feat: add ssr
This commit is contained in:
gosangam
@@ -0,0 +1,803 @@
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#!/usr/bin/env python3
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"""Extract The Nectar of Devotion (1970 Edition) from other.html.
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The book has 3 intro segments (Front Flap, Preface, Introduction),
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51 chapters organized in 4 parts, and a Concluding Words section.
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Output:
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lib/jsons/books/nod/intro.json — Book metadata
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lib/jsons/books/nod/intro/*.json — Intro segments
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lib/jsons/books/nod/chapters/*.json — Chapters + concluding words
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Usage:
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python3 scripts/nod_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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# ---------------------------------------------------------------------------
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# CSS class categorization for NoD
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# ---------------------------------------------------------------------------
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# Bookmark class — used for section boundary detection
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BOOKMARK_CLASS = "p13"
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# Book title header class
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BOOK_HEADER_CLASS = "p12"
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# Body paragraph classes
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BODY_CLASSES = frozenset(["p6", "p35", "p30"])
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# Section heading (bold) — "Chapter One", "Preface", etc.
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SECTION_HEADING_CLASS = "p31"
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# Chapter title (bold) — "Characteristics of Pure Devotional Service", etc.
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CHAPTER_TITLE_CLASS = "p27"
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# Part header class — "PART ONE", "PART TWO", etc.
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PART_HEADER_CLASS = "p34"
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# Sub-section heading (bold) within chapters
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SUB_HEADING_CLASS = "p36"
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# Italic sub-headings
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ITALIC_SUB_HEADING_CLASSES = frozenset(["p37", "p38"])
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# Title page / copyright text classes (in intro area)
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TITLE_PAGE_CLASS = "p3"
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COPYRIGHT_CLASS = "p32"
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# Sanskrit verse (italic with br tags)
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SANSKRIT_VERSE_CLASS = "p33"
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# Classes to skip entirely (empty/decorative)
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SKIP_CLASSES = frozenset(["p9", "p15"])
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# ---------------------------------------------------------------------------
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# Intro segment definitions
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# ---------------------------------------------------------------------------
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INTRO_SEGMENTS = [
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{
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"slug": "front-flap",
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"title": "Front Flap",
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"order": 1,
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"bookmark_pattern": "NoD-1970: Front Flap",
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},
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{
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"slug": "preface",
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"title": "Preface",
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"order": 2,
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"bookmark_pattern": "NoD-1970: Preface",
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},
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{
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"slug": "introduction",
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"title": "Introduction",
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"order": 3,
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"bookmark_pattern": "NoD-1970: Introduction",
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},
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]
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# ---------------------------------------------------------------------------
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# Extraction helpers
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# ---------------------------------------------------------------------------
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def find_nod_boundaries(all_paragraphs: list) -> tuple[int, int]:
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"""Find the start and end paragraph indices of the NoD content.
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Start: <p class="p12"> containing "Nectar of Devotion"
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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 "Nectar of Devotion" 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 NoD start = end of NoD
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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("ERROR: Could not find 'The Nectar of Devotion' header in other.html!")
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sys.exit(1)
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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 NoD range.
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Returns list of (paragraph_index, bookmark_text).
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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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text = decode_text(p)
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if "NoD" in text:
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bookmarks.append((i, text))
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return bookmarks
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def parse_chapter_bookmark(text: str) -> tuple[int, str] | None:
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"""Parse a chapter bookmark like 'NoD 1-1970: Characteristics of Pure Devotional Service'.
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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"NoD\s+(\d+)-1970:\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) -> str | None:
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"""Parse an intro bookmark like 'NoD-1970: Preface'.
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Returns the segment name or None if not an intro bookmark.
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"""
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m = re.match(r"NoD-1970:\s*(.+)", text.strip())
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if m:
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return m.group(1).strip()
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return None
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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_intro: bool = False,
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) -> list[str]:
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"""Extract paragraphs for a section (chapter or intro segment).
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Handles all CSS classes within the NoD content.
