849 lines
28 KiB
Python
849 lines
28 KiB
Python
#!/usr/bin/env python3
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"""Extract Perfect Questions Perfect Answers (1977) from other.html.
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The book has 3 intro segments (front-pages, introduction, about-the-author),
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9 chapters, and a Concluding Words section.
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Output:
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lib/jsons/books/pqpa/intro.json — Book metadata
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lib/jsons/books/pqpa/intro/*.json — Intro segments
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lib/jsons/books/pqpa/chapters/*.json — Chapters + concluding words
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Usage:
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python3 scripts/pqpa_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
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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 PQPA
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# ---------------------------------------------------------------------------
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# Book title header class
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BOOK_HEADER_CLASS = "p12"
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# Bookmark class — used for section boundary detection
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BOOKMARK_CLASS = "p13"
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# Body paragraph classes (primary content)
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BODY_CLASSES = frozenset(["p6", "p48"])
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# Dialogue line class — the main content class (714 occurrences)
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DIALOGUE_CLASS = "p92"
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# Sanskrit verse pāda classes (italic, centered)
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VERSE_PADA_CLASSES = frozenset(["p53", "p66"])
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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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# Section heading (bold)
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SECTION_HEADING_CLASS = "p31"
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# Front pages text
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FRONT_PAGES_CLASS = "p18"
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# Attribution text
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ATTRIBUTION_CLASS = "p42"
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# Numbered items / verse quotes
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NUMBERED_CLASS = "p24"
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# Mantra text
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MANTRA_CLASS = "p19"
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# Contents heading
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CONTENTS_CLASS = "p69"
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# Title page text
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TITLE_PAGE_CLASS = "p3"
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# Misc classes to include
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MISC_CLASSES = frozenset(["p28", "p76", "p93", "p94", "p95", "p96"])
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# Classes to skip entirely (empty/decorative)
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SKIP_CLASSES = frozenset(["p9", "p15", "p70"])
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# ---------------------------------------------------------------------------
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# Extraction helpers
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# ---------------------------------------------------------------------------
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def find_pqpa_boundaries(all_paragraphs: list) -> tuple:
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"""Find the start and end paragraph indices of the PQPA content.
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Start: <p class="p12"> containing "Perfect Questions Perfect Answers"
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End: next <p class="p12"> after start, or end of file
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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 "Perfect Questions Perfect Answers" 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 PQPA start = end of PQPA
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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 'Perfect Questions Perfect Answers' header in other.html!"
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)
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sys.exit(1)
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# Also check for Glossary marker as end boundary
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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 == TITLE_PAGE_CLASS:
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text = p.get_text().strip()
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if text == "Glossary":
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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_all_bookmarks(all_paragraphs: list, start_idx: int, end_idx: int) -> list:
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"""Find all p13 bookmark tags in the PQPA range.
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Returns list of (paragraph_index, bookmark_text).
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The bookmark text is stripped of any span noise like <span class="s27"> </span>.
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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 == BOOKMARK_CLASS:
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# Get text, stripping span noise
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text = decode_text(p).strip()
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if text.startswith("PQPA"):
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bookmarks.append((i, text))
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return bookmarks
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def parse_chapter_bookmark(text: str):
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"""Parse a chapter bookmark like 'PQPA 1: Kṛṣṇa, the All-Attractive'.
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Returns (chapter_number, title) or None if not a chapter bookmark.
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"""
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m = re.match(r"PQPA\s+(\d+):\s+(.+)", text.strip())
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if m:
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return int(m.group(1)), m.group(2).strip()
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return None
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def parse_intro_bookmark(text: str) -> bool:
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"""Check if this is the Introduction bookmark."""
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return text.strip() == "PQPA Introduction"
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def parse_concluding_bookmark(text: str) -> bool:
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"""Check if this is the Concluding Words bookmark."""
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return text.strip() == "PQPA Concluding Words"
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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, intro segment, or conclusion).
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Handles all CSS classes within the PQPA content.
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Groups consecutive p53/p66 verse pādas into centered italic divs.
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Detects verse patterns in p6/p92 (starts with <i>, has <br>).
