"""
GLM-OCR Hugging Face Space app for PDF/image OCR with header inclusion
and table-structure stabilization for downstream bank-statement pipelines.
Hard-coded knobs (no environment variables required).
Primary goals for reconcile rate:
- preserve right-most columns (often "Balance") by higher DPI render + right padding
- keep tables as tables (convert markdown pipe tables -> HTML table)
- return ---page-separator--- between pages
- normalize HTML tables generically so downstream parsing/classification is stable:
  1) expand colspan/rowspan into a rectangular grid
  2) drop truly-empty columns (common in summary tables)
  3) merge "blank header" columns that contain text into the left column (common when DESCRIPTION is split)
  4) clean common header artifacts (e.g. "DESCRIPTIONBeginning Balance", "BALANCE$3,447.10")
  5) recover fused first data row generically (two-signal guard: text fusion + money fusion must both fire)
  6) promote misplaced header row: when real column headers land in a data row, restructure the grid
  7) fix fused key-value rows in summary sections (e.g. Interest Summary) appended to transaction tables
  8) reconstruct mid-table separator rows split across columns by OCR (e.g. 'Card account # XXXX 2 | | 889')
- footer extraction enabled on all pages with deduplication guard (no double-print if body OCR already captured it)
- Fix 4: cross-validate OCR table row counts against PDF text layer; inject missing duplicate rows
         (safe no-op for scanned PDFs and non-transaction pages); after injection, re-run normalize_html_tables
         + subset dedupe so UCB-style fused cells from the text layer get Fix 13/14
- Fix NF-1: extract Navy Federal "Summary of your deposit accounts" table from PDF text layer
            using spatial word positions (GLM-OCR fails on this wide-column layout)
- Fix 13: collapse Date|Description|Amount|Description|Amount tables to 3 columns; fix fused
            Beginning/Ending balance rows (UCB-style wide summary tables)
- Fix 14: UCB 3-col tables where Beginning Balance amount is fused into description and Amount is $0.00
- Fix 15: UCB (and similar) 3-col rows where Date+Description are fused in col1 and Amount sits in col2 with col3 empty
- Fix 16: remove duplicate rows within a single Date|Description|Amount table; multi-pass table dedupe + final doc-wide dedupe
- Fix 17: UCB page 3 — split one merged table under Deposits (continued) into Deposits + Electronic Credits +
            Electronic Debits (PDF text order); drop orphan empty EC/ED headers that followed the merged table
- Post-pass: dedupe 3-col Date|Description|Amount tables when one is a duplicate fragment
            (later subset of earlier, earlier subset of later, or identical row sets)
- Fix 18: adjacent DA3 tables — if the last K data rows of table N match the first K rows of table N+1
            (same normalized Date|Description|Amount keys), strip that suffix from table N. Fixes
            formats (e.g. Truist) where deposit rows are glued onto the withdrawals table and repeated
            under the proper deposits heading (K>=2; first table must keep >=1 data row).
- Fix 19: DA3 rows whose Amount cell is not strict currency (e.g. fused card digits + merchant tail)
            — take the rightmost plausible money token from Description+Amount text for the amount.
- Fix 20: DA3 in-table dedupe — cap consecutive identical normalized keys at 2 rows (keeps legitimate
            back-to-back identical charges; still collapses 3+ OCR stutters).
- Fix 21: Cross-table DA3 repair for MSBILL / MICROSOFT#G… PIN rows — when Amount is junk in one table
            but another table repeats the same authorization id with a strict amount, copy that amount
            (then subset dedupe can drop the stray duplicate block, e.g. under Deposits).
- Fix 22: Drop a single OCR “flat line” between pages: (continued) + DESCRIPTION + AMOUNT + many MM/DD
            tokens (noise before a proper <table>).
- Fix 23: Doc-wide DA3 table dedupe using multiset of (date, amount) keys — catches duplicate sections when
            OCR varies description text between copies (e.g. withdrawals repeated under Deposits).
- Fix 24: Run orphan flatline strip on merged PDF output and after text-layer patch so page-boundary
            noise is removed consistently.
- Fix 25: After a centered “deposits + credits/interest” heading, if two DA3 tables appear back-to-back
            and the first is mostly credit/deposit rows while the second is mostly debit/withdrawal-style
            rows (generic keywords), drop the second — fixes OCR pasting the withdrawals block again.
- Fix 23b: Loose multiset keys use normalized float amounts so comma/spacing variants still match.
"""

# Patch asyncio first (before Gradio imports it) to suppress Python 3.13 cleanup noise
import asyncio
try:
    _orig_close = asyncio.BaseEventLoop.close
    def _safe_close(self):
        try:
            _orig_close(self)
        except (ValueError, OSError):
            pass
    asyncio.BaseEventLoop.close = _safe_close
except Exception:
    pass

import logging
import os
import re
import html
import tempfile
from typing import List, Tuple
from collections import Counter, defaultdict
from html.parser import HTMLParser

import yaml
import gradio as gr
import glmocr

log = logging.getLogger("glmocr_app")
logging.basicConfig(level=logging.INFO)

GLMOCR_BASE = os.path.dirname(glmocr.__file__)
CONFIG_PATH = os.path.join(GLMOCR_BASE, "config.yaml")
FORMATTER_PATH = os.path.join(GLMOCR_BASE, "postprocess", "result_formatter.py")

# ============================================================
# HARD-CODED SETTINGS (edit these numbers to tune quality/speed)
# ============================================================

GLMOCR_API_KEY = "1960cf47d08547d2b4d03544143cce98.AAxia6FudQwLdUDb"

# Higher = better OCR for small/right-aligned digits; slower
RENDER_SCALE = 2.2  # try 2.5 if right-side numbers are missed

# Padding to protect columns near edges (Balance is often right-most)
PAD_LEFT_FRAC = 0.02
PAD_RIGHT_FRAC = 0.06  # try 0.10 if right-most balances are missing
PAD_TOP_FRAC = 0.01
PAD_BOTTOM_FRAC = 0.01

ENABLE_CONTRAST = True

DEFAULT_ZONE_FRAC = 0.12
PDF_HEADER_BAND_FRAC = 0.10

ENABLE_FOOTER_OCR = True        # enabled — 3-step fallback with dedup guard
PDF_FOOTER_BAND_FRAC = 0.88

