#!/usr/bin/env python3 from __future__ import annotations import argparse import csv import os import re import unicodedata import xml.etree.ElementTree as ET from dataclasses import dataclass, field from pathlib import Path # Manual verification workflow: # - `verification_source` in `data/verified_author_overrides.tsv` should point to # the page used to confirm corrected metadata. # - `lubimyczytac.pl` is an accepted auxiliary source for author, title, and # series/cycle verification when audiobook storefront metadata is missing or # ambiguous. AUDIO_EXTS = { ".aac", ".flac", ".m4a", ".m4b", ".mp3", ".ogg", ".opus", ".wav", } DISC_RE = re.compile(r"^(disc|cd|disk)\s*0*\d+$", re.IGNORECASE) TRACK_PREFIX_RE = re.compile(r"^\s*(cd|disc|disk)?\s*\d+\s*([._ -]+)\s*", re.IGNORECASE) YEAR_RE = re.compile(r"^\(?((?:19|20)\d{2})\)?\s*-\s*(.+)$") LEADING_SEQ_RE = re.compile(r"^\[?(\d+(?:[.,]\d+)?)\]?\s*(?:[.-]\s+|\s+-\s+)(.+)$") SEQ_LABEL_RE = re.compile( r"(?i)(?:^| - )(?:(?:vol(?:ume)?|book|tom|t|cz(?:eść|esc)?)\.?\s*)(\d+(?:[.,]\d+)?)" ) BRACKET_SEQ_RE = re.compile(r"^\[(\d+(?:[.,]\d+)?)\]\s*(.+)$") SERIES_SEQ_PATTERNS = [ re.compile(r"^(.+?)\s+(?:tom|vol(?:ume)?|book)\.?\s*(\d+(?:[.,]\d+)?)\s*-\s*(.+)$", re.IGNORECASE), re.compile(r"^(.+?)\s*-\s*(\d+(?:[.,]\d+)?)\s+(.+)$"), ] WORD_TOKEN_RE = re.compile(r"[A-Za-zÀ-ÖØ-öø-ÿĀ-žĄĆĘŁŃÓŚŹŻąćęłńóśźż0-9&']+") NARRATOR_PATTERNS = [ re.compile(r"(?i)\[(?:czyta|czyt\.?|lektor|read by)\s+([^\]]+)\]"), re.compile(r"(?i)\((?:czyta|czyt\.?|lektor|read by)\s+([^)]+)\)"), re.compile(r"(?i)(?:^| - |\s)(?:czyta|czyt\.?|lektor|read by)\s+(.+)$"), ] NOISE_PATTERNS = [ re.compile(r"(?i)\[[^]]*kbps[^]]*\]"), re.compile(r"(?i)\[[^]]*czas[^]]*\]"), re.compile(r"(?i)\([^)]*(?:trawel|hex-lub|lapiejko|elgrande|kwgr)[^)]*\)"), ] GENERIC_KEYWORDS = { "angielski", "audiobook", "audiobooki", "biblioteka", "brakuje", "brakujace", "czyta", "czyt", "cykl", "do przeczytania", "eng", "kolekcja", "lekt", "lektor", "magazyn", "nowe", "sluchowisko", } NAME_PARTICLES = {"al", "bin", "da", "de", "del", "di", "la", "le", "van", "von", "and"} TITLE_CONNECTORS = {"a", "and", "bez", "dla", "do", "for", "i", "na", "nad", "o", "of", "po", "pod", "przez", "the", "to", "w", "z", "ze"} @dataclass class BookRoot: path: Path audio_files: list[Path] = field(default_factory=list) def normalize_unicode(text: str) -> str: return unicodedata.normalize("NFKC", text) def collapse_spaces(text: str) -> str: return re.sub(r"\s+", " ", text).strip() def clean_component(raw: str) -> str: text = normalize_unicode(raw) text = text.replace("_", " ") text = text.replace(".", " ") text = text.replace("–", " - ") text = text.replace("—", " - ") text = collapse_spaces(text) text = re.sub(r"\s*-\s*", " - ", text) return collapse_spaces(text) def ascii_fold(text: str) -> str: special_map = str.maketrans({ "Ł": "L", "ł": "l", }) normalized = unicodedata.normalize("NFKD", text.translate(special_map)) return "".join(ch for ch in normalized if not unicodedata.combining(ch)) def signature(text: str) -> str: folded = ascii_fold(clean_component(text)).lower() folded = re.sub(r"[^a-z0-9]+", " ", folded) return collapse_spaces(folded) def same_signature_tokens(left: str, right: str) -> bool: left_tokens = signature(left).split() right_tokens = signature(right).split() if not left_tokens or not right_tokens: return False return sorted(left_tokens) == sorted(right_tokens) def contains_signature_tokens(container: str, text: str) -> bool: container_tokens = set(signature(container).split()) needle_tokens = set(signature(text).split()) if not container_tokens or not needle_tokens: return False return needle_tokens.issubset(container_tokens) def looks_generic(text: str) -> bool: sig = signature(text) if not sig: return True if any(keyword in sig for keyword in GENERIC_KEYWORDS): return True if re.search(r"\d", sig) and len(sig.split()) <= 4: return True return False def author_confidence_for(name: str) -> str: sig = signature(name) if not sig: return "none" tokens = sig.split() initials = sum(1 for token in tokens if len(token) == 1) if 2 <= len(tokens) <= 4: return "high" if len(tokens) == 1 or initials: return "medium" return "low" def is_author_like(text: str) -> bool: cleaned = clean_component(text) if not cleaned or looks_generic(cleaned): return False if " - " in cleaned: return False allowed = re.sub(r"[A-Za-zÀ-ÖØ-öø-ÿĀ-žĄĆĘŁŃÓŚŹŻąćęłńóśźż0-9,&' /-]", "", cleaned) if allowed: return False if re.search(r"\d", cleaned): return False sig = signature(cleaned) tokens = sig.split() if not 1 <= len(tokens) <= 5: return False if all(len(token) == 1 for token in tokens): return False initials = sum(1 for token in tokens if len(token) == 1) if len(tokens) == 5 and initials == 0: return False raw_tokens = re.findall(r"[A-Za-zÀ-ÖØ-öø-ÿĀ-žĄĆĘŁŃÓŚŹŻąćęłńóśźż]+", cleaned) for token in raw_tokens: if len(token) == 1: continue if token[0].islower() and token.lower() not in NAME_PARTICLES: return False return True def strip_noise(text: str) -> tuple[str, list[str]]: notes: list[str] = [] result = text for pattern in NOISE_PATTERNS: if pattern.search(result): notes.append("removed technical/uploader marker") result = pattern.sub("", result) return collapse_spaces(result), notes def extract_narrator(text: str) -> tuple[str, str]: result = text narrator = "" for pattern in NARRATOR_PATTERNS: match = pattern.search(result) if not match: continue narrator = collapse_spaces(match.group(1)) result = collapse_spaces(pattern.sub("", result)) break return result, narrator def extract_year(text: str) -> tuple[str, str]: match = YEAR_RE.match(text) if not match: return text, "" return collapse_spaces(match.group(2)), match.group(1) def extract_sequence(text: str) -> tuple[str, str]: working = text.lstrip("- ").strip() match = BRACKET_SEQ_RE.match(working) if match: return collapse_spaces(match.group(2)), match.group(1) match = LEADING_SEQ_RE.match(working) if match: seq = match.group(1) if len(seq) < 4: return collapse_spaces(match.group(2)), seq match = SEQ_LABEL_RE.search(working) if not match: return working, "" seq = match.group(1) start, end = match.span() stripped = collapse_spaces((working[:start] + working[end:]).strip(" -")) return stripped, seq def strip_author_prefix(text: str, author: str) -> str: cleaned_tokens = component_words(text) author_words = component_words(author) if not cleaned_tokens or not author_words: return text if len(cleaned_tokens) <= len(author_words): return text prefix = " ".join(cleaned_tokens[: len(author_words)]) if not same_signature_tokens(prefix, author): return text stripped = collapse_spaces(" ".join(cleaned_tokens[len(author_words) :])) return stripped.lstrip("- ").strip() def normalize_author_segments(segments: list[str]) -> str: cleaned = [collapse_spaces(segment) for segment in segments if collapse_spaces(segment)] if not cleaned: return "" if len(cleaned) == 1: return cleaned[0] return "-".join(cleaned) def is_author_like_flexible(text: str) -> bool: if is_author_like(text): return True if "-" in text: return is_author_like(text.replace("-", " ")) return False def author_title_candidate_score(author: str, title: str) -> int: author_words = len(component_words(author)) title_tokens = signature(title).split() score = author_words * 10 + min(len(title_tokens), 6) if author_words == 1: score -= 2 if re.search(r"\d", title): score += 2 if " - " in title: score += 1 return score def split_author_title(text: str) -> tuple[str, str]: cleaned = clean_component(text) if " - " in cleaned: parts = [collapse_spaces(part) for part in cleaned.split(" - ") if collapse_spaces(part)] best_author = "" best_title = "" best_score = -1 for size in range(1, len(parts)): left_author = normalize_author_segments(parts[:size]) right_title = collapse_spaces(" - ".join(parts[size:])) if left_author and right_title and is_author_like_flexible(left_author): score = author_title_candidate_score(left_author, right_title) if score > best_score: best_author = left_author best_title = right_title best_score = score right_author = normalize_author_segments(parts[size:]) left_title = collapse_spaces(" - ".join(parts[:size])) if right_author and left_title and is_author_like_flexible(right_author): score = author_title_candidate_score(right_author, left_title) - 1 if score > best_score: best_author = right_author best_title = left_title best_score = score if best_score >= 0: return best_author, best_title tokens = cleaned.split() for size in range(2, min(5, len(tokens) - 1) + 1): author = " ".join(tokens[:size]) title = " ".join(tokens[size:]) if not is_author_like(author): continue title_tokens = signature(title).split() if TITLE_CONNECTORS.intersection(title_tokens): return author, title return "", "" def component_words(text: str) -> list[str]: return WORD_TOKEN_RE.findall(clean_component(text)) def find_token_sequence(text: str, sequence_tokens: list[str]) -> str: if not sequence_tokens: return "" words = component_words(text) word_tokens = [signature(word) for word in words] for start in range(0, len(word_tokens) - len(sequence_tokens) + 1): if word_tokens[start : start + len(sequence_tokens)] == sequence_tokens: return collapse_spaces(" ".join(words[start : start + len(sequence_tokens)])) return "" def extract_author_from_title_context(text: str, known_title: str) -> str: title_tokens = signature(known_title).split() if not title_tokens: return "" words = component_words(text) word_tokens = [signature(word) for word in words] if len(word_tokens) <= len(title_tokens): return "" if word_tokens[-len(title_tokens) :] == title_tokens: candidate = collapse_spaces(" ".join(words[: len(word_tokens) - len(title_tokens)])).strip("- ") if is_author_like(candidate): return clean_component(candidate) if word_tokens[: len(title_tokens)] == title_tokens: candidate = collapse_spaces(" ".join(words[len(title_tokens) :])).strip("- ") if is_author_like(candidate): return clean_component(candidate) return "" def normalize_author_order_from_context(author: str, contexts: list[str]) -> str: words = component_words(author) if len(words) < 2: return "" token_orders = [] reversed_tokens = [signature(word) for word in reversed(words)] if reversed_tokens: token_orders.append(reversed_tokens) rotated_tokens = [signature(word) for word in [words[-1], *words[:-1]]] if rotated_tokens and rotated_tokens != reversed_tokens: token_orders.append(rotated_tokens) for text in contexts: for token_order in token_orders: match = find_token_sequence(text, token_order) if match: return clean_component(match) return "" def strip_title_suffix(text: str, title: str) -> str: cleaned = clean_component(text) cleaned_tokens = cleaned.split() title_words = component_words(title) if not cleaned_tokens or not title_words: return text if len(cleaned_tokens) <= len(title_words): return text suffix = " ".join(cleaned_tokens[-len(title_words) :]) if not same_signature_tokens(suffix, title): return text stripped = collapse_spaces(" ".join(cleaned_tokens[: -len(title_words)])) return stripped.strip("- ").strip() TRAILING_SERIES_SEQ_PATTERNS = [ re.compile(r"^(.+?)\s+(?:tom|vol(?:ume)?|book|t|cz(?:eść|esc)?)\.?\s*(\d+(?:[.,]\d+)?)$", re.IGNORECASE), re.compile(r"^(.+?)\s+(\d+(?:[.,]\d+)?)$"), ] INLINE_SERIES_SEQ_TITLE_PATTERNS = [ re.compile(r"^(.+?)\s+(?:tom|vol(?:ume)?|book|t|cz(?:eść|esc)?)\.?\s*(\d+(?:[.,]\d+)?)\s+(.+)$", re.IGNORECASE), re.compile(r"^(.+?)\s+(0\d+(?:[.,]\d+)?)\s+(.