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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 == 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, p66) — 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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# Dialogue lines (p92) — the main content class
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if cls == DIALOGUE_CLASS:
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html = decode_html_content(p)
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if html:
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# Detect verses disguised as dialogue: starts with <i> and has <br>
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stripped_html = html.strip()
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if stripped_html.startswith("<i>") and "<br" in stripped_html:
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inner = re.sub(r"</?i>", "", stripped_html)
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result.append(
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f'<div style="text-align:center"><i>{inner}</i></div>'
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)
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else:
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result.append(html)
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continue
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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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# Section heading (p31) — include as paragraph
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if cls == SECTION_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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# Front pages text (p18) — include
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if cls == FRONT_PAGES_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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# Contents heading (p69) — include
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if cls == CONTENTS_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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# Attribution text (p42)
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if cls == ATTRIBUTION_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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# Numbered items / verse quotes (p24)
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if cls == NUMBERED_CLASS:
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html = decode_html_content(p)
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if html:
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# Detect verses: starts with <i> and has <br>
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stripped_html = html.strip()
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if stripped_html.startswith("<i>") and "<br" in stripped_html:
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inner = re.sub(r"</?i>", "", stripped_html)
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result.append(
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f'<div style="text-align:center"><i>{inner}</i></div>'
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)
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else:
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result.append(html)
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continue
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# Mantra text (p19) — wrap in centered div
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if cls == MANTRA_CLASS:
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html = decode_html_content(p)
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if html:
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# Check if it's a multi-line verse
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if "<br" in html:
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inner = re.sub(r"</?i>", "", html)
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result.append(
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f'<div style="text-align:center"><i>{inner}</i></div>'
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)
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else:
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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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# 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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# Body paragraphs (p6, p48)
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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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# Detect Sanskrit verses disguised as body paragraphs:
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# they start with <i> and contain <br> (multi-line italic = verse)
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stripped_html = html.strip()
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if stripped_html.startswith("<i>") and "<br" in stripped_html:
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inner = re.sub(r"</?i>", "", stripped_html)
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result.append(
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f'<div style="text-align:center"><i>{inner}</i></div>'
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)
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else:
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result.append(html)
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continue
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# Misc classes — include as-is
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if cls in MISC_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_pqpa(soup: BeautifulSoup) -> dict:
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"""Extract the entire Perfect Questions Perfect Answers 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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"concluding_words": concluding words dict
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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 PQPA boundaries
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pqpa_start, pqpa_end = find_pqpa_boundaries(all_paragraphs)
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print(f" PQPA content: paragraphs {pqpa_start} to {pqpa_end}")
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print(f" Total paragraphs in range: {pqpa_end - pqpa_start}")
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# Step 2: Find all bookmarks
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bookmarks = find_all_bookmarks(all_paragraphs, pqpa_start, pqpa_end)
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print(f" Found {len(bookmarks)} PQPA bookmarks")
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for pos, text in bookmarks:
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print(f" Line ~{pos}: {text}")
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# Step 3: Categorize bookmarks
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intro_bookmark = None # (para_idx,)
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chapter_bookmarks = [] # (para_idx, chapter_num, title)
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concluding_bookmark = None # (para_idx,)
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for pos, text in bookmarks:
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ch = parse_chapter_bookmark(text)
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if ch:
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chapter_bookmarks.append((pos, ch[0], ch[1]))
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elif parse_intro_bookmark(text):
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intro_bookmark = pos
