645 lines
21 KiB
Python
645 lines
21 KiB
Python
#!/usr/bin/env python3
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"""Extract Kṛṣṇa Consciousness: The Matchless Gift (1974) from other.html.
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The book has 1 intro segment (front-pages) and 7 chapters.
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Output:
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lib/jsons/books/mg/intro.json — Book metadata
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lib/jsons/books/mg/intro/*.json — Intro segments
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lib/jsons/books/mg/chapters/*.json — Chapters
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Usage:
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python3 scripts/mg_extract.py
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"""
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import json
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import os
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import re
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import sys
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from bs4 import BeautifulSoup, Tag
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# ---------------------------------------------------------------------------
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# ScaGoudy → IAST Unicode mapping
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# ---------------------------------------------------------------------------
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SCAGOUDY_TO_IAST = {
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"ä": "ā",
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"Ä": "Ā",
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"å": "ṛ",
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"Å": "Ṛ",
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"ñ": "ṣ",
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"Ñ": "Ṣ",
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"ö": "ṭ",
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"Ö": "Ṭ",
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"ë": "ṇ",
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"Ë": "Ṇ",
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"ò": "ḍ",
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"Ò": "Ḍ",
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"ç": "ś",
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"Ç": "Ś",
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"ì": "ṅ",
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"ï": "ñ",
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"ü": "ū",
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"Ü": "Ū",
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"é": "ī",
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"É": "Ī",
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"à": "ṁ",
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"ù": "ḥ",
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"è": "ṝ",
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"û": "ṁl",
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"ÿ": "ḷ",
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}
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def decode_scagoudy(text: str) -> str:
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"""Replace ScaGoudy-encoded characters with proper IAST Unicode."""
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for src, dst in SCAGOUDY_TO_IAST.items():
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text = text.replace(src, dst)
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return text
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def decode_html_content(tag: Tag) -> str:
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"""Extract inner HTML from a tag, decode ScaGoudy, preserve formatting tags."""
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inner = "".join(str(child) for child in tag.children)
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inner = decode_scagoudy(inner)
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# Remove span tags with CSS classes (RTF conversion artifacts)
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inner = re.sub(
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r'<span[^>]*class="[^"]*"[^>]*>(.*?)</span>', r"\1", inner, flags=re.DOTALL
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)
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# Clean up Apple-tab-span leftovers (tab characters)
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inner = inner.replace("\t", " ")
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# Normalize whitespace (but preserve <br> tags)
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inner = re.sub(r"\s+", " ", inner).strip()
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return inner
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def decode_text(tag: Tag) -> str:
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"""Get plain text from a tag with ScaGoudy decoding."""
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return decode_scagoudy(tag.get_text().strip())
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def get_css_class(tag: Tag) -> str:
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"""Get the first CSS class of a tag, or empty string."""
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classes = tag.get("class", [])
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return classes[0] if classes else ""
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def is_empty(tag: Tag) -> bool:
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"""Check if a tag is empty or whitespace-only."""
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text = tag.get_text().strip()
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return not text or text == "\xa0"
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def is_dash_line(text: str) -> bool:
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"""Return True if text is only dashes, spaces, and/or whitespace."""
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cleaned = text.strip()
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if not cleaned:
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return False
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return all(c in "-—–\u2014\u2013 \t" for c in cleaned)
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# ---------------------------------------------------------------------------
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# CSS class categorization for MG
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# ---------------------------------------------------------------------------
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# Book title header class
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BOOK_HEADER_CLASS = "p12"
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# Chapter bookmark class
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CHAPTER_BOOKMARK_CLASS = "p13"
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# Body paragraph classes (primary content)
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BODY_CLASSES = frozenset(["p6", "p78", "p30"])
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# Sanskrit verse pāda classes (italic, centered) — BOTH p53 AND p24
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VERSE_PADA_CLASSES = frozenset(["p53", "p24"])
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# Verse translations / body text
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TRANSLATION_CLASS = "p44"
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# Chapter heading (bold)
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CHAPTER_HEADING_CLASS = "p27"
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# Title page text
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TITLE_PAGE_CLASS = "p3"
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# Book list header/footer
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BOOK_LIST_CLASSES = frozenset(["p76", "p77"])
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# Classes to skip entirely (empty/decorative)
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SKIP_CLASSES = frozenset(["p9", "p70", "p79"])
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# ---------------------------------------------------------------------------
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# Extraction helpers
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# ---------------------------------------------------------------------------
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def find_mg_boundaries(all_paragraphs: list) -> tuple:
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"""Find the start and end paragraph indices of the MG content.