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"""
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result: list[str] = []
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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
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if cls == BOOKMARK_CLASS:
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continue
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# Skip book title header
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if cls == BOOK_HEADER_CLASS:
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continue
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# Part headers — include as bold paragraph
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if cls == PART_HEADER_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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# Chapter title (p27) — include as bold paragraph
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if cls == CHAPTER_TITLE_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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# Sub-section headings within chapters (p36, bold)
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if cls == SUB_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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# Italic sub-headings (p37, p38)
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if cls in ITALIC_SUB_HEADING_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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# Title page / copyright text (in intro area)
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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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# Copyright text
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if cls == COPYRIGHT_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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# Sanskrit verse (italic with br tags) — format as centered italic div
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# Strip all inner <i>/<\/i> tags before re-wrapping to avoid nesting
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if cls == SANSKRIT_VERSE_CLASS:
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html = decode_html_content(p)
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if html:
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stripped = re.sub(r"</?i>", "", html)
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result.append(f'<div style="text-align:center"><i>{stripped}</i></div>')
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continue
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# Body paragraphs (p6, p35, 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:
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result.append(html)
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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_nod(soup: BeautifulSoup) -> dict:
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"""Extract the entire Nectar of Devotion 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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"parts": list of part assignment 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 NoD boundaries
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nod_start, nod_end = find_nod_boundaries(all_paragraphs)
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print(f" NoD content: paragraphs {nod_start} to {nod_end}")
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print(f" Total paragraphs in range: {nod_end - nod_start}")
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# Step 2: Find all bookmarks
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bookmarks = find_all_bookmarks(all_paragraphs, nod_start, nod_end)
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print(f" Found {len(bookmarks)} NoD bookmarks")
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# Step 3: Categorize bookmarks into intro vs chapter
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intro_bookmarks = [] # (para_idx, segment_name)
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chapter_bookmarks = [] # (para_idx, chapter_num, title)
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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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else:
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intro_name = parse_intro_bookmark(text)
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if intro_name:
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intro_bookmarks.append((pos, intro_name))
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print(f" Intro bookmarks: {len(intro_bookmarks)}")
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print(f" Chapter bookmarks: {len(chapter_bookmarks)}")
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# Step 4: Track PART assignments by scanning for p34 tags
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# We need to know which chapters belong to which part.
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part_assignments = {} # chapter_num -> part_num
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parts_data = [] # list of {number, title, chapters}
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# Walk through all paragraphs in NoD range and track parts
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# We process this before extracting chapters so we know part assignments
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part_positions = [] # (para_idx, part_title)
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for i in range(nod_start, nod_end):
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p = all_paragraphs[i]
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cls = get_css_class(p)
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if cls == PART_HEADER_CLASS:
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text = decode_text(p)
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if text:
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part_positions.append((i, text))
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print(f" Part headers found: {len(part_positions)}")
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for pos, title in part_positions:
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print(f" Line ~{pos}: {title}")
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# Assign parts to chapters based on position
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# A chapter belongs to the most recent PART header before its bookmark
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part_number_map = {}
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for pos, title in part_positions:
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# Extract part number from title like "PART ONE", "PART TWO", etc.
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part_num = None
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title_upper = title.upper()
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if "ONE" in title_upper or "I" == title_upper.replace("PART", "").strip():
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part_num = 1
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elif "TWO" in title_upper or "II" == title_upper.replace("PART", "").strip():
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part_num = 2
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elif "THREE" in title_upper or "III" == title_upper.replace("PART", "").strip():
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part_num = 3
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elif "FOUR" in title_upper or "IV" == title_upper.replace("PART", "").strip():
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part_num = 4
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if part_num:
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part_number_map[pos] = part_num
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# Now assign each chapter to a part
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sorted_part_positions = sorted(part_number_map.keys())
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for ch_pos, ch_num, ch_title in chapter_bookmarks:
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# Find the most recent PART header before this chapter's bookmark
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assigned_part = None
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for pp in sorted_part_positions:
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if pp < ch_pos:
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assigned_part = part_number_map[pp]
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else:
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break
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if assigned_part:
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part_assignments[ch_num] = assigned_part
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# Build parts_data
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for part_num in sorted(set(part_assignments.values())):
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chs = sorted([ch for ch, pn in part_assignments.items() if pn == part_num])
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part_title = f"Part {'One' if part_num == 1 else 'Two' if part_num == 2 else 'Three' if part_num == 3 else 'Four'}"
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parts_data.append(
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{
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"number": part_num,
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"title": part_title,
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"chapters": chs,
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}
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)
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# Step 5: Extract intro segments
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intro_segments = []
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for seg_def in INTRO_SEGMENTS:
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# Find matching bookmark
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bookmark_pos = None
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for pos, name in intro_bookmarks:
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if (
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seg_def["bookmark_pattern"].endswith(name)
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or name == seg_def["bookmark_pattern"].split(": ", 1)[-1]
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):
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bookmark_pos = pos
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break
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if bookmark_pos is None:
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print(f" WARNING: Could not find bookmark for '{seg_def['slug']}'")
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continue
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# Content starts after the bookmark
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content_start = bookmark_pos + 1
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# Content ends at the next bookmark (intro or chapter) or PART header
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content_end = nod_end
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all_section_starts = sorted(
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[pos for pos, _ in intro_bookmarks]
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+ [pos for pos, _, _ in chapter_bookmarks]
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)
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for s in all_section_starts:
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if s > bookmark_pos:
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content_end = s
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break
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paragraphs = extract_paragraphs(
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all_paragraphs, content_start, content_end, is_intro=True
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)
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seg = {
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"slug": seg_def["slug"],
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"title": seg_def["title"],
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"type": "intro-segment",
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"book": "nod",
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"order": seg_def["order"],
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"paragraphs": paragraphs,
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}
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intro_segments.append(seg)
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# Step 6: 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 "Concluding Words" p27 or nod_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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# Last chapter (51) — content ends at "Concluding Words" p27 heading
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# or at nod_end. We need to find where "Concluding Words" starts.
