#!/usr/bin/env python3
"""Extract The Nectar of Devotion (1970 Edition) from other.html.

The book has 3 intro segments (Front Flap, Preface, Introduction),
51 chapters organized in 4 parts, and a Concluding Words section.

Output:
    lib/jsons/books/nod/intro.json          — Book metadata
    lib/jsons/books/nod/intro/*.json         — Intro segments
    lib/jsons/books/nod/chapters/*.json      — Chapters + concluding words

Usage:
    python3 scripts/nod_extract.py
"""

import json
import os
import re
import sys

from bs4 import BeautifulSoup, Tag

# ---------------------------------------------------------------------------
# ScaGoudy → IAST Unicode mapping
# ---------------------------------------------------------------------------
SCAGOUDY_TO_IAST = {
    "ä": "ā",
    "Ä": "Ā",
    "å": "ṛ",
    "Å": "Ṛ",
    "ñ": "ṣ",
    "Ñ": "Ṣ",
    "ö": "ṭ",
    "Ö": "Ṭ",
    "ë": "ṇ",
    "Ë": "Ṇ",
    "ò": "ḍ",
    "Ò": "Ḍ",
    "ç": "ś",
    "Ç": "Ś",
    "ì": "ṅ",
    "ï": "ñ",
    "ü": "ū",
    "Ü": "Ū",
    "é": "ī",
    "É": "Ī",
    "à": "ṁ",
    "ù": "ḥ",
    "è": "ṝ",
    "û": "ṁl",
    "ÿ": "ḷ",
}


def decode_scagoudy(text: str) -> str:
    """Replace ScaGoudy-encoded characters with proper IAST Unicode."""
    for src, dst in SCAGOUDY_TO_IAST.items():
        text = text.replace(src, dst)
    return text


def decode_html_content(tag: Tag) -> str:
    """Extract inner HTML from a tag, decode ScaGoudy, preserve formatting tags."""
    inner = "".join(str(child) for child in tag.children)
    inner = decode_scagoudy(inner)
    # Remove span tags with CSS classes (RTF conversion artifacts)
    inner = re.sub(
        r'<span[^>]*class="[^"]*"[^>]*>(.*?)</span>', r"\1", inner, flags=re.DOTALL
    )
    # Clean up Apple-tab-span leftovers (tab characters)
    inner = inner.replace("\t", " ")
    # Normalize whitespace
    inner = re.sub(r"\s+", " ", inner).strip()
    return inner


def decode_text(tag: Tag) -> str:
    """Get plain text from a tag with ScaGoudy decoding."""
    return decode_scagoudy(tag.get_text().strip())


def get_css_class(tag: Tag) -> str:
    """Get the first CSS class of a tag, or empty string."""
    classes = tag.get("class", [])
    return classes[0] if classes else ""


def is_empty(tag: Tag) -> bool:
    """Check if a tag is empty or whitespace-only."""
    text = tag.get_text().strip()
    return not text or text == "\xa0"


# ---------------------------------------------------------------------------
# CSS class categorization for NoD
# ---------------------------------------------------------------------------

# Bookmark class — used for section boundary detection
BOOKMARK_CLASS = "p13"

# Book title header class
BOOK_HEADER_CLASS = "p12"

# Body paragraph classes
BODY_CLASSES = frozenset(["p6", "p35", "p30"])

# Section heading (bold) — "Chapter One", "Preface", etc.
SECTION_HEADING_CLASS = "p31"

# Chapter title (bold) — "Characteristics of Pure Devotional Service", etc.
CHAPTER_TITLE_CLASS = "p27"

# Part header class — "PART ONE", "PART TWO", etc.
PART_HEADER_CLASS = "p34"

# Sub-section heading (bold) within chapters
SUB_HEADING_CLASS = "p36"

# Italic sub-headings
ITALIC_SUB_HEADING_CLASSES = frozenset(["p37", "p38"])

# Title page / copyright text classes (in intro area)
TITLE_PAGE_CLASS = "p3"
COPYRIGHT_CLASS = "p32"

# Sanskrit verse (italic with br tags)
SANSKRIT_VERSE_CLASS = "p33"

# Classes to skip entirely (empty/decorative)
SKIP_CLASSES = frozenset(["p9", "p15"])


# ---------------------------------------------------------------------------
# Intro segment definitions
# ---------------------------------------------------------------------------

INTRO_SEGMENTS = [
    {
        "slug": "front-flap",
        "title": "Front Flap",
        "order": 1,
        "bookmark_pattern": "NoD-1970: Front Flap",
    },
    {
        "slug": "preface",
        "title": "Preface",
        "order": 2,
        "bookmark_pattern": "NoD-1970: Preface",
    },
    {
        "slug": "introduction",
        "title": "Introduction",
        "order": 3,
        "bookmark_pattern": "NoD-1970: Introduction",
    },
]


# ---------------------------------------------------------------------------
# Extraction helpers
# ---------------------------------------------------------------------------


def find_nod_boundaries(all_paragraphs: list) -> tuple[int, int]:
    """Find the start and end paragraph indices of the NoD content.