MIN_CROP_HEIGHT = 112
MIN_CROP_PIXELS = 112 * 112

# ============================================================

_parser = None

def get_parser():
    global _parser
    if _parser is None:
        from glmocr import GlmOcr
        _parser = GlmOcr(api_key=GLMOCR_API_KEY, mode="maas")
    return _parser

# ---------------------------------------------------------------------------
# Best-effort config tweaks (safe to fail on read-only HF env)
# ---------------------------------------------------------------------------
try:
    with open(CONFIG_PATH, "r") as f:
        config = yaml.safe_load(f)
    config["pipeline"]["maas"]["enabled"] = True
    config["pipeline"]["maas"]["api_key"] = GLMOCR_API_KEY
    with open(CONFIG_PATH, "w") as f:
        yaml.dump(config, f, default_flow_style=False, sort_keys=False)
except Exception:
    pass

# Best-effort formatter tweak: avoid stripping header/footer labels
try:
    with open(FORMATTER_PATH, "r") as f:
        source = f.read()
    for label in (
        '"header"', "'header'",
        '"footer"', "'footer'",
        '"doc_header"', "'doc_header'",
        '"doc_footer"', "'doc_footer'",
    ):
        source = re.sub(r",\s*" + re.escape(label), "", source)
        source = re.sub(re.escape(label) + r"\s*,", "", source)
        source = re.sub(re.escape(label), "", source)
    with open(FORMATTER_PATH, "w") as f:
        f.write(source)
except Exception:
    pass

# --------------------------
# Header/footer helpers
# --------------------------
def get_header_footer_zones(regions, norm_height=1000):
    if not regions:
        return None, None
    y_tops, y_bottoms = [], []
    for r in regions:
        bbox = r.get("bbox_2d") if isinstance(r, dict) else getattr(r, "bbox_2d", None)
        if bbox and len(bbox) >= 4:
            y_tops.append(bbox[1])
            y_bottoms.append(bbox[3])
    if not y_tops:
        return None, None
    return min(y_tops) / norm_height, max(y_bottoms) / norm_height

def extract_zone_text_pdf(pdf_path, page_num, y_start_frac, y_end_frac):
    try:
        import pymupdf as fitz
        doc = fitz.open(pdf_path)
        page = doc[page_num]
        h, w = page.rect.height, page.rect.width
        rect = fitz.Rect(0, h * y_start_frac, w, h * y_end_frac)
        text = page.get_text(clip=rect).strip()
        doc.close()
        return text
    except Exception:
        return ""

def extract_pdf_text_in_band(pdf_path, page_num, y_start_frac, y_end_frac):
    try:
        import pymupdf as fitz
        doc = fitz.open(pdf_path)
        page = doc[page_num]
        h = page.rect.height
        y_lo = h * y_start_frac
        y_hi = h * y_end_frac
        words = page.get_text("words")
        doc.close()
        parts = []
        for w in words:
            if len(w) >= 5:
                y0, y1 = float(w[1]), float(w[3])
                if y0 < y_hi and y1 > y_lo:
                    parts.append(w[4])
        return " ".join(parts).strip()
    except Exception:
        return ""

def ocr_zone(image_path, y_start_frac, y_end_frac):
    zone_name = "header" if y_end_frac < 0.5 else "footer"
    try:
        from PIL import Image
        img = Image.open(image_path).convert("RGB")
        w, h = img.size
        y0 = max(0, int(h * y_start_frac))
        y1 = min(h, int(h * y_end_frac))
        if y1 <= y0:
            return ""

        crop = img.crop((0, y0, w, y1))
        cw, ch = crop.size

        if ch < MIN_CROP_HEIGHT or (cw * ch) < MIN_CROP_PIXELS:
            need_h = max(ch, MIN_CROP_HEIGHT)
            need_w = max(cw, 1)
            if (need_w * need_h) < MIN_CROP_PIXELS:
                need_w = max(need_w, (MIN_CROP_PIXELS + need_h - 1) // need_h)
            canvas = Image.new("RGB", (need_w, need_h), (255, 255, 255))
            if zone_name == "header":
                canvas.paste(crop, (0, 0))
            else:
                canvas.paste(crop, (0, need_h - ch))
            crop = canvas

        fd, path = tempfile.mkstemp(suffix=".jpg")
        os.close(fd)
        try:
            crop.save(path, "JPEG", quality=92)
            parser = get_parser()
            out = parser.parse(path)
            if not isinstance(out, list):
                out = [out]
            if out and getattr(out[0], "markdown_result", None):
                return (out[0].markdown_result or "").strip()
        finally:
            try:
                os.unlink(path)
            except Exception:
                pass
    except Exception as e:
        log.warning("[%s] ocr_zone failed: %s", zone_name, e, exc_info=True)
    return ""

def fix_account_number(hdr: str) -> str:
    if not hdr:
        return hdr
    if "Account Number:" in hdr and "Account Number: " not in hdr:
        m = re.search(r"[0-9]{5,}", hdr)
        if m:
            hdr = hdr.replace("Account Number:", "Account Number: " + m.group(0))
    acct_match = re.search(r"Account Number: ([0-9]{5,})", hdr)
    if acct_match:
        acct = acct_match.group(1)
        if hdr.startswith(acct):
            hdr = hdr[len(acct):].lstrip()
    return hdr

