"),
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tags.get("series_title"),
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tags.get("seriestitle"),
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)
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series_sequence = None
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for key in ("series_index", "series_position", "series_sequence", "series_number", "seriesnumber", "book_number", "booknumber"):
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raw = tags.get(key)
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normalized = self._normalize_series_sequence(raw)
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if normalized:
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series_sequence = normalized
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break
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if not series_name:
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series_sequence = None
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data = {
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"title": title,
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"subtitle": tags.get("subtitle"),
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"authors": authors,
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"narrators": narrators,
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"description": description,
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"publisher": tags.get("publisher"),
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"genres": genres,
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"tags": keywords,
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"language": language,
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"publishedYear": self._extract_year(
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tags.get("published") or tags.get("publication_year") or tags.get("date") or tags.get("year")
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),
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"seriesName": series_name,
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"seriesSequence": series_sequence,
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"isbn": self._first_nonempty(tags.get("isbn"), tags.get("asin")),
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}
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published_date = self._first_nonempty(
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tags.get("published"), tags.get("publication_date"), tags.get("date")
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)
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if published_date:
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data["publishedDate"] = published_date
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rating_text = self._first_nonempty(tags.get("rating"), tags.get("my_rating"))
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if rating_text:
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try:
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data["rating"] = float(str(rating_text).strip())
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except ValueError:
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pass
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rating_max_text = self._first_nonempty(tags.get("rating_max"), tags.get("rating_scale"))
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if rating_max_text:
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try:
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data["ratingMax"] = float(str(rating_max_text).strip())
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except ValueError:
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pass
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# Remove empty values
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cleaned = {}
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for key, value in data.items():
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if value is None:
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continue
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if isinstance(value, str) and not value.strip():
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continue
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if isinstance(value, (list, tuple)) and not value:
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continue
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cleaned[key] = value
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return cleaned
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def load_audiobookshelf_chapters(self, job: Any) -> Optional[List[Dict[str, Any]]]:
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"""Load chapters from job artifacts for Audiobookshelf."""
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metadata_ref = job.result.artifacts.get("metadata") if getattr(job, "result", None) else None
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if not metadata_ref:
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return None
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metadata_path = metadata_ref if isinstance(metadata_ref, Path) else Path(str(metadata_ref))
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if not metadata_path.exists():
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return None
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try:
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payload = json.loads(metadata_path.read_text(encoding="utf-8"))
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except (OSError, json.JSONDecodeError):
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return None
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chapters = payload.get("chapters")
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if not isinstance(chapters, list):
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return None
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cleaned = []
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for entry in chapters:
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if not isinstance(entry, Mapping):
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continue
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title = self._first_nonempty(entry.get("title"), entry.get("original_title"))
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start = entry.get("start")
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end = entry.get("end")
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if title is None or not isinstance(start, (int, float)):
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continue
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chapter_payload = {"title": title, "start": float(start)}
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if isinstance(end, (int, float)):
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chapter_payload["end"] = float(end)
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cleaned.append(chapter_payload)
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return cleaned or None
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return _load_abs_chapters(metadata_path)
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def upload_audiobookshelf(
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self,
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@@ -520,137 +426,6 @@ class ExportService:
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# Helpers
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# ----------------------------------------------------------------------
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@staticmethod
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def _normalize_metadata_casefold(values: Optional[Mapping[str, Any]]) -> Dict[str, Any]:
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normalized = {}
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if not values:
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return normalized
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for key, value in values.items():
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if value is None:
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continue
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key_text = str(key).strip().lower()
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if not key_text:
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continue
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if isinstance(value, (list, tuple, set)):
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normalized[key_text] = value
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else:
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text = str(value).strip()
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if text:
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normalized[key_text] = text
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return normalized
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@staticmethod
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def _split_people_field(raw: Any) -> List[str]:
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if raw is None:
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return []
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if isinstance(raw, (list, tuple, set)):
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results = []
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for item in raw:
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results.extend(ExportService._split_people_field(item))
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return results
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text = str(raw or "").strip()
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if not text:
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return []
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import re
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tokens = [token.strip() for token in re.split(r"[;,/&]|\band\b", text, flags=re.IGNORECASE) if token.strip()]
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seen = set()
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ordered = []
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for token in tokens:
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key = token.casefold()
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if key in seen:
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continue
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seen.add(key)
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ordered.append(token)
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return ordered
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@staticmethod
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def _split_simple_list(raw: Any) -> List[str]:
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if raw is None:
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return []
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if isinstance(raw, (list, tuple, set)):
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results = []
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for item in raw:
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results.extend(ExportService._split_simple_list(item))
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return results
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text = str(raw or "").strip()
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if not text:
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return []
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import re
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tokens = [token.strip() for token in re.split(r"[;,\n]", text) if token.strip()]
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seen = set()
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ordered = []
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for token in tokens:
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key = token.casefold()
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if key in seen:
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continue
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seen.add(key)
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ordered.append(token)
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return ordered
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@staticmethod
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def _first_nonempty(*values: Any) -> Optional[str]:
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for value in values:
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if value is None:
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continue
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if isinstance(value, (list, tuple, set)):
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items = list(value)
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if not items:
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continue
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value = items[0]
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text = str(value).strip()
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if text:
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return text
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return None
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@staticmethod
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def _extract_year(raw: Optional[str]) -> Optional[int]:
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if not raw:
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return None
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text = str(raw).strip()
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if not text:
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return None
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import re
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match = re.search(r"(19|20)\d{2}", text)
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if match:
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try:
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return int(match.group(0))
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except ValueError:
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return None
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try:
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parsed = int(text)
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except ValueError:
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return None
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if 0 < parsed < 3000:
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return parsed
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return None
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@staticmethod
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def _normalize_series_sequence(raw: Any) -> Optional[str]:
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if raw is None:
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return None
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if isinstance(raw, (int, float)):
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if isinstance(raw, float) and (raw != raw or raw == float("inf") or raw == float("-inf")):
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return None
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text = str(raw)
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else:
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text = str(raw).strip()
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if not text:
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return None
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candidate = text.replace(",", ".")
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import re
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match = re.search(r"\d+(?:\.\d+)?", candidate)
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if not match:
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return None
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normalized = match.group(0)
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if "." in normalized:
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normalized = normalized.rstrip("0").rstrip(".")
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return normalized or "0"
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try:
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return str(int(normalized))
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except ValueError:
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cleaned = normalized.lstrip("0")
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return cleaned or "0"
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@staticmethod
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def _coerce_bool(value: Any, default: bool = True) -> bool:
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if isinstance(value, bool):
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