    Start: <p class="p12"> containing "Nectar of Devotion"
    End: next <p class="p12"> after start, or end of file
    """
    start_idx = None
    end_idx = len(all_paragraphs)

    for i, p in enumerate(all_paragraphs):
        cls = get_css_class(p)
        if cls != BOOK_HEADER_CLASS:
            continue
        text = decode_text(p)
        if "Nectar of Devotion" in text:
            start_idx = i
        elif start_idx is not None:
            # Next p12 after NoD start = end of NoD
            end_idx = i
            break

    if start_idx is None:
        print("ERROR: Could not find 'The Nectar of Devotion' header in other.html!")
        sys.exit(1)

    return start_idx, end_idx


def find_all_bookmarks(all_paragraphs: list, start_idx: int, end_idx: int) -> list:
    """Find all p13 bookmark tags in the NoD range.

    Returns list of (paragraph_index, bookmark_text).
    """
    bookmarks = []
    for i in range(start_idx, end_idx):
        p = all_paragraphs[i]
        cls = get_css_class(p)
        if cls == BOOKMARK_CLASS:
            text = decode_text(p)
            if "NoD" in text:
                bookmarks.append((i, text))
    return bookmarks


def parse_chapter_bookmark(text: str) -> tuple[int, str] | None:
    """Parse a chapter bookmark like 'NoD 1-1970: Characteristics of Pure Devotional Service'.

    Returns (chapter_number, title) or None if not a chapter bookmark.
    """
    m = re.match(r"NoD\s+(\d+)-1970:\s*(.+)", text.strip())
    if m:
        return int(m.group(1)), m.group(2).strip()
    return None


def parse_intro_bookmark(text: str) -> str | None:
    """Parse an intro bookmark like 'NoD-1970: Preface'.

    Returns the segment name or None if not an intro bookmark.
    """
    m = re.match(r"NoD-1970:\s*(.+)", text.strip())
    if m:
        return m.group(1).strip()
    return None


def extract_paragraphs(
    all_paragraphs: list,
    start_idx: int,
    end_idx: int,
    is_intro: bool = False,
) -> list[str]:
    """Extract paragraphs for a section (chapter or intro segment).

    Handles all CSS classes within the NoD content.
    """
    result: list[str] = []

    for i in range(start_idx, end_idx):
        p = all_paragraphs[i]
        cls = get_css_class(p)

        # Skip empty paragraphs
        if is_empty(p):
            continue

        # Skip noise classes
        if cls in SKIP_CLASSES:
            continue

        # Skip bookmark tags
        if cls == BOOKMARK_CLASS:
            continue

        # Skip book title header
        if cls == BOOK_HEADER_CLASS:
            continue

        # Part headers — include as bold paragraph
        if cls == PART_HEADER_CLASS:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Section heading (p31) — include as paragraph
        if cls == SECTION_HEADING_CLASS:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Chapter title (p27) — include as bold paragraph
        if cls == CHAPTER_TITLE_CLASS:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Sub-section headings within chapters (p36, bold)
        if cls == SUB_HEADING_CLASS:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Italic sub-headings (p37, p38)
        if cls in ITALIC_SUB_HEADING_CLASSES:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Title page / copyright text (in intro area)
        if cls == TITLE_PAGE_CLASS:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Copyright text
        if cls == COPYRIGHT_CLASS:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Sanskrit verse (italic with br tags) — format as centered italic div
        # Strip all inner <i>/<\/i> tags before re-wrapping to avoid nesting
        if cls == SANSKRIT_VERSE_CLASS:
            html = decode_html_content(p)
            if html:
                stripped = re.sub(r"</?i>", "", html)
                result.append(f'<div style="text-align:center"><i>{stripped}</i></div>')
            continue

        # Body paragraphs (p6, p35, p30)
        if cls in BODY_CLASSES:
            html = decode_html_content(p)
            if html:
                result.append(html)
            continue

        # Any other class — try to extract content (be permissive)
        html = decode_html_content(p)
        if html:
            result.append(html)

    return result


# ---------------------------------------------------------------------------
# Main extraction logic
# ---------------------------------------------------------------------------


def extract_nod(soup: BeautifulSoup) -> dict:
    """Extract the entire Nectar of Devotion from the parsed HTML.