# --------------------------
# Table stabilization helpers
# --------------------------
def close_unclosed_html(md: str) -> str:
    if not md:
        return md
    open_tags = re.findall(r"<(table|tbody|thead|tr|td|th)\b", md, flags=re.IGNORECASE)
    close_tags = re.findall(r"</(table|tbody|thead|tr|td|th)>", md, flags=re.IGNORECASE)

    def count(tags, name):
        return sum(1 for t in tags if t.lower() == name)

    for tag in reversed(["td", "th", "tr", "thead", "tbody", "table"]):
        opened = count(open_tags, tag)
        closed = count(close_tags, tag)
        if opened > closed:
            md += ("</%s>" % tag) * (opened - closed)
    return md

def looks_like_markdown_table(block: str) -> bool:
    lines = [ln.rstrip() for ln in block.strip().splitlines() if ln.strip()]
    if len(lines) < 2:
        return False
    if "|" not in lines[0]:
        return False
    sep = lines[1].replace(" ", "")
    return ("---" in sep) and ("|" in sep)

def md_table_to_html(block: str) -> str:
    lines = [ln.strip() for ln in block.strip().splitlines() if ln.strip()]
    if len(lines) < 2:
        return block

    def split_row(row: str):
        row = row.strip()
        if row.startswith("|"):
            row = row[1:]
        if row.endswith("|"):
            row = row[:-1]
        return [p.strip() for p in row.split("|")]

    header = split_row(lines[0])
    body_lines = [ln for ln in lines[2:] if "|" in ln]

    html_rows = []
    html_rows.append("<tr>" + "".join(f"<th>{html.escape(c)}</th>" for c in header) + "</tr>")
    for ln in body_lines:
        cols = split_row(ln)
        if len(cols) < len(header):
            cols += [""] * (len(header) - len(cols))
        html_rows.append("<tr>" + "".join(f"<td>{html.escape(c)}</td>" for c in cols[: len(header)]) + "</tr>")
    return "<table>\n" + "\n".join(html_rows) + "\n</table>"

def normalize_money_glyphs(text: str) -> str:
    if not text:
        return text
    t = text.replace("−", "-").replace("–", "-").replace("—", "-")
    t = re.sub(
        r"\(\s*\$?\s*([0-9]{1,3}(?:,[0-9]{3})*|[0-9]+)(\.[0-9]{2})\s*\)",
        r"-\1\2",
        t,
    )

    def o_to_zero(m):
        token = m.group(0)
        return token.replace("O", "0").replace("o", "0")

    t = re.sub(r"\b[0-9Oo\$,.\-]{4,}\b", o_to_zero, t)
    return t

# ---- Generic HTML table normalizer ----
class TableGridParser(HTMLParser):
    """Parse a <table> into rows of (text, colspan, rowspan)."""

    def __init__(self):
        super().__init__()
        self.rows = []
        self._current_row = []
        self._cell_text = []
        self._colspan = 1
        self._rowspan = 1
        self._in_cell = False

    def handle_starttag(self, tag, attrs):
        if tag == "tr":
            self._current_row = []
        elif tag in ("td", "th"):
            attrs_d = dict(attrs)
            self._colspan = max(1, int(attrs_d.get("colspan", 1)))
            self._rowspan = max(1, int(attrs_d.get("rowspan", 1)))
            self._cell_text = []
            self._in_cell = True

    def handle_endtag(self, tag):
        if tag in ("td", "th"):
            text = "".join(self._cell_text).strip().replace("\n", " ")
            self._current_row.append((text, self._colspan, self._rowspan))
            self._in_cell = False
        elif tag == "tr":
            self.rows.append(self._current_row)

    def handle_data(self, data):
        if self._in_cell:
            self._cell_text.append(data)

def _build_grid(rows_data):
    if not rows_data:
        return []
    blocked = defaultdict(set)
    grid = []

    for r, row_cells in enumerate(rows_data):
        grid.append([])
        col = 0
        for content, C, R in row_cells:
            while col in blocked[r]:
                grid[r].append("")
                col += 1
            for k in range(C):
                grid[r].append(content if k == 0 else "")
            for k in range(1, R):
                blocked[r + k].add(col)
            col += C

    max_cols = max(len(row) for row in grid) if grid else 0
    for row in grid:
        while len(row) < max_cols:
            row.append("")
    return grid

def _grid_to_html(grid):
    if not grid:
        return ""
    lines = ["<table>"]
    for r, row in enumerate(grid):
        lines.append("<tr>")
        tag = "th" if r == 0 else "td"
        for cell in row:
            escaped = (
                (cell or "")
                .replace("&", "&amp;")
                .replace("<", "&lt;")
                .replace(">", "&gt;")
                .replace('"', "&quot;")
            )
            lines.append(f"<{tag}>{escaped}</{tag}>")
        lines.append("</tr>")
    lines.append("</table>")
    return "\n".join(lines)

def _is_amount_like(s: str) -> bool:
    if not s:
        return False
    return re.fullmatch(r"\$?-?\d{1,3}(?:,\d{3})*(?:\.\d{2})?", s.strip()) is not None

def _drop_truly_empty_columns(grid):
    """
    Drop columns that are empty in header AND almost always empty in body.
    This fixes summary tables that have an extra blank trailing column.
    """
    if not grid or len(grid) < 1:
        return grid

    header = [str(c or "").strip() for c in grid[0]]
    ncols = len(header)
    if ncols <= 1:
        return grid

    body = grid[1:]
    keep = [True] * ncols

    for i in range(ncols):
        if header[i] != "":
            continue
        total = 0
        non_empty = 0
        for row in body:
            if i >= len(row):
                continue
            total += 1
            if str(row[i] or "").strip():
                non_empty += 1
        if total > 0 and (non_empty / total) <= 0.05:
            keep[i] = False

    if all(keep):
        return grid

    new_grid = []
    for row in grid:
        new_grid.append([cell for idx, cell in enumerate(row) if idx < len(keep) and keep[idx]])
    return new_grid

def _merge_blank_header_text_columns(grid):
    """
    If the header row has blank columns, and most body rows have non-empty *text* in that
    blank column, merge that column into the nearest non-empty header to the left (usually
    DESCRIPTION), then remove the blank column.
    This fixes transaction tables where DESCRIPTION is split across two columns.
    """
    if not grid or len(grid) < 2:
        return grid

    header = [str(c or "").strip() for c in grid[0]]
    ncols = len(header)
    body = grid[1:]
    blank_cols = [i for i, h in enumerate(header) if h == ""]
    if not blank_cols:
        return grid

    keep = [True] * ncols

    for i in blank_cols:
        j = i - 1
        while j >= 0 and header[j] == "":
            j -= 1
        if j < 0:
            continue

        total = 0
        non_empty = 0
        texty = 0
        for row in body:
            if i >= len(row) or j >= len(row):
                continue
            v = str(row[i] or "").strip()
            total += 1
            if v:
                non_empty += 1
                if not _is_amount_like(v):
                    texty += 1

        if total == 0:
            continue
        non_empty_ratio = non_empty / total
        texty_ratio = (texty / non_empty) if non_empty else 0.0

        if non_empty_ratio >= 0.55 and texty_ratio >= 0.70:
            for r in range(1, len(grid)):
                row = grid[r]
                if i >= len(row) or j >= len(row):
                    continue
                left = str(row[j] or "").strip()
                right = str(row[i] or "").strip()
                if right:
                    row[j] = (left + " " + right).strip() if left else right
            keep[i] = False

    if all(keep):
        return grid

    new_grid = []
    for row in grid:
        new_grid.append([cell for idx, cell in enumerate(row) if idx < len(keep) and keep[idx]])
    return new_grid