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elif parse_concluding_bookmark(text):
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concluding_bookmark = pos
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print(
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f" Introduction bookmark: {'found' if intro_bookmark is not None else 'NOT FOUND'}"
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)
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print(f" Chapter bookmarks: {len(chapter_bookmarks)}")
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print(
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f" Concluding Words bookmark: {'found' if concluding_bookmark is not None else 'NOT FOUND'}"
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)
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# Collect all section starts for boundary computation
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all_section_starts = sorted(
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([intro_bookmark] if intro_bookmark is not None else [])
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+ [pos for pos, _, _ in chapter_bookmarks]
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+ ([concluding_bookmark] if concluding_bookmark is not None else [])
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)
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# Step 4: Extract front-pages (from p12 header to Introduction bookmark)
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front_pages_start = pqpa_start + 1 # after p12 header
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front_pages_end = (
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intro_bookmark
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if intro_bookmark is not None
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else (chapter_bookmarks[0][0] if chapter_bookmarks else pqpa_end)
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)
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front_pages_paras = extract_paragraphs(
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all_paragraphs, front_pages_start, front_pages_end, is_front_pages=True
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)
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front_pages = {
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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": "pqpa",
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"order": 1,
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"paragraphs": front_pages_paras,
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}
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# Step 5: Extract Introduction (from PQPA Introduction to PQPA 1)
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introduction = None
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if intro_bookmark is not None:
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intro_content_start = intro_bookmark + 1
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# End at the first chapter bookmark
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intro_content_end = chapter_bookmarks[0][0] if chapter_bookmarks else pqpa_end
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intro_paras = extract_paragraphs(
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all_paragraphs, intro_content_start, intro_content_end
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)
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introduction = {
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"slug": "introduction",
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"title": "Introduction",
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"type": "intro-segment",
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"book": "pqpa",
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"order": 2,
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"paragraphs": intro_paras,
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}
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# Step 6: Extract chapters 1-9
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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 concluding words, or pqpa_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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elif concluding_bookmark is not None:
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content_end = concluding_bookmark
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else:
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content_end = pqpa_end
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paragraphs = extract_paragraphs(all_paragraphs, content_start, content_end)
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ch = {
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"book": "pqpa",
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"chapter": ch_num,
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"title": ch_title,
|
||
"type": "chapter",
|
||
"paragraphs": paragraphs,
|
||
}
|
||
chapters.append(ch)
|
||
|
||
# Step 7: Extract Concluding Words + About the Author
|
||
concluding_words = None
|
||
about_the_author = None
|
||
|
||
if concluding_bookmark is not None:
|
||
# Find the "About the Author" boundary:
|
||
# <p class="p27"><b>His Divine Grace A. C. Bhaktivedanta Swami Prabhupāda</b></p>
|
||
about_author_pos = None
|
||
for i in range(concluding_bookmark + 1, pqpa_end):
|
||
p = all_paragraphs[i]
|
||
cls = get_css_class(p)
|
||
if cls == CHAPTER_HEADING_CLASS:
|
||
text = decode_text(p)
|
||
if "His Divine Grace" in text:
|
||
about_author_pos = i
|
||
break
|
||
|
||
# Concluding Words: from bookmark to About the Author (or pqpa_end)
|
||
cw_content_start = concluding_bookmark + 1
|
||
cw_content_end = about_author_pos if about_author_pos is not None else pqpa_end
|
||
|
||
cw_paras = extract_paragraphs(all_paragraphs, cw_content_start, cw_content_end)
|
||
|
||
concluding_words = {
|
||
"book": "pqpa",
|
||
"chapter": None,
|
||
"title": "Concluding Words",
|
||
"type": "conclusion",
|
||
"slug": "concluding-words",
|
||
"paragraphs": cw_paras,
|
||
}
|
||
|
||
# About the Author: from p27 heading to pqpa_end
|
||
if about_author_pos is not None:
|
||
ata_paras = extract_paragraphs(all_paragraphs, about_author_pos, pqpa_end)
|
||
about_the_author = {
|
||
"slug": "about-the-author",
|
||
"title": "About the Author",
|
||
"type": "intro-segment",
|
||
"book": "pqpa",
|
||
"order": 99,
|
||
"paragraphs": ata_paras,
|
||
}
|
||
|
||
# Step 8: Build intro segments list
|
||
intro_segments = [front_pages]
|
||
if introduction:
|
||
intro_segments.append(introduction)
|
||
if about_the_author:
|
||
intro_segments.append(about_the_author)
|
||
|
||
# Step 9: Build chapter listing for intro.json
|
||
chapter_listing = [
|
||
{"number": ch["chapter"], "title": ch["title"]} for ch in chapters
|
||
]
|
||
|
||
return {
|
||
"intro_segments": intro_segments,
|
||
"chapters": chapters,
|
||
"concluding_words": concluding_words,
|
||
"chapter_listing": chapter_listing,
|
||
}
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Output
|
||
# ---------------------------------------------------------------------------
|
||
|
||
|
||
def write_json(data, filepath):
|
||
"""Write data as formatted JSON to filepath, creating directories as needed."""
|
||
os.makedirs(os.path.dirname(filepath), exist_ok=True)
|
||
with open(filepath, "w", encoding="utf-8") as f:
|
||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||
|
||
|
||
def main():
|
||
base_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||
html_path = os.path.join(base_dir, "public", "html", "other.html")
|
||
output_base = os.path.join(base_dir, "lib", "jsons", "books", "pqpa")
|
||
|
||
if not os.path.exists(html_path):
|
||
print(f"ERROR: {html_path} not found!")
|
||
sys.exit(1)
|
||
|
||
print(f"Reading {html_path}...")