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# Look for p27 containing "Concluding Words" after the last chapter bookmark.
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concluding_pos = None
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for i in range(ch_pos + 1, nod_end):
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p = all_paragraphs[i]
|
||||
cls = get_css_class(p)
|
||||
if cls == CHAPTER_TITLE_CLASS:
|
||||
text = decode_text(p)
|
||||
if "Concluding Words" in text:
|
||||
concluding_pos = i
|
||||
break
|
||||
|
||||
if concluding_pos:
|
||||
content_end = concluding_pos
|
||||
else:
|
||||
content_end = nod_end
|
||||
|
||||
# For the PART header: if a PART header appears right before this
|
||||
# chapter's bookmark, include it in this chapter's content range.
|
||||
# The PART header might be 1-3 paragraphs before the bookmark (with
|
||||
# possible p15 empty lines in between).
|
||||
actual_start = content_start
|
||||
for pp in sorted_part_positions:
|
||||
# Check if this PART header is between the previous section end and
|
||||
# this chapter's bookmark
|
||||
if pp < ch_pos and pp >= content_start - 5:
|
||||
# Check that there's no other bookmark between pp and ch_pos
|
||||
has_other_bookmark = False
|
||||
for bpos, _ in bookmarks:
|
||||
if bpos > pp and bpos < ch_pos:
|
||||
has_other_bookmark = True
|
||||
break
|
||||
if not has_other_bookmark:
|
||||
actual_start = pp
|
||||
|
||||
paragraphs = extract_paragraphs(all_paragraphs, actual_start, content_end)
|
||||
|
||||
ch = {
|
||||
"book": "nod",
|
||||
"chapter": ch_num,
|
||||
"title": ch_title,
|
||||
"type": "chapter",
|
||||
"paragraphs": paragraphs,
|
||||
}
|
||||
chapters.append(ch)
|
||||
|
||||
# Step 7: Extract "Concluding Words"
|
||||
concluding_words = None
|
||||
# Find the "Concluding Words" p27 heading after chapter 51
|
||||
last_ch_pos = chapter_bookmarks[-1][0] if chapter_bookmarks else nod_start
|
||||
for i in range(last_ch_pos, nod_end):
|
||||
p = all_paragraphs[i]
|
||||
cls = get_css_class(p)
|
||||
if cls == CHAPTER_TITLE_CLASS:
|
||||
text = decode_text(p)
|
||||
if "Concluding Words" in text:
|
||||
# Extract from this heading to nod_end
|
||||
paragraphs = extract_paragraphs(all_paragraphs, i, nod_end)
|
||||
concluding_words = {
|
||||
"book": "nod",
|
||||
"chapter": None,
|
||||
"title": "Concluding Words",
|
||||
"type": "conclusion",
|
||||
"slug": "concluding-words",
|
||||
"paragraphs": paragraphs,
|
||||
}
|
||||
break
|
||||
|
||||
# Step 8: 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,
|
||||
"parts": parts_data,
|
||||
"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", "nod")
|
||||
|
||||
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 The Nectar of Devotion (1970 Edition)...\n")
|
||||
result = extract_nod(soup)
|
||||
|
||||
intro_segments = result["intro_segments"]
|
||||
chapters = result["chapters"]
|
||||
concluding_words = result["concluding_words"]
|
||||
parts = result["parts"]
|
||||
chapter_listing = result["chapter_listing"]
|
||||
|
||||
# --- Write intro.json ---
|
||||
intro_meta = {
|
||||
"book": "nod",
|
||||
"title": "The Nectar of Devotion",
|
||||
"subtitle": "A Summary Study of Śrīla Rūpa Gosvāmī's Bhakti-rasāmṛta-sindhu",
|
||||
"author": "His Divine Grace A.C. Bhaktivedanta Swami Prabhupāda",
|
||||
"year": 1970,
|
||||
"description": "The complete science of bhakti-yoga, or devotional service to Lord Kṛṣṇa.",
|
||||
"totalChapters": 51,
|
||||
"parts": parts,
|
||||
"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)
|
||||
part_info = ""
|
||||
for p in parts:
|
||||
if ch["chapter"] in p["chapters"]:
|
||||
part_info = f" [Part {p['number']}]"
|
||||
break
|
||||
print(
|
||||
f" Ch {ch['chapter']:>2}: {len(ch['paragraphs']):>3} paragraphs — "
|
||||
f"{ch['title']}{part_info}"
|
||||
)
|
||||
|
||||
# --- 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)}")
|
||||
print(f" Chapters: {len(chapters)}")
|
||||
print(f" Concluding Words: {'Yes' if concluding_words else 'No'}")
|
||||
print(f" Parts: {len(parts)}")
|
||||
for p in parts:
|
||||
print(
|
||||
f" Part {p['number']} ({p['title']}): chapters {p['chapters'][0]}-{p['chapters'][-1]} ({len(p['chapters'])} chapters)"
|
||||
)
|
||||
print(f" Total paragraphs: {total_paras}")
|
||||
print(f" Intro: {total_intro_paras}")
|
||||
print(f" Chapters: {total_chapter_paras}")
|
||||
print(f" Concluding: {total_concluding_paras}")
|
||||
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)[:100]
|
||||
print(f" [{i}] {preview}...")