# --------------------------
# Shared keyword definitions
# --------------------------

# Matches a standalone money value across currencies.
# Covers: $3,447.10  -1,234.56  £500.00  Rs1000  etc.
_MONEY_RE = re.compile(
    r"^(?:[\$£€¥]|Rs\.?|INR|PKR)?\s*-?\s?\d{1,3}(?:,\d{3})*(?:\.\d{1,4})?$",
    re.IGNORECASE,
)

# All column-header keywords recognised across any bank statement format.
_HEADER_KW_PATTERNS = [
    # Date variants
    r"DATE",
    r"POSTING\s+DATE",
    r"VALUE\s+DATE",
    r"TXN\s+DATE",
    r"TRANSACTION\s+DATE",
    r"ENTRY\s+DATE",
    r"EFFECTIVE\s+DATE",
    # Transaction ID / reference
    r"TRANSACTION(?:\s+(?:ID|TYPE|NO|NUMBER))?",
    r"TXN(?:\s+(?:ID|NO|TYPE))?",
    r"REF(?:ERENCE)?(?:\s*(?:NO|NUM|NUMBER))?",
    r"CHEQUE(?:\s*(?:NO|NUMBER))?",
    r"CHQ(?:\s*(?:NO|NUMBER))?",
    r"VOUCHER(?:\s*(?:NO|NUMBER))?",
    r"SR\.?\s*NO\.?",
    r"SERIAL(?:\s*(?:NO|NUMBER))?",
    # Description variants
    r"DESCRIPTION",
    r"DETAILS?",
    r"PARTICULARS?(?:\s+OF\s+TRANSACTION)?",
    r"NARRATION",
    r"REMARKS?",
    r"NOTES?",
    # Debit variants
    r"DEBIT",
    r"DEBITS?",
    r"DR\.?",
    r"WITHDRAWALS?",
    r"PAID\s+OUT",
    r"MONEY\s+OUT",
    # Credit variants
    r"CREDIT",
    r"CREDITS?",
    r"CR\.?",
    r"DEPOSITS?",
    r"PAID\s+IN",
    r"MONEY\s+IN",
    # Amount
    r"AMOUNT",
    # Balance variants
    r"BALANCE",
    r"BAL\.?",
    r"RUNNING\s+BALANCE",
    r"RUNNING\s+BAL\.?",
    r"AVAILABLE\s+BALANCE",
    r"AVAILABLE\s+BAL\.?",
    r"AVAIL\.?\s+BAL\.?",
    r"LEDGER\s+BALANCE",
    r"LEDGER\s+BAL\.?",
    r"CLOSING\s+BALANCE",
    r"CLOSING\s+BAL\.?",
    r"OPENING\s+BALANCE",
    r"OPENING\s+BAL\.?",
]

# Pre-compiled: each pattern anchored at start, case-insensitive
_HEADER_KW_RES = [
    re.compile(r"^(" + p + r")(.*)", re.IGNORECASE | re.DOTALL)
    for p in _HEADER_KW_PATTERNS
]

# Quick exact-match check: "is this entire string a known keyword?"
_HEADER_KW_EXACT_RE = re.compile(
    r"^(?:" + r"|".join(_HEADER_KW_PATTERNS) + r")$",
    re.IGNORECASE,
)

# Minimum fraction of cells in a row that must be pure keywords for that row
# to be considered a misplaced header row.
_HEADER_ROW_KEYWORD_THRESHOLD = 0.5

# --------------------------
# Fix 1: Fused-header recovery
# --------------------------

def _split_keyword_remainder(cell_text: str):
    """
    If cell_text starts with a known header keyword followed by extra content,
    return (keyword, remainder).  Otherwise return (cell_text, "").
    """
    s = cell_text.strip()
    for pattern in _HEADER_KW_RES:
        m = pattern.match(s)
        if m:
            keyword = m.group(1).strip()
            remainder = m.group(2).strip()
            return keyword, remainder
    return s, ""

def _extract_fused_header_artifacts(header_row):
    """
    Generic detector for the OCR artifact where the first data row of a table
    gets fused into the header cells during OCR.
    The artifact pattern (BOTH signals must fire simultaneously):
      - Signal 1 — text-fused: a header cell contains KEYWORD + free descriptive text
        e.g.  "DESCRIPTIONBeginning Balance"  "NARRATIONOpening Balance"
      - Signal 2 — money-fused: a DIFFERENT header cell contains KEYWORD + money amount
        e.g.  "BALANCE$3,447.10"  "AMOUNT 5,000.00"
    Two-signal guard prevents false positives on clean PDFs from any bank.
    Returns:
        (cleaned_header : list[str], recovered_row : list[str] | None)
    """
    if not header_row:
        return list(header_row), None

    ncols = len(header_row)
    cleaned = list(header_row)
    recovered = [""] * ncols

    text_fused = []
    money_fused = []

    for idx, cell in enumerate(header_row):
        cell_s = str(cell or "").strip()
        if not cell_s:
            continue

        keyword, remainder = _split_keyword_remainder(cell_s)

        if not remainder:
            continue

        remainder_no_space = remainder.replace(" ", "")

        if _MONEY_RE.match(remainder_no_space):
            cleaned[idx] = keyword
            recovered[idx] = remainder
            money_fused.append(idx)

        elif not _HEADER_KW_EXACT_RE.match(remainder.split()[0] if remainder.split() else ""):
            cleaned[idx] = keyword
            recovered[idx] = remainder
            text_fused.append(idx)

    if text_fused and money_fused:
        return cleaned, recovered

    return list(header_row), None

def _clean_header_artifacts(grid):
    """
    Entry point for Fix 1 called from normalize_html_tables.
    Returns (grid, recovered_row | None).
    """
    if not grid or not grid[0]:
        return grid, None

    cleaned_header, recovered_row = _extract_fused_header_artifacts(grid[0])
    grid[0] = cleaned_header
    return grid, recovered_row