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Start: <p class="p12"> containing "Matchless Gift" and "1974"
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End: next <p class="p12"> after start, or <p class="p27"> containing "APPENDIX"
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"""
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start_idx = None
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end_idx = len(all_paragraphs)
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for i, p in enumerate(all_paragraphs):
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cls = get_css_class(p)
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if cls != BOOK_HEADER_CLASS:
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continue
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text = decode_text(p)
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if "Matchless Gift" in text and "1974" in text:
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start_idx = i
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elif start_idx is not None:
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# Next p12 after MG start = end of MG
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end_idx = i
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break
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if start_idx is None:
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print(
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"ERROR: Could not find 'Kṛṣṇa Consciousness: The Matchless Gift (1974)' header in other.html!"
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)
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sys.exit(1)
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# Also check for APPENDIXES/APPENDIX as end boundary (p27 with bold text)
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for i in range(start_idx + 1, end_idx):
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p = all_paragraphs[i]
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cls = get_css_class(p)
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if cls == CHAPTER_HEADING_CLASS:
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text = p.get_text().strip().upper()
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if text.startswith("APPENDIX") or text.startswith("APPENDIXES"):
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end_idx = i
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break
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return start_idx, end_idx
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def find_chapter_bookmarks(all_paragraphs: list, start_idx: int, end_idx: int) -> list:
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"""Find all MG chapter bookmark tags (p13) in the MG range.
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Returns list of (paragraph_index, chapter_number, chapter_title).
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"""
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bookmarks = []
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for i in range(start_idx, end_idx):
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p = all_paragraphs[i]
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cls = get_css_class(p)
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if cls == CHAPTER_BOOKMARK_CLASS:
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text = decode_text(p)
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# Use re.DOTALL to handle titles that span multiple lines
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m = re.match(r"MG\s+(\d+)\s*:\s*(.+)", text, re.DOTALL)
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if m:
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ch_num = int(m.group(1))
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# Clean the title: replace newlines with spaces, normalize whitespace
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ch_title = re.sub(r"\s+", " ", m.group(2)).strip()
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# Capitalize "love" in "Learning to love" -> "Learning to Love"
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if ch_title == "Learning to love":
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ch_title = "Learning to Love"
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bookmarks.append((i, ch_num, ch_title))
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return bookmarks
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def extract_paragraphs(
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all_paragraphs: list,
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start_idx: int,
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end_idx: int,
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is_front_pages: bool = False,
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) -> list:
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"""Extract paragraphs for a section (chapter or intro segment).
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Handles all CSS classes within the MG content.
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Groups consecutive p53/p24 verse pādas into centered italic divs.
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"""
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result = []
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verse_accumulator = []
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def flush_verse():
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if verse_accumulator:
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joined = "<br>".join(verse_accumulator)
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result.append(f'<div style="text-align:center"><i>{joined}</i></div>')
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verse_accumulator.clear()
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for i in range(start_idx, end_idx):
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p = all_paragraphs[i]
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cls = get_css_class(p)
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# Skip empty paragraphs
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if is_empty(p):
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continue
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# Skip noise classes
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if cls in SKIP_CLASSES:
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continue
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# Skip bookmark tags (p13)
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if cls == CHAPTER_BOOKMARK_CLASS:
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continue
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# Skip book title header (p12)
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if cls == BOOK_HEADER_CLASS:
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continue
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# Check for dash lines — skip them
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text = decode_text(p)
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if is_dash_line(text):
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continue
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# Sanskrit verse pādas (p53 and p24) — accumulate into centered block
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if cls in VERSE_PADA_CLASSES:
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html = decode_html_content(p)
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if html:
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# Remove inner <i> tags before re-wrapping
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stripped = re.sub(r"</?i>", "", html)
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verse_accumulator.append(stripped)
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continue
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# Non-verse paragraph — flush any accumulated verse first
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flush_verse()
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# Chapter heading (p27) — include as paragraph
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if cls == CHAPTER_HEADING_CLASS:
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html = decode_html_content(p)
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if html:
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result.append(html)
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continue
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# Title page text (p3) — include
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if cls == TITLE_PAGE_CLASS:
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html = decode_html_content(p)
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if html:
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result.append(html)
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continue
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# Book list classes (p76, p77) — include in front-pages
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if cls in BOOK_LIST_CLASSES:
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if is_front_pages:
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html = decode_html_content(p)
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if html:
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result.append(html)
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continue
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# Verse translation / body text (p44)
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if cls == TRANSLATION_CLASS:
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html = decode_html_content(p)
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if html:
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result.append(html)
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continue
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# Body paragraphs (p6, p78, p30)
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if cls in BODY_CLASSES:
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html = decode_html_content(p)
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if html:
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result.append(html)
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continue
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# Any other class — try to extract content (be permissive)
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html = decode_html_content(p)
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if html and not is_dash_line(text):
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result.append(html)
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# Flush any trailing verse
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flush_verse()
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return result
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# ---------------------------------------------------------------------------
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# Main extraction logic
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# ---------------------------------------------------------------------------
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def extract_mg(soup: BeautifulSoup) -> dict:
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"""Extract the entire Kṛṣṇa Consciousness: The Matchless Gift (1974) from the parsed HTML.