    Returns a dict with:
        "intro_segments": list of intro segment dicts
        "chapters": list of chapter dicts
        "concluding_words": concluding words dict
        "parts": list of part assignment dicts
        "chapter_listing": list of {number, title} for intro.json
    """
    all_paragraphs = list(soup.find_all("p"))

    # Step 1: Find NoD boundaries
    nod_start, nod_end = find_nod_boundaries(all_paragraphs)
    print(f"  NoD content: paragraphs {nod_start} to {nod_end}")
    print(f"  Total paragraphs in range: {nod_end - nod_start}")

    # Step 2: Find all bookmarks
    bookmarks = find_all_bookmarks(all_paragraphs, nod_start, nod_end)
    print(f"  Found {len(bookmarks)} NoD bookmarks")

    # Step 3: Categorize bookmarks into intro vs chapter
    intro_bookmarks = []  # (para_idx, segment_name)
    chapter_bookmarks = []  # (para_idx, chapter_num, title)

    for pos, text in bookmarks:
        ch = parse_chapter_bookmark(text)
        if ch:
            chapter_bookmarks.append((pos, ch[0], ch[1]))
        else:
            intro_name = parse_intro_bookmark(text)
            if intro_name:
                intro_bookmarks.append((pos, intro_name))

    print(f"  Intro bookmarks: {len(intro_bookmarks)}")
    print(f"  Chapter bookmarks: {len(chapter_bookmarks)}")

    # Step 4: Track PART assignments by scanning for p34 tags
    # We need to know which chapters belong to which part.
    part_assignments = {}  # chapter_num -> part_num
    parts_data = []  # list of {number, title, chapters}

    # Walk through all paragraphs in NoD range and track parts
    # We process this before extracting chapters so we know part assignments
    part_positions = []  # (para_idx, part_title)
    for i in range(nod_start, nod_end):
        p = all_paragraphs[i]
        cls = get_css_class(p)
        if cls == PART_HEADER_CLASS:
            text = decode_text(p)
            if text:
                part_positions.append((i, text))

    print(f"  Part headers found: {len(part_positions)}")
    for pos, title in part_positions:
        print(f"    Line ~{pos}: {title}")

    # Assign parts to chapters based on position
    # A chapter belongs to the most recent PART header before its bookmark
    part_number_map = {}
    for pos, title in part_positions:
        # Extract part number from title like "PART ONE", "PART TWO", etc.
        part_num = None
        title_upper = title.upper()
        if "ONE" in title_upper or "I" == title_upper.replace("PART", "").strip():
            part_num = 1
        elif "TWO" in title_upper or "II" == title_upper.replace("PART", "").strip():
            part_num = 2
        elif "THREE" in title_upper or "III" == title_upper.replace("PART", "").strip():
            part_num = 3
        elif "FOUR" in title_upper or "IV" == title_upper.replace("PART", "").strip():
            part_num = 4
        if part_num:
            part_number_map[pos] = part_num

    # Now assign each chapter to a part
    sorted_part_positions = sorted(part_number_map.keys())
    for ch_pos, ch_num, ch_title in chapter_bookmarks:
        # Find the most recent PART header before this chapter's bookmark
        assigned_part = None
        for pp in sorted_part_positions:
            if pp < ch_pos:
                assigned_part = part_number_map[pp]
            else:
                break
        if assigned_part:
            part_assignments[ch_num] = assigned_part

    # Build parts_data
    for part_num in sorted(set(part_assignments.values())):
        chs = sorted([ch for ch, pn in part_assignments.items() if pn == part_num])
        part_title = f"Part {'One' if part_num == 1 else 'Two' if part_num == 2 else 'Three' if part_num == 3 else 'Four'}"
        parts_data.append(
            {
                "number": part_num,
                "title": part_title,
                "chapters": chs,
            }
        )

    # Step 5: Extract intro segments
    intro_segments = []
    for seg_def in INTRO_SEGMENTS:
        # Find matching bookmark
        bookmark_pos = None
        for pos, name in intro_bookmarks:
            if (
                seg_def["bookmark_pattern"].endswith(name)
                or name == seg_def["bookmark_pattern"].split(": ", 1)[-1]
            ):
                bookmark_pos = pos
                break

        if bookmark_pos is None:
            print(f"  WARNING: Could not find bookmark for '{seg_def['slug']}'")
            continue

        # Content starts after the bookmark
        content_start = bookmark_pos + 1

        # Content ends at the next bookmark (intro or chapter) or PART header
        content_end = nod_end
        all_section_starts = sorted(
            [pos for pos, _ in intro_bookmarks]
            + [pos for pos, _, _ in chapter_bookmarks]
        )
        for s in all_section_starts:
            if s > bookmark_pos:
                content_end = s
                break

        paragraphs = extract_paragraphs(
            all_paragraphs, content_start, content_end, is_intro=True
        )

        seg = {
            "slug": seg_def["slug"],
            "title": seg_def["title"],
            "type": "intro-segment",
            "book": "nod",
            "order": seg_def["order"],
            "paragraphs": paragraphs,
        }
        intro_segments.append(seg)

    # Step 6: Extract chapters
    chapters = []
    for idx, (ch_pos, ch_num, ch_title) in enumerate(chapter_bookmarks):
        content_start = ch_pos + 1

        # Content ends at next chapter bookmark or "Concluding Words" p27 or nod_end
        if idx + 1 < len(chapter_bookmarks):
            content_end = chapter_bookmarks[idx + 1][0]
        else:
            # Last chapter (51) — content ends at "Concluding Words" p27 heading
            # or at nod_end. We need to find where "Concluding Words" starts.
            # Look for p27 containing "Concluding Words" after the last chapter bookmark.
            concluding_pos = None
            for i in range(ch_pos + 1, 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:
                        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()