|
||
with open(html_path, "r", encoding="utf-8") as f:
|
||
soup = BeautifulSoup(f, "html.parser")
|
||
|
||
print("Extracting Perfect Questions Perfect Answers (1977)...\n")
|
||
result = extract_pqpa(soup)
|
||
|
||
intro_segments = result["intro_segments"]
|
||
chapters = result["chapters"]
|
||
concluding_words = result["concluding_words"]
|
||
chapter_listing = result["chapter_listing"]
|
||
|
||
# --- Write intro.json ---
|
||
intro_meta = {
|
||
"book": "pqpa",
|
||
"title": "Perfect Questions Perfect Answers",
|
||
"author": "His Divine Grace A.C. Bhaktivedanta Swami Prabhupāda",
|
||
"year": 1977,
|
||
"description": "Conversations between Śrīla Prabhupāda and Bob Cohen, a Peace Corps worker in India.",
|
||
"totalChapters": 9,
|
||
"chapters": chapter_listing,
|
||
}
|
||
intro_path = os.path.join(output_base, "intro.json")
|
||
write_json(intro_meta, intro_path)
|
||
print(f"\n Written: {intro_path}")
|
||
|
||
# --- Write intro segments ---
|
||
print(f"\n--- Intro Segments ---")
|
||
for seg in intro_segments:
|
||
filepath = os.path.join(output_base, "intro", f"{seg['slug']}.json")
|
||
write_json(seg, filepath)
|
||
print(
|
||
f" {seg['order']}. {seg['title']}: {len(seg['paragraphs'])} paragraphs → {filepath}"
|
||
)
|
||
|
||
# --- Write chapters ---
|
||
print(f"\n--- Chapters ---")
|
||
for ch in chapters:
|
||
ch_num_str = str(ch["chapter"]).zfill(2)
|
||
filepath = os.path.join(output_base, "chapters", f"{ch_num_str}.json")
|
||
write_json(ch, filepath)
|
||
print(
|
||
f" Ch {ch['chapter']:>2}: {len(ch['paragraphs']):>3} paragraphs — "
|
||
f"{ch['title']}"
|
||
)
|
||
|
||
# --- Write concluding words ---
|
||
if concluding_words:
|
||
filepath = os.path.join(output_base, "chapters", "concluding-words.json")
|
||
write_json(concluding_words, filepath)
|
||
print(
|
||
f"\n Concluding Words: {len(concluding_words['paragraphs'])} paragraphs → {filepath}"
|
||
)
|
||
|
||
# --- Summary ---
|
||
total_intro_paras = sum(len(s["paragraphs"]) for s in intro_segments)
|
||
total_chapter_paras = sum(len(ch["paragraphs"]) for ch in chapters)
|
||
total_concluding_paras = (
|
||
len(concluding_words["paragraphs"]) if concluding_words else 0
|
||
)
|
||
total_paras = total_intro_paras + total_chapter_paras + total_concluding_paras
|
||
|
||
print(f"\n{'=' * 60}")
|
||
print(f" EXTRACTION SUMMARY")
|
||
print(f"{'=' * 60}")
|
||
print(f" Intro segments: {len(intro_segments)}")
|
||
for seg in intro_segments:
|
||
print(
|
||
f" {seg['order']}. {seg['title']}: {len(seg['paragraphs'])} paragraphs"
|
||
)
|
||
print(f" Chapters: {len(chapters)}")
|
||
print(f" Concluding Words: {'Yes' if concluding_words else 'No'}")
|
||
print(f" Total paragraphs: {total_paras}")
|
||
print(f" Intro: {total_intro_paras}")
|
||
print(f" Chapters: {total_chapter_paras}")
|
||
print(f" Concluding: {total_concluding_paras}")
|
||
if chapters:
|
||
print(f" Avg paras/chapter: {total_chapter_paras / len(chapters):.1f}")
|
||
print(f" Output directory: {output_base}/")
|
||
|
||
# --- Samples ---
|
||
print(f"\n--- Sample: Chapter 1 ---")
|
||
if chapters:
|
||
ch1 = chapters[0]
|
||
for i, p in enumerate(ch1["paragraphs"][:4]):
|
||
preview = re.sub(r"<[^>]+>", "", p)[:120]
|
||
print(f" [{i}] {preview}...")
|
||
|
||
if len(chapters) >= 9:
|
||
print(f"\n--- Sample: Chapter 9 (last) ---")
|
||
last = chapters[-1]
|
||
for i, p in enumerate(last["paragraphs"][:3]):
|
||
preview = re.sub(r"<[^>]+>", "", p)[:120]
|
||
print(f" [{i}] {preview}...")
|
||
|
||
if concluding_words:
|
||
print(f"\n--- Sample: Concluding Words ---")
|
||
for i, p in enumerate(concluding_words["paragraphs"][:3]):
|
||
preview = re.sub(r"<[^>]+>", "", p)[:120]
|
||
print(f" [{i}] {preview}...")