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Returns a dict with:
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"intro_segments": list of intro segment dicts
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"chapters": list of chapter dicts
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"chapter_listing": list of {number, title} for intro.json
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"""
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all_paragraphs = list(soup.find_all("p"))
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# Step 1: Find MG boundaries
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mg_start, mg_end = find_mg_boundaries(all_paragraphs)
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print(f" MG content: paragraphs {mg_start} to {mg_end}")
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print(f" Total paragraphs in range: {mg_end - mg_start}")
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# Step 2: Find all chapter bookmarks
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chapter_bookmarks = find_chapter_bookmarks(all_paragraphs, mg_start, mg_end)
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print(f" Found {len(chapter_bookmarks)} MG chapter bookmarks")
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for pos, ch_num, ch_title in chapter_bookmarks:
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print(f" [chapter {ch_num}] paragraph {pos}: {ch_title}")
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# Step 3: Extract front pages
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# From book start (mg_start) to first chapter bookmark (MG 1)
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intro_segments = []
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if chapter_bookmarks:
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front_pages_end = chapter_bookmarks[0][0]
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else:
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front_pages_end = mg_end
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front_pages_paragraphs = extract_paragraphs(
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all_paragraphs, mg_start, front_pages_end, is_front_pages=True
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)
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intro_segments.append(
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{
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"slug": "front-pages",
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"title": "Front Pages",
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"type": "intro-segment",
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"book": "mg",
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"order": 1,
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"paragraphs": front_pages_paragraphs,
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}
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)
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# Step 4: Extract chapters
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chapters = []
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for idx, (ch_pos, ch_num, ch_title) in enumerate(chapter_bookmarks):
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content_start = ch_pos + 1
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# Content ends at next chapter bookmark or mg_end
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if idx + 1 < len(chapter_bookmarks):
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content_end = chapter_bookmarks[idx + 1][0]
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else:
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content_end = mg_end
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paragraphs = extract_paragraphs(all_paragraphs, content_start, content_end)
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ch = {
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"book": "mg",
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"chapter": ch_num,
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"title": ch_title,
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"type": "chapter",
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"paragraphs": paragraphs,
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}
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chapters.append(ch)
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# Step 5: Build chapter listing for intro.json
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chapter_listing = [
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{"number": ch["chapter"], "title": ch["title"]} for ch in chapters
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]
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return {
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"intro_segments": intro_segments,
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"chapters": chapters,
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"chapter_listing": chapter_listing,
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}
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# ---------------------------------------------------------------------------
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# Output
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# ---------------------------------------------------------------------------
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def write_json(data, filepath):
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"""Write data as formatted JSON to filepath, creating directories as needed."""
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os.makedirs(os.path.dirname(filepath), exist_ok=True)
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with open(filepath, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=2)
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def main():
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base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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html_path = os.path.join(base_dir, "public", "html", "other.html")
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output_base = os.path.join(base_dir, "lib", "jsons", "books", "mg")
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if not os.path.exists(html_path):
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print(f"ERROR: {html_path} not found!")
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sys.exit(1)
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print(f"Reading {html_path}...")