|
||||
|
||||
print(f"\n--- Sample: Chapter {chapters[-1]['chapter']} (last) ---")
|
||||
if chapters:
|
||||
last = chapters[-1]
|
||||
for i, p in enumerate(last["paragraphs"][:3]):
|
||||
preview = re.sub(r"<[^>]+>", "", p)[:100]
|
||||
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)[:100]
|
||||
print(f" [{i}] {preview}...")
|
||||
|
||||
# --- Quality Checks ---
|
||||
print(f"\n--- Quality Checks ---")
|
||||
|
||||
# Check all 51 chapters present
|
||||
expected_chapters = set(range(1, 52))
|
||||
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 51 chapters present")
|
||||
|
||||
# Check all 3 intro segments present
|
||||
expected_intros = {"front-flap", "preface", "introduction"}
|
||||
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 "Thus ends" markers in chapters
|
||||
chapters_with_end = 0
|
||||
for ch in chapters:
|
||||
has_end = any("Thus end" in p for p in ch["paragraphs"])
|
||||
if has_end:
|
||||
chapters_with_end += 1
|
||||
print(f" ℹ {chapters_with_end}/{len(chapters)} chapters have 'Thus ends' markers")
|
||||
|
||||
# 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 that should have been decoded
|
||||
# Note: ñ is excluded because it's a valid IAST output character (ï→ñ mapping)
|
||||
encoded_chars = set("äåöëòçüéàùè")
|
||||
suspect_count = 0
|
||||
for ch in chapters:
|
||||
for p in ch["paragraphs"]:
|
||||
# Only flag if the encoded chars appear outside HTML tags
|
||||
plain = re.sub(r"<[^>]+>", "", p)
|
||||
for c in encoded_chars:
|
||||
if c in plain:
|
||||
# Many of these are legitimate uses (e.g., German loanwords)
|
||||
# but in this context they almost always indicate ScaGoudy encoding
|
||||
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 part assignments
|
||||
all_part_chapters = set()
|
||||
for p in parts:
|
||||
all_part_chapters.update(p["chapters"])
|
||||
unassigned = [
|
||||
ch["chapter"] for ch in chapters if ch["chapter"] not in all_part_chapters
|
||||
]
|
||||
if unassigned:
|
||||
print(f" ⚠ Chapters not assigned to any part: {unassigned}")
|
||||
else:
|
||||
print(f" ✅ All chapters assigned to parts")
|
||||
|
||||
# Verify ScaGoudy decoding with a spot check on known text
|
||||
print(f"\n--- ScaGoudy Decoding Spot Check ---")
|
||||
# Chapter 1 should contain "Kṛṣṇa" (encoded as "Kåñëa")
|
||||
if chapters:
|
||||
ch1_text = " ".join(chapters[0]["paragraphs"][:5])
|
||||
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 5 paragraphs (may be normal)"
|
||||
)
|
||||
|
||||
# Check for "Śrīmad-Bhāgavatam" (encoded as "Çrémad-Bhägavatam")
|
||||
if "Śrīmad-Bhāgavatam" in ch1_text:
|
||||
print(f" ✅ 'Śrīmad-Bhāgavatam' correctly decoded")
|
||||
elif "Çrémad-Bhägavatam" in ch1_text:
|
||||
print(f" ⚠ ScaGoudy decoding incomplete for 'Çrémad-Bhägavatam'")
|
||||
|
||||
print(f"\nDone! ✨")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user