# --------------------------
# Fix 2: Misplaced header row promotion
# --------------------------

def _row_keyword_score(row):
    """
    Return the fraction of non-empty cells in this row that are pure header
    keywords (e.g. DATE, DESCRIPTION, DEBIT, CREDIT, BALANCE).
    Also handles cells like "DATE DESCRIPTION" where two keywords are
    space-joined into one cell — these count as a keyword cell too.
    """
    non_empty = [str(c or "").strip() for c in row if str(c or "").strip()]
    if not non_empty:
        return 0.0

    keyword_hits = 0
    for cell in non_empty:
        if _HEADER_KW_EXACT_RE.match(cell):
            keyword_hits += 1
            continue
        parts = cell.split()
        if all(_HEADER_KW_EXACT_RE.match(p) for p in parts) and len(parts) > 1:
            keyword_hits += 1
            continue

    return keyword_hits / len(non_empty)

# Pattern: header cell contains balance/account info — do NOT promote away from it.
# Matches things like "Beginning Balance:", "Ending Balance:", account numbers, "$20.48$3.46"
_BALANCE_INFO_RE = re.compile(
    r"(beginning|ending|opening|closing)\s+balance"
    r"|account\s*#?\s*\d{5,}"
    r"|\b\d{7,}\b"
    r"|\$\d{1,3}(?:,\d{3})*\.\d{2}",
    re.IGNORECASE,
)

def _header_contains_balance_info(header_row):
    """
    Return True if any cell in the header row contains balance or account
    information — indicating this IS a legitimate header row even if its
    keyword score is low (e.g. Citi's "208479667 | Beginning Balance:$20.48 | ...").
    """
    for cell in header_row:
        if _BALANCE_INFO_RE.search(str(cell or "")):
            return True
    return False

def _promote_misplaced_header_row(grid):
    """
    Detects and fixes the OCR artifact where real column headers land in a
    <td> data row instead of the <th> header row.
    Two scenarios handled:
    Scenario A — Simple misplaced header (e.g. TD Bank):
      <th>ACCOUNT ACTIVITY</th>        ← section title, not a real header
      <td>DATE DESCRIPTION</td> ...    ← real column headers in a data row
      <td>01/05 ...</td>               ← data
    → Promote the keyword row to header, discard the section title rows above.
    Scenario B — Metadata header + misplaced column headers (e.g. Citi page 1):
      <th>208479667</th> <th>Beginning Balance:$20.48 Ending Balance:$3.46</th>
      <td>Date Description</td> <td>Debits</td> <td>Credits</td> <td>Balance</td>
      <td>04/01 DEBIT CARD... 8.01</td> ...
    → The existing th row has balance/account metadata (NOT a real column header).
    → Promote the keyword data row to header.
    → Preserve metadata row cells as a plain-text prefix OUTSIDE the table
      by embedding them as a leading data row with colspan (kept for reference).
    → Split any "Date Description" fused cells in data rows.
    Guard conditions (no-op if not met — safe for all other PDFs):
      - Current header keyword score < threshold.
      - A data row within the first 5 rows has keyword score >= threshold.
    """
    if not grid or len(grid) < 2:
        return grid

    current_header_score = _row_keyword_score(grid[0])
    if current_header_score >= _HEADER_ROW_KEYWORD_THRESHOLD:
        return grid

    # Search the first few data rows for a candidate keyword header row.
    candidate_idx = None
    candidate_score = 0.0
    for i in range(1, min(len(grid), 6)):
        score = _row_keyword_score(grid[i])
        if score >= _HEADER_ROW_KEYWORD_THRESHOLD and score > candidate_score:
            candidate_score = score
            candidate_idx = i

    if candidate_idx is None:
        return grid

    candidate_row = grid[candidate_idx]

    # Expand any merged keyword cells (e.g. "DATE DESCRIPTION" → ["DATE", "DESCRIPTION"])
    expanded_header = []
    for cell in candidate_row:
        cell_s = str(cell or "").strip()
        parts = cell_s.split()
        if len(parts) > 1 and all(_HEADER_KW_EXACT_RE.match(p) for p in parts):
            expanded_header.extend(parts)
        else:
            expanded_header.append(cell_s)

    new_ncols = len(expanded_header)
    col_expansion = new_ncols - len(candidate_row)

    # If the existing header row contains balance/account metadata (Scenario B),
    # preserve it as the first data row so the information is not lost.
    # This row will have its content in col 0 (joined) and blanks elsewhere.
    metadata_row = None
    if _header_contains_balance_info(grid[0]):
        meta_cells = [str(c or "").strip() for c in grid[0]]
        meta_text = " ".join(c for c in meta_cells if c).strip()
        if meta_text:
            metadata_row = [meta_text] + [""] * (new_ncols - 1)