+)$"), ] def extract_trailing_series_sequence(text: str) -> tuple[str, str]: cleaned = clean_component(text).strip("- ") if not cleaned: return "", "" for pattern in TRAILING_SERIES_SEQ_PATTERNS: match = pattern.match(cleaned) if not match: continue series, seq = (collapse_spaces(part) for part in match.groups()) if len(seq) < 4: return series, seq return cleaned, "" def extract_inline_series_sequence_title(text: str) -> tuple[str, str, str]: cleaned = clean_component(text).strip("- ") if not cleaned: return "", "", "" for pattern in INLINE_SERIES_SEQ_TITLE_PATTERNS: match = pattern.match(cleaned) if not match: continue series, sequence, title = (collapse_spaces(part) for part in match.groups()) if len(sequence) < 4: return series, sequence, title return "", "", "" def normalize_series_candidate(text: str, author: str, title_hints: list[str]) -> tuple[str, str]: working = clean_component(text) if not working: return "", "" if author: stripped = strip_author_prefix(working, author) if stripped != working: working = stripped working, _ = extract_narrator(working) working, _ = strip_noise(working) for title_hint in sorted( {clean_component(hint) for hint in title_hints if hint}, key=lambda hint: len(component_words(hint)), ): stripped = strip_title_suffix(working, title_hint) if stripped != working: working = stripped break working = clean_component(working).strip("- ") if not working: return "", "" series, sequence, parsed_title = extract_inline_series_sequence_title(working) if series: if not looks_generic(series) and not (author and contains_signature_tokens(series, author)): if not title_hints or any(contains_signature_tokens(title_hint, parsed_title) for title_hint in title_hints if title_hint): return series, sequence series, sequence = extract_trailing_series_sequence(working) if not series or looks_generic(series): return "", "" if author and contains_signature_tokens(series, author): return "", "" for title_hint in title_hints: if title_hint and contains_signature_tokens(series, title_hint): return "", "" return series, sequence def infer_series_from_context(parts: list[str], author_index: int, author: str, title_hints: list[str]) -> tuple[str, str]: start = author_index + 1 if author_index >= 0 else 0 candidates = parts[start:] for part in reversed(candidates): series, sequence = normalize_series_candidate(part, author, title_hints) if series: return series, sequence return "", "" def series_needs_normalization(series: str, author: str, title: str) -> bool: if not series: return True if looks_generic(series): return True if author and contains_signature_tokens(series, author): return True if title and contains_signature_tokens(series, title): return True return False def strip_series_prefix(text: str, series: str, sequence: str) -> str: cleaned_tokens = clean_component(text).split() series_tokens = component_words(series) if not cleaned_tokens or not series_tokens or len(cleaned_tokens) <= len(series_tokens): return text prefix = " ".join(cleaned_tokens[: len(series_tokens)]) if signature(prefix) != signature(series): return text index = len(series_tokens) if sequence and index < len(cleaned_tokens): next_token = signature(cleaned_tokens[index]) if next_token == signature(sequence): index += 1 elif index + 1 < len(cleaned_tokens) and signature(cleaned_tokens[index]) in {"tom", "t", "vol", "volume", "book"} and signature(cleaned_tokens[index + 1]) == signature(sequence): index += 2 stripped = collapse_spaces(" ".join(cleaned_tokens[index:])).strip("- ") return stripped or text def sanitize_component(text: str) -> str: value = ascii_fold(collapse_spaces(text.replace("/", "-").replace("\\", "-"))) value = value.strip(". ") return value or "__REVIEW__" def extract_series_sequence_title(text: str) -> tuple[str, str, str]: for pattern in SERIES_SEQ_PATTERNS: match = pattern.match(text) if not match: continue series, sequence, title = (collapse_spaces(part) for part in match.groups()) if len(sequence) >= 4: continue return series, sequence, title return "", "", text def title_from_filenames(audio_files: list[Path]) -> str: stems: list[str] = [] for path in audio_files[:12]: stem = clean_component(path.stem) stem = TRACK_PREFIX_RE.sub("", stem) stem = collapse_spaces(stem) if stem and not stem.isdigit(): stems.append(stem) unique = {signature(stem): stem for stem in stems if stem} if len(unique) == 1: return next(iter(unique.values())) return "" def parse_opf(path: Path) -> dict[str, str]: try: root = ET.parse(path).getroot() except Exception: return {} values: dict[str, str] = {} wanted = { "title", "author", "narrator", "publishYear", "publisher", "isbn", "description", "genres", "language", "series", "volumeNumber", } for element in root.iter(): tag = element.tag.rsplit("}", 1)[-1] if tag in wanted and element.text and tag not in values: values[tag] = collapse_spaces(element.text) return values def collect_book_roots(root: Path) -> list[BookRoot]: books: dict[Path, BookRoot] = {} for dirpath_str, _, filenames in os.walk(root): dirpath = Path(dirpath_str) audio_files = [dirpath / name for name in sorted(filenames) if Path(name).suffix.lower() in AUDIO_EXTS] if not audio_files: continue book_root = dirpath.parent if DISC_RE.match(clean_component(dirpath.name)) else dirpath book = books.setdefault(book_root, BookRoot(path=book_root)) book.audio_files.extend(audio_files) return sorted(books.values(), key=lambda book: str(book.path)) def choose_author(parts: list[str], filename_title: str = "") -> tuple[str, int, str, str]: if filename_title: for index in range(len(parts) - 1, -1, -1): author = extract_author_from_title_context(parts[index], filename_title) if author: return author, index, "title-context", author_confidence_for(author) for index, part in enumerate(parts): if is_author_like(part): return clean_component(part), index, "folder", author_confidence_for(part) best_author = "" best_index = -1 best_score = -1 for index, part in enumerate(parts): author, title = split_author_title(part) if author: score = author_title_candidate_score(author, title) if score > best_score: best_author = author best_index = index best_score = score if best_author: return best_author, best_index, "mixed-folder", "medium" return "", -1, "missing", "none" def choose_series(parts: list[str], author_index: int, leaf: str, author: str) -> tuple[str, list[str]]: notes: list[str] = [] if author_index < 0: candidates = parts[:-1] else: candidates = parts[author_index + 1 : -1] leaf_sig = signature(leaf) author_sig = signature(author) for part in candidates: cleaned = clean_component(part) if not cleaned: continue if looks_generic(cleaned): notes.append(f"ignored grouping folder '{cleaned}'") continue if signature(cleaned) in {leaf_sig, author_sig}: continue _, possible_title = split_author_title(cleaned) if possible_title and signature(possible_title) == leaf_sig: continue return cleaned, notes return "", notes def infer_book(root: Path, library_root: Path, audio_files: list[Path]) -> dict[str, str]: rel_parts = [clean_component(part) for part in root.relative_to(library_root).parts] leaf = rel_parts[-1] notes: list[str] = [] filename_title = title_from_filenames(audio_files) opf_path = next((path for path in root.iterdir() if path.suffix.lower() == ".opf"), None) opf = parse_opf(opf_path) if opf_path else {} if opf_path: notes.append(f"loaded sidecar metadata from {opf_path.name}") author, author_index, author_source, author_confidence = choose_author(rel_parts, filename_title) if author_source == "missing": notes.append("author not confidently identifiable from path") elif author_confidence == "medium": notes.append("author inferred from a weak path signal") series, series_notes = choose_series(rel_parts, author_index, leaf, author) notes.extend(series_notes) narrator = opf.get("narrator", "") year = opf.get("publishYear", "") sequence = opf.get("volumeNumber", "") title = opf.get("title", "") if opf.get("author"): author = opf["author"] author_source = "opf" author_confidence = "high" notes.append("author taken from OPF sidecar") if opf.get("series"): series = opf["series"] if title: title_source = "opf" else: title_source = "path" title = leaf if author: stripped = strip_author_prefix(title, author) if stripped != title: title = stripped leaf_author, leaf_title = split_author_title(leaf) if leaf_title and same_signature_tokens(leaf_author, author): title = leaf_title title, narrator_from_path = extract_narrator(title) if narrator_from_path and not narrator: narrator = narrator_from_path notes.append("narrator inferred from folder name") title, noise_notes = strip_noise(title) notes.extend(noise_notes) title, year_from_path = extract_year(title) if year_from_path and not year: year = year_from_path title, sequence_from_path = extract_sequence(title) if sequence_from_path and not sequence: sequence = sequence_from_path notes.append("sequence inferred from folder name") if not series: series_from_title, seq_from_series_title, stripped_title = extract_series_sequence_title(title) if series_from_title: series = series_from_title title = stripped_title if seq_from_series_title and not sequence: sequence = seq_from_series_title notes.append("series inferred from folder name") if filename_title and author: stripped_filename_title = strip_author_prefix(filename_title, author) if stripped_filename_title: filename_title = stripped_filename_title if not title or signature(title) == signature(author): if filename_title: title = filename_title title_source = "filename" notes.append("title inferred from repeated audio filename stem") title_hints = [title] if filename_title: title_hints.append(filename_title) normalized_series, normalized_sequence = infer_series_from_context(rel_parts, author_index, author, title_hints) if normalized_series and series_needs_normalization(series, author, title): series = normalized_series notes.append("series normalized from folder context") if normalized_sequence and not sequence: sequence = normalized_sequence notes.append("sequence inferred from folder context") if series and title: stripped_title = strip_series_prefix(title, series, sequence) if stripped_title != title: title = stripped_title if author and not opf.get("author"): context_texts = [leaf] if author_index != len(rel_parts) - 1 else [] context_texts.extend(clean_component(path.stem) for path in audio_files[:12]) if author_source == "mixed-folder" and ("-" in author or len(component_words(author)) > 2): context_texts.extend(part for index, part in enumerate(rel_parts) if index != author_index) normalized_author = normalize_author_order_from_context(author, context_texts) if normalized_author and signature(normalized_author) != signature(author): author = normalized_author notes.append("author order normalized from current folder/file name") title = collapse_spaces(title) if not title: title = sanitize_component(root.name) notes.append("title fallback came from raw folder name") if author_index > 0: notes.append(f"author came from nested folder '{rel_parts[author_index]}'") if author_index < 0 and len(rel_parts) > 1 and looks_generic(rel_parts[0]): notes.append(f"top-level folder '{rel_parts[0]}' looks like a generic bucket") status = "ready" if title and author_confidence in {"high", "medium"} else "review" author_folder = sanitize_component(author) if author else "__AUTHOR_REVIEW__" title_bits = [] if sequence: title_bits.append(f"Vol. {sequence}") if year: title_bits.append(year) title_bits.append(title) title_folder = sanitize_component(" - ".join(bit for bit in title_bits if bit)) if narrator: title_folder = f"{title_folder} {{{sanitize_component(narrator)}}}" proposed_parts = [author_folder] if series: proposed_parts.append(sanitize_component(series)) proposed_parts.append(title_folder) proposed_path = "/".join(proposed_parts) return { "verification_status": "unverified", "verification_source": "", "status": status, "current_path": str(root), "audio_file_count": str(len(audio_files)), "sample_audio_file": audio_files[0].name if audio_files else "", "author": author, "author_confidence": author_confidence, "author_source": author_source, "series": series, "sequence": sequence, "publish_year": year, "title": title, "title_source": title_source, "narrator": narrator, "proposed_abs_path": proposed_path, "notes": "; ".join(dict.fromkeys(note for note in notes if note)), } def load_overrides(path: Path | None) -> dict[str, dict[str, str]]: if not path or not path.exists(): return {} with