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with open(html_path, "r", encoding="utf-8") as f:
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soup = BeautifulSoup(f, "html.parser")
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print("Extracting Kṛṣṇa Consciousness: The Matchless Gift (1974)...\n")
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result = extract_mg(soup)
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intro_segments = result["intro_segments"]
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chapters = result["chapters"]
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chapter_listing = result["chapter_listing"]
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# --- Write intro.json ---
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intro_meta = {
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"book": "mg",
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"title": "Kṛṣṇa Consciousness: The Matchless Gift",
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"author": "His Divine Grace A.C. Bhaktivedanta Swami Prabhupāda",
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"year": 1974,
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"description": "A profound introduction to the science of Kṛṣṇa consciousness and the matchless gift of love of God.",
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"totalChapters": 7,
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"chapters": chapter_listing,
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}
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intro_path = os.path.join(output_base, "intro.json")
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write_json(intro_meta, intro_path)
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print(f"\n Written: {intro_path}")
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# --- Write intro segments ---
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print(f"\n--- Intro Segments ---")
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for seg in intro_segments:
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filepath = os.path.join(output_base, "intro", f"{seg['slug']}.json")
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write_json(seg, filepath)
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print(
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f" {seg['order']}. {seg['title']}: {len(seg['paragraphs'])} paragraphs → {filepath}"
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)
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# --- Write chapters ---
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print(f"\n--- Chapters ---")
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for ch in chapters:
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ch_num_str = str(ch["chapter"]).zfill(2)
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filepath = os.path.join(output_base, "chapters", f"{ch_num_str}.json")
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write_json(ch, filepath)
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print(
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f" Ch {ch['chapter']:>2}: {len(ch['paragraphs']):>3} paragraphs — "
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f"{ch['title']}"
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)
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# --- Summary ---
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total_intro_paras = sum(len(s["paragraphs"]) for s in intro_segments)
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total_chapter_paras = sum(len(ch["paragraphs"]) for ch in chapters)
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total_paras = total_intro_paras + total_chapter_paras
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print(f"\n{'=' * 60}")
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print(f" EXTRACTION SUMMARY")
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print(f"{'=' * 60}")
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print(f" Intro segments: {len(intro_segments)}")
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print(f" Chapters: {len(chapters)}")
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print(f" Total paragraphs: {total_paras}")
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print(f" Intro: {total_intro_paras}")
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print(f" Chapters: {total_chapter_paras}")
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if chapters:
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print(f" Avg paras/chapter: {total_chapter_paras / len(chapters):.1f}")
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print(f" Output directory: {output_base}/")
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# --- Samples ---
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print(f"\n--- Sample: Front Pages ---")
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if intro_segments:
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front = intro_segments[0]
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for i, p in enumerate(front["paragraphs"][:5]):
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preview = re.sub(r"<[^>]+>", "", p)[:100]
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print(f" [{i}] {preview}...")
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if chapters:
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print(f"\n--- Sample: Chapter 1 ({chapters[0]['title']}) ---")
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ch1 = chapters[0]
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for i, p in enumerate(ch1["paragraphs"][:5]):
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preview = re.sub(r"<[^>]+>", "", p)[:100]
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print(f" [{i}] {preview}...")
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if chapters and len(chapters) > 1:
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print(
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f"\n--- Sample: Chapter {chapters[-1]['chapter']} ({chapters[-1]['title']}) ---"
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)