    # Re-align data rows below the candidate header
    new_data_rows = []
    for row in grid[candidate_idx + 1:]:
        if col_expansion > 0 and row:
            first_cell = str(row[0] or "").strip()
            date_match = re.match(r"^(\d{1,2}/\d{1,2})\s+(.*)", first_cell, re.DOTALL)
            if date_match and col_expansion == 1:
                new_row = [date_match.group(1), date_match.group(2).strip()] + list(row[1:])
            else:
                new_row = list(row) + [""] * col_expansion
            new_data_rows.append(new_row)
        else:
            new_data_rows.append(list(row))

    def pad(row, n):
        r = list(row)
        while len(r) < n:
            r.append("")
        return r[:n]

    new_grid = [pad(expanded_header, new_ncols)]
    # Insert metadata row first if present (preserves Beginning/Ending Balance)
    if metadata_row is not None:
        new_grid.append(pad(metadata_row, new_ncols))
    for row in new_data_rows:
        new_grid.append(pad(row, new_ncols))

    return new_grid

# --------------------------
# Fix 3: Fused key-value rows in summary sections
# --------------------------

# Matches a value suffix fused onto a label with no space.
# e.g. "Beginning Interest Rate0.00%" → label + "0.00%"
#      "Number of days in this Period31" → label + "31"
# Strict: no \s* between groups so label trailing-space detects space-separated values
_FUSED_KV_RE = re.compile(
    r"^(.+?)(-?\d{1,3}(?:,\d{3})*(?:\.\d+)?%?)$",
    re.DOTALL,
)

def _fix_fused_keyvalue_rows(grid):
    """
    Fix rows where label+value are fused into the first cell with all other
    cells empty — common in Interest Summary / Fee Summary sections appended
    to a transaction table by OCR.
    e.g. "Beginning Interest Rate0.00%"  → label="Beginning Interest Rate"  value="0.00%"
         "Number of days in this Period31" → label="..."  value="31"
    Guard conditions (no-op if not met — safe for all other tables):
      - All cells except first must be empty.
      - A non-space character must immediately precede the digit run (fused).
      - Both label and value parts must be non-empty after splitting.
      - The cell must NOT contain an account number (7+ digit run) — prevents
        incorrectly splitting "STREAMLINED CHECKING #208479667" or
        "Charges debited from account #208479667".
      - The cell must NOT contain a proper dollar amount with decimal — prevents
        incorrectly splitting metadata like "Beginning Balance:$20.48$3.46".
    """
    if not grid or len(grid) < 2:
        return grid

    # Matches a proper dollar/currency amount with decimal point — these appear
    # in legitimate label cells and must not trigger the fused-KV split.
    _DOLLAR_AMOUNT_RE = re.compile(r"\$\s*\d{1,3}(?:,\d{3})*\.\d{2}")
    # Matches a long account/reference number (7+ consecutive digits)
    _LONG_NUMBER_RE = re.compile(r"\d{7,}")

    ncols = len(grid[0])
    new_grid = [grid[0]]

    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        if not cells:
            new_grid.append(row)
            continue

        first = cells[0]
        rest_empty = all(c == "" for c in cells[1:])

        if not rest_empty or not first:
            new_grid.append(row)
            continue

        # Guard: skip rows containing a proper dollar amount with decimal
        # (these are metadata/balance rows, not fused key-value summary rows)
        if _DOLLAR_AMOUNT_RE.search(first):
            new_grid.append(row)
            continue

        # Guard: skip rows containing a long account/reference number (7+ digits)
        # (e.g. "STREAMLINED CHECKING #208479667", "account #208479667")
        if _LONG_NUMBER_RE.search(first):
            new_grid.append(row)
            continue

        # Guard: skip rows containing a # followed by digits or X-patterns
        # (card/account number labels like "Card account # XXXX XXXX XXXX 2889")
        # These are separator rows reconstructed by Fix5, not fused KV rows.
        if re.search(r"#\s*[\dX]", first):
            new_grid.append(row)
            continue

        m = _FUSED_KV_RE.match(first)
        if not m:
            new_grid.append(row)
            continue

        label_raw = m.group(1)   # trailing space means value was space-separated, not fused
        value = m.group(2).strip()

        # Space before value means NOT fused — "Interest Rate 0.00%" is clean
        if label_raw != label_raw.rstrip():
            new_grid.append(row)
            continue

        label = label_raw.strip()

        if not label or not value:
            new_grid.append(row)
            continue

        new_row = [""] * ncols
        new_row[0] = label
        new_row[ncols - 1] = value
        new_grid.append(new_row)

    return new_grid

# --------------------------
# Fix 4: PDF text-layer row-count patch
# --------------------------

def _extract_textlayer_rows(pdf_path: str, page_num: int):
    """
    Extract structured transaction rows from the PDF text layer using pymupdf.
    Returns list of {"date": str, "desc": str, "amount": str} dicts, or []
    if the PDF has no text layer, pymupdf is unavailable, or fewer than 2
    transaction rows are found (guards against non-transaction pages).
    Supports multiple date formats:
      - Numeric:    MM/DD, MM/DD/YY, MM/DD/YYYY  (Chase, TD, BoA, Citi)
      - Month-name: Jan 16, Feb 7, Mar 05         (Capital One, Amex)
      - ISO:        YYYY-MM-DD
    """
    try:
        import pymupdf as fitz

        doc = fitz.open(pdf_path)
        page = doc[page_num]
        words = page.get_text("words")
        doc.close()

        if not words:
            return []

        # Group words by y-bucket (5pt tolerance)
        lines_by_y = {}
        for w in words:
            x0, y0, word = float(w[0]), float(w[1]), w[4]
            bucket = None
            for existing_y in lines_by_y:
                if abs(existing_y - y0) <= 5:
                    bucket = existing_y
                    break
            if bucket is None:
                bucket = y0
            lines_by_y.setdefault(bucket, []).append((x0, word))

        sorted_lines = []
        for y in sorted(lines_by_y):
            line_words = sorted(lines_by_y[y], key=lambda t: t[0])
            sorted_lines.append([w for _, w in line_words])

        # Date patterns — numeric (MM/DD, MM-DD variants) or month-name (Jan 16)
        _MONTHS = r"(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)"
        date_re = re.compile(
            r"^\d{1,2}/\d{2}(?:/\d{2,4})?$"          # MM/DD, MM/DD/YY, MM/DD/YYYY
            r"|^\d{1,2}-\d{2}(?:-\d{2,4})?$"           # MM-DD, MM-DD-YY (East West Bank)
            r"|^\d{4}-\d{2}-\d{2}$"                    # YYYY-MM-DD
            r"|^" + _MONTHS + r"$",                        # "Jan", "Feb" etc (month-name date part 1)
            re.IGNORECASE,
        )
        # When a month-name date appears, the next token is the day number
        month_re = re.compile(r"^" + _MONTHS + r"$", re.IGNORECASE)
        day_re   = re.compile(r"^\d{1,2}$")