path.open(encoding="utf-8", newline="") as handle: rows = list(csv.DictReader(handle, delimiter="\t")) return {row["current_path"]: row for row in rows if row.get("current_path")} def apply_override(row: dict[str, str], override: dict[str, str]) -> dict[str, str]: updated = dict(row) for key, value in override.items(): if key == "current_path": continue if key in {"verified_author", "verified_series", "verified_sequence", "verified_publish_year", "verified_title", "verified_narrator"}: continue if value: updated[key] = value mapping = { "verified_author": "author", "verified_series": "series", "verified_sequence": "sequence", "verified_publish_year": "publish_year", "verified_title": "title", "verified_narrator": "narrator", } for source_key, target_key in mapping.items(): value = collapse_spaces(override.get(source_key, "")) if value: updated[target_key] = value updated["author_source"] = "verified-override" if updated.get("author") else updated["author_source"] updated["author_confidence"] = "high" if updated.get("author") else updated["author_confidence"] updated["verification_status"] = override.get("verification_status", "verified") or "verified" updated["verification_source"] = override.get("verification_source", "") if override.get("verification_note"): notes = [note for note in [updated.get("notes", ""), override["verification_note"]] if note] updated["notes"] = "; ".join(dict.fromkeys(notes)) author_folder = sanitize_component(updated["author"]) if updated.get("author") else "__AUTHOR_REVIEW__" title_bits = [] if updated.get("sequence"): title_bits.append(f"Vol. {updated['sequence']}") if updated.get("publish_year"): title_bits.append(updated["publish_year"]) title_bits.append(updated["title"]) title_folder = sanitize_component(" - ".join(bit for bit in title_bits if bit)) if updated.get("narrator"): title_folder = f"{title_folder} {{{sanitize_component(updated['narrator'])}}}" proposed_parts = [author_folder] if updated.get("series"): proposed_parts.append(sanitize_component(updated["series"])) proposed_parts.append(title_folder) updated["proposed_abs_path"] = "/".join(proposed_parts) updated["status"] = "ready" if updated.get("author") else "review" return updated def main() -> int: parser = argparse.ArgumentParser( description="Generate a non-destructive Audiobookshelf path proposal report." ) parser.add_argument( "--root", default="/mnt/nextcloudExtDS/Ksiazki/Audiobooki", help="Path to the current audiobook library", ) parser.add_argument( "--output", default="reports/audiobookshelf_mock_report.tsv", help="TSV output path", ) parser.add_argument( "--overrides", default="data/verified_author_overrides.tsv", help="Optional TSV with verified metadata overrides", ) args = parser.parse_args() library_root = Path(args.root).resolve() output_path = Path(args.output).resolve() output_path.parent.mkdir(parents=True, exist_ok=True) overrides = load_overrides(Path(args.overrides).resolve()) books = collect_book_roots(library_root) rows = [] for book in books: row = infer_book(book.path, library_root, sorted(book.audio_files)) override = overrides.get(row["current_path"]) if override: row = apply_override(row, override) rows.append(row) fieldnames = [ "verification_status", "verification_source", "verification_note", "status", "current_path", "audio_file_count", "sample_audio_file", "author", "author_confidence", "author_source", "series", "sequence", "publish_year", "title", "title_source", "narrator", "proposed_abs_path", "notes", ] with output_path.open("w", encoding="utf-8", newline="") as handle: writer = csv.DictWriter(handle, fieldnames=fieldnames, delimiter="\t") writer.writeheader() writer.writerows(rows) ready = sum(1 for row in rows if row["status"] == "ready") review = len(rows) - ready print(f"library_root\t{library_root}") print(f"report\t{output_path}") print(f"books\t{len(rows)}") print(f"ready\t{ready}") print(f"review\t{review}") return 0 if __name__ == "__main__": raise SystemExit(main())