|
||
|
||
# --- Quality Checks ---
|
||
print(f"\n--- Quality Checks ---")
|
||
|
||
# Check all 9 chapters present
|
||
expected_chapters = set(range(1, 10))
|
||
actual_chapters = set(ch["chapter"] for ch in chapters)
|
||
missing = expected_chapters - actual_chapters
|
||
extra = actual_chapters - expected_chapters
|
||
if missing:
|
||
print(f" ⚠ Missing chapters: {sorted(missing)}")
|
||
if extra:
|
||
print(f" ⚠ Extra chapters: {sorted(extra)}")
|
||
if not missing and not extra:
|
||
print(f" ✅ All 9 chapters present")
|
||
|
||
# Check intro segments
|
||
expected_intros = {"front-pages", "introduction", "about-the-author"}
|
||
actual_intros = {s["slug"] for s in intro_segments}
|
||
missing_intros = expected_intros - actual_intros
|
||
if missing_intros:
|
||
print(f" ⚠ Missing intro segments: {missing_intros}")
|
||
else:
|
||
print(f" ✅ All 3 intro segments present")
|
||
|
||
# Check concluding words
|
||
if concluding_words:
|
||
print(f" ✅ Concluding Words present")
|
||
else:
|
||
print(f" ⚠ Concluding Words NOT found")
|
||
|
||
# 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 ScaGoudy decoding — look for common encoded characters
|
||
encoded_chars = set("äåöëòçüéàùè")
|
||
suspect_count = 0
|
||
all_sections = chapters + intro_segments
|
||
if concluding_words:
|
||
all_sections = all_sections + [concluding_words]
|
||
for section in all_sections:
|
||
for p in section["paragraphs"]:
|
||
plain = re.sub(r"<[^>]+>", "", p)
|
||
for c in encoded_chars:
|
||
if c in plain:
|
||
suspect_count += 1
|
||
break
|
||
if suspect_count == 0:
|
||
print(f" ✅ No suspected undecoded ScaGoudy characters found")
|
||
else:
|
||
print(
|
||
f" ℹ {suspect_count} paragraphs contain characters that may be undecoded ScaGoudy"
|
||
)
|
||
|
||
# Check 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
|
||
print(f" ℹ {verse_block_count} verse blocks in chapters")
|
||
|
||
# ScaGoudy decoding spot check
|
||
print(f"\n--- ScaGoudy Decoding Spot Check ---")
|
||
if chapters:
|
||
ch1_text = " ".join(chapters[0]["paragraphs"][:10])
|
||
if "Kṛṣṇa" in ch1_text:
|
||
print(f" ✅ 'Kṛṣṇa' correctly decoded in Chapter 1")
|
||
elif "Kåñëa" in ch1_text:
|
||
print(f" ⚠ 'Kåñëa' still encoded — ScaGoudy decoding may have failed!")
|
||
else:
|
||
print(
|
||
f" ℹ 'Kṛṣṇa' not found in Chapter 1 first 10 paragraphs (may be normal)"
|
||
)
|
||
|
||
# Check for "Śrīla Prabhupāda"
|
||
if "Prabhupāda" in ch1_text:
|
||
print(f" ✅ 'Prabhupāda' correctly decoded")
|
||
elif "Prabhupäda" in ch1_text:
|
||
print(f" ⚠ ScaGoudy decoding incomplete for 'Prabhupäda'")
|
||
|
||
# Dialogue line check — look for BOB: and ŚRĪLA PRABHUPĀDA: speakers
|
||
print(f"\n--- Dialogue Speaker Check ---")
|
||
speaker_counts = {}
|
||
for ch in chapters:
|
||
for p in ch["paragraphs"]:
|
||
# Look for bold speaker patterns
|
||
m = re.match(r"<b>([^<]+):</b>", p)
|
||
if m:
|
||
speaker = m.group(1).strip()
|
||
speaker_counts[speaker] = speaker_counts.get(speaker, 0) + 1
|
||
if speaker_counts:
|
||
print(f" Speakers found:")
|
||
for speaker, count in sorted(speaker_counts.items(), key=lambda x: -x[1]):
|
||
print(f" {speaker}: {count} lines")
|
||
else:
|
||
print(f" ⚠ No dialogue speakers detected")
|
||
|
||
# Sample verse blocks
|
||
print(f"\n--- Sample Verse Blocks ---")
|
||
shown = 0
|
||
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}...")
|
||
shown += 1
|
||
if shown >= 3:
|
||
break
|
||
if shown >= 3:
|
||
break
|
||
|
||
print(f"\nDone! ✨")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|