        # Amount: -1,234.56  $193.63  -$240.46  - $500.00 (with space after -)
        amount_re = re.compile(
            r"^-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{1,4})?$"
            r"|^\$-?\d{1,3}(?:,\d{3})*(?:\.\d{1,4})?$"
            r"|^-\s+\$\d{1,3}(?:,\d{3})*(?:\.\d{1,4})?$"  # "- $500.00"
        )

        rows = []
        for line in sorted_lines:
            if len(line) < 2:
                continue

            # Detect date and find where description starts
            date_str = None
            desc_start = None

            if date_re.match(line[0]):
                if month_re.match(line[0]) and len(line) > 1 and day_re.match(line[1]):
                    # Month-name date: "Jan 16" → two tokens
                    date_str = line[0] + " " + line[1]
                    desc_start = 2
                else:
                    # Single-token numeric date
                    date_str = line[0]
                    desc_start = 1
            else:
                continue

            if desc_start is None or desc_start >= len(line):
                continue

            # For Capital One: Trans Date and Post Date are both present
            # Line looks like: Jan 16 Jan 16 CAPITAL ONE MOBILE PYMT - $500.00
            # After consuming first date, check if next tokens are also a date
            remaining = line[desc_start:]
            # Capture post_date if next tokens are also a date
            post_date_str = ""
            if remaining and month_re.match(remaining[0]):
                if len(remaining) > 1 and day_re.match(remaining[1]):
                    post_date_str = remaining[0] + " " + remaining[1]
                    remaining = remaining[2:]
                elif len(remaining) > 0:
                    remaining = remaining[1:]
            elif remaining and date_re.match(remaining[0]) and not month_re.match(remaining[0]):
                post_date_str = remaining[0]
                remaining = remaining[1:]

            if len(remaining) < 2:
                continue

            # Handle "- $500.00" split as two tokens at end
            if (len(remaining) >= 2
                    and remaining[-2] == "-"
                    and remaining[-1].startswith("$")):
                amount_candidate = remaining[-2] + " " + remaining[-1]
                desc_tokens = remaining[:-2]
            else:
                amount_candidate = remaining[-1]
                desc_tokens = remaining[:-1]

            if not amount_re.match(amount_candidate):
                continue

            desc = " ".join(desc_tokens).strip()
            if not desc:
                continue

            # Guard: description must contain at least one letter.
            # Rows where description is purely digits/dates/amounts are
            # DAILY BALANCE rows (e.g. "170,198.04 01-13 45,442.31 01-24"),
            # not real transactions — skip them.
            if not re.search(r"[A-Za-z]", desc):
                continue

            rows.append({"date": date_str, "post_date": post_date_str, "desc": desc, "amount": amount_candidate})

        # Guard: require at least 2 rows to avoid false positives on non-transaction pages
        return rows if len(rows) >= 2 else []

    except Exception as e:
        log.debug("_extract_textlayer_rows failed (page %d): %s", page_num, e)
        return []

def _jb_extract_checks_fallback(text_layer: str) -> List[Tuple[str, str, str]]:
    """
    Johnson Bank: when pdfminer column extraction fails (e.g. pymupdf line text or
    multi-column layout), extract (date, check#, amount) triplets from the Checks
    subsection. Anchored by 'Checks (' ... 'Daily Account Balance' only.
    """
    if not text_layer:
        return []
    m = re.search(
        r"Checks\s*\([^\n]*\n(.*?)(?=^Daily\s+Account\s+Balance\s*$)",
        text_layer,
        re.DOTALL | re.MULTILINE | re.IGNORECASE,
    )
    if not m:
        return []
    chunk = m.group(1)
    chunk = re.sub(
        r"^\s*Date\s+Number\s+Amount\s*$",
        "",
        chunk,
        flags=re.MULTILINE | re.IGNORECASE,
    )
    _triplet = re.compile(
        r"(\d{2}-\d{2})\s+(\*?\d+)\s+(\d{1,3}(?:,\d{3})*\.\d{2})"
    )
    out: List[Tuple[str, str, str]] = []
    seen = set()
    for t in _triplet.findall(chunk):
        if t not in seen:
            seen.add(t)
            out.append(t)
    return out

def _extract_jb_summary_sections(text_layer: str, needs_checks: bool = True, needs_dab: bool = True) -> str:
    """
    Parse Johnson Bank Checks and Daily Account Balance from pdfminer column-separated
    page text and return as HTML tables.
    pdfminer outputs multi-column sections as separate column lists:
        Date      Number    Amount
        04-14     3259      45.72
    becomes:
        "Date\n04-14\n04-09\n\nNumber\n3259\n3260\n\nAmount\n45.72\n1,628.00"
    Uses pdfminer (always available) not pymupdf.
    """
    if not text_layer:
        return ""

    _DATE_RE  = re.compile(r"^\d{2}-\d{2}$")
    _MONEY_RE = re.compile(r"^\d{1,3}(?:,\d{3})*\.\d{2}$")
    _CHECK_RE = re.compile(r"^\*?\d+$")

    lines = [l.strip() for l in text_layer.splitlines()]
    output = []

    # ── Checks ───────────────────────────────────────────────────────────
    if needs_checks:
        checks = []
        i = 0
        while i < len(lines):
            if lines[i].lower().startswith("checks"):
                i += 1
                while i < len(lines):
                    if "daily account balance" in lines[i].lower():
                        break
                    if lines[i] == "Date":
                        i += 1
                        dates = []
                        while i < len(lines) and _DATE_RE.match(lines[i]):
                            dates.append(lines[i]); i += 1
                        while i < len(lines) and lines[i] != "Number":
                            i += 1
                        i += 1
                        numbers = []
                        while i < len(lines):
                            if _CHECK_RE.match(lines[i]):
                                numbers.append(lines[i]); i += 1
                            elif lines[i] == "":
                                i += 1
                            else:
                                break
                        while i < len(lines) and lines[i] != "Amount":
                            i += 1
                        i += 1
                        amounts = []
                        while i < len(lines):
                            if _MONEY_RE.match(lines[i]):
                                amounts.append(lines[i]); i += 1
                            elif lines[i] == "":
                                i += 1
                            else:
                                break
                        for d, n, a in zip(dates, numbers, amounts):
                            checks.append((d, n, a))
                    else:
                        i += 1
                break
            i += 1

        if not checks and re.search(r"\bChecks\s*\(", text_layer, re.I):
            checks = _jb_extract_checks_fallback(text_layer)

        if checks:
            rows = "\n".join(
                "<tr><td>" + d + "</td><td>" + n + "</td><td>" + a + "</td></tr>"
                for d, n, a in checks
            )
            output.append("Checks")
            output.append(
                '<table class="jb-summary-checks">\n<tr><th>Date</th><th>Number</th><th>Amount</th></tr>\n'
                + rows + "\n</table>"
            )