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last = chapters[-1]
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for i, p in enumerate(last["paragraphs"][:5]):
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preview = re.sub(r"<[^>]+>", "", p)[:100]
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print(f" [{i}] {preview}...")
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# --- Quality Checks ---
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print(f"\n--- Quality Checks ---")
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# Check all 7 chapters present
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expected_chapters = set(range(1, 8))
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actual_chapters = set(ch["chapter"] for ch in chapters)
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missing = expected_chapters - actual_chapters
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extra = actual_chapters - expected_chapters
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if missing:
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print(f" ⚠ Missing chapters: {sorted(missing)}")
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if extra:
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print(f" ⚠ Extra chapters: {sorted(extra)}")
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if not missing and not extra:
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print(f" ✅ All 7 chapters present")
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# Check all 1 intro segment present
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expected_intros = {"front-pages"}
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actual_intros = {s["slug"] for s in intro_segments}
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missing_intros = expected_intros - actual_intros
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extra_intros = actual_intros - expected_intros
|
|
if missing_intros:
|
|
print(f" ⚠ Missing intro segments: {missing_intros}")
|
|
if extra_intros:
|
|
print(f" ⚠ Extra intro segments: {extra_intros}")
|
|
if not missing_intros and not extra_intros:
|
|
print(f" ✅ All 1 intro segments present")
|
|
|
|
# Check empty chapters
|
|
empty_chapters = [ch["chapter"] for ch in chapters if len(ch["paragraphs"]) == 0]
|
|
if empty_chapters:
|
|
print(f" ⚠ Empty chapters (0 paragraphs): {empty_chapters}")
|
|
else:
|
|
print(f" ✅ No empty chapters")
|
|
|
|
# Check empty intro segments
|
|
empty_intros = [s["slug"] for s in intro_segments if len(s["paragraphs"]) == 0]
|
|
if empty_intros:
|
|
print(f" ⚠ Empty intro segments: {empty_intros}")
|
|
else:
|
|
print(f" ✅ No empty intro segments")
|
|
|
|
# Check ScaGoudy decoding
|
|
print(f"\n--- ScaGoudy Decoding Check ---")
|
|
all_text = ""
|
|
for ch in chapters:
|
|
all_text += " ".join(ch["paragraphs"][:20])
|
|
for seg in intro_segments:
|
|
all_text += " ".join(seg["paragraphs"][:10])
|
|
|
|
# Check for properly decoded words
|
|
if "Kṛṣṇa" in all_text:
|
|
print(f" ✅ 'Kṛṣṇa' found (ScaGoudy properly decoded)")
|
|
else:
|
|
print(f" ⚠ 'Kṛṣṇa' not found — ScaGoudy decoding may have failed")
|
|
|
|
if "Bhagavad-gītā" in all_text:
|
|
print(f" ✅ 'Bhagavad-gītā' found (ScaGoudy properly decoded)")
|
|
elif "gītā" in all_text or "Gītā" in all_text:
|
|
print(f" ✅ 'gītā' found (ScaGoudy properly decoded)")
|
|
else:
|
|
print(f" ⚠ 'Gītā' not found — ScaGoudy decoding may have failed")
|
|
|
|
if "Prabhupāda" in all_text:
|
|
print(f" ✅ 'Prabhupāda' found (ScaGoudy properly decoded)")
|
|
|
|
# Check for un-decoded ScaGoudy remnants (common encoded chars)
|
|
scagoudy_remnants = 0
|
|
for ch in chapters:
|
|
for p in ch["paragraphs"]:
|
|
if "Kåñëa" in p or "Prabhupäda" in p:
|
|
scagoudy_remnants += 1
|
|
for seg in intro_segments:
|
|
for p in seg["paragraphs"]:
|
|
if "Kåñëa" in p or "Prabhupäda" in p:
|
|
scagoudy_remnants += 1
|
|
if scagoudy_remnants == 0:
|
|
print(f" ✅ No ScaGoudy remnants detected")
|
|
else:
|
|
print(f" ⚠ {scagoudy_remnants} paragraphs with ScaGoudy remnants")
|
|
|
|
# Check for centered verse blocks
|
|
verse_block_count = 0
|
|
for ch in chapters:
|
|
for p in ch["paragraphs"]:
|
|
if '<div style="text-align:center"><i>' in p:
|
|
verse_block_count += 1
|
|
if verse_block_count > 0:
|
|
print(f" ✅ {verse_block_count} centered Sanskrit verse blocks found")
|
|
else:
|
|
print(f" ⚠ No centered verse blocks found — verse grouping may have failed")
|
|
|
|
# Check for residual span tags
|
|
span_count = 0
|
|
for ch in chapters:
|
|
for p in ch["paragraphs"]:
|
|
if 'class="' in p and "<span" in p:
|
|
span_count += 1
|
|
for seg in intro_segments:
|
|
for p in seg["paragraphs"]:
|
|
if 'class="' in p and "<span" in p:
|
|
span_count += 1
|
|
if span_count == 0:
|
|
print(f" ✅ No residual span class artifacts found")
|
|
else:
|
|
print(f" ⚠ {span_count} paragraphs still contain span class artifacts")
|
|
|
|
# Check for dash-line paragraphs that shouldn't be there
|
|
dash_count = 0
|
|
for ch in chapters:
|
|
for p in ch["paragraphs"]:
|
|
plain = re.sub(r"<[^>]+>", "", p)
|
|
if is_dash_line(plain):
|
|
dash_count += 1
|
|
for seg in intro_segments:
|
|
for p in seg["paragraphs"]:
|
|
plain = re.sub(r"<[^>]+>", "", p)
|
|
if is_dash_line(plain):
|
|
dash_count += 1
|
|
if dash_count == 0:
|
|
print(f" ✅ No dashed-line paragraphs in output")
|
|
else:
|
|
print(f" ⚠ {dash_count} dashed-line paragraphs found in output")
|
|
|
|
# Show a verse block sample
|
|
print(f"\n--- Sample: Verse Block ---")
|
|
for ch in chapters:
|
|
for p in ch["paragraphs"]:
|
|
if '<div style="text-align:center"><i>' in p:
|
|
preview = re.sub(r"<[^>]+>", "", p)[:120]
|
|
print(f" {preview}...")
|
|
break
|
|
else:
|
|
continue
|
|
break
|
|
|
|
# Chapter title decoding check
|
|
print(f"\n--- Chapter Title Decoding Check ---")
|
|
for ch in chapters:
|
|
title = ch["title"]
|
|
if "å" in title or "ë" in title or "ñ" in title or "ä" in title:
|
|
print(f" ⚠ Chapter {ch['chapter']} title has un-decoded ScaGoudy: {title}")
|
|
else:
|
|
print(f" ✅ Ch {ch['chapter']}: {title}")
|
|
|
|
print(f"\nDone! ✨")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|