    # ── Daily Account Balance ─────────────────────────────────────────────
    if needs_dab:
        balances = []
        i = 0
        while i < len(lines):
            if "daily account balance" in lines[i].lower():
                i += 1
                while i < len(lines):
                    if lines[i] == "Date":
                        i += 1
                        dates = []
                        while i < len(lines) and _DATE_RE.match(lines[i]):
                            dates.append(lines[i]); i += 1
                        while i < len(lines) and lines[i] != "Balance":
                            i += 1
                        i += 1
                        bals = []
                        while i < len(lines):
                            if _MONEY_RE.match(lines[i]):
                                bals.append(lines[i]); i += 1
                            elif lines[i] == "":
                                i += 1
                            else:
                                break
                        for d, b in zip(dates, bals):
                            balances.append((d, b))
                    else:
                        i += 1
                break
            i += 1

        if balances:
            rows = "\n".join(
                "<tr><td>" + d + "</td><td>" + b + "</td></tr>"
                for d, b in balances
            )
            output.append("Daily Account Balance")
            output.append(
                "<table>\n<tr><th>Date</th><th>Balance</th></tr>\n"
                + rows + "\n</table>"
            )

    return "\n\n".join(output)

def _patch_ocr_with_textlayer(page_md: str, pdf_path: str, page_num: int) -> str:
    """
    Compare OCR table rows against PDF text-layer rows and inject any rows
    the OCR model silently dropped (typically identical consecutive rows).
    Guard conditions (all must pass for any injection):
      1. PDF text layer must exist and yield >= 2 transaction rows.
      2. The OCR table must have DATE + DESCRIPTION + AMOUNT columns.
      3. At least one (date, desc, amount) key must appear in both OCR and
         text layer — ensures we are patching the right table.
      4. Only injects copies of rows ALREADY present in OCR (ocr_count > 0)
         — never invents new row content.
      5. Entire function wrapped in try/except — any error returns page_md unchanged.
    """
    if not page_md or "<table" not in page_md.lower():
        return page_md

    try:
        tl_rows = _extract_textlayer_rows(pdf_path, page_num)
        if not tl_rows:
            return page_md  # scanned PDF or non-transaction page — safe no-op

        def _norm(s):
            return re.sub(r"\s+", " ", str(s or "").strip().lower())

        # Build text-layer frequency map
        tl_freq = {}
        for r in tl_rows:
            key = (_norm(r["date"]), _norm(r["desc"]), _norm(r["amount"]))
            tl_freq[key] = tl_freq.get(key, 0) + 1

        table_pattern = re.compile(r"<table[^>]*>.*?</table>", re.DOTALL | re.IGNORECASE)
        result = page_md

        for tbl_match in table_pattern.finditer(page_md):
            table_html = tbl_match.group(0)

            p = TableGridParser()
            p.feed(table_html)
            grid = _build_grid(p.rows)
            if len(grid) < 2:
                continue

            # Identify DATE, DESCRIPTION, AMOUNT column indices
            header = [str(c or "").strip().upper() for c in grid[0]]
            date_col = desc_col = amt_col = None
            for ci, h in enumerate(header):
                if re.search(r"\bDATE\b", h) and date_col is None:
                    date_col = ci
                elif re.search(r"\b(DESCRIPTION|DETAILS?|NARRATION|PARTICULARS?)\b", h) and desc_col is None:
                    desc_col = ci
                elif re.search(r"\b(AMOUNT|DEBIT|CREDIT|DR|CR)\b", h) and amt_col is None:
                    amt_col = ci

            if date_col is None or desc_col is None or amt_col is None:
                continue  # not a transaction table

            # Build OCR frequency map
            ocr_freq = {}
            ocr_rows_indexed = []
            for ri, row in enumerate(grid[1:], start=1):
                cells = [str(c or "").strip() for c in row]
                if len(cells) <= max(date_col, desc_col, amt_col):
                    continue
                d = _norm(cells[date_col])
                desc = _norm(cells[desc_col])
                amt = _norm(cells[amt_col])
                if not d or not desc:
                    continue
                key = (d, desc, amt)
                ocr_freq[key] = ocr_freq.get(key, 0) + 1
                ocr_rows_indexed.append((ri, key))

            # Guard: overlap check for Case A (duplicate restore).
            # For Case A we require at least one row in both OCR and text layer
            # to confirm we are patching the right table.
            # For Case B (entirely missing rows) we use a lighter structural check:
            # if the OCR table has DATE+DESCRIPTION+AMOUNT columns (already verified)
            # and the text layer date format matches the OCR date format,
            # we can safely inject — even when zero rows overlap.
            overlap = set(ocr_freq.keys()) & set(tl_freq.keys())

            # Detect date format used in OCR table (MM/DD vs MM-DD vs Mon DD)
            _ocr_dates = [
                _norm(str(row[date_col] or ""))
                for row in grid[1:]
                if len(row) > date_col and str(row[date_col] or "").strip()
            ]
            _tl_dates = [_norm(r["date"]) for r in tl_rows]
            _slash_re  = re.compile(r"^\d{1,2}/\d{2}")
            _hyphen_re = re.compile(r"^\d{1,2}-\d{2}")
            def _date_fmt(dates):
                if any(_slash_re.match(d) for d in dates):  return "slash"
                if any(_hyphen_re.match(d) for d in dates): return "hyphen"
                return "other"
            ocr_fmt = _date_fmt(_ocr_dates)
            tl_fmt  = _date_fmt(_tl_dates)
            date_fmt_match = (ocr_fmt == tl_fmt) or "other" in (ocr_fmt, tl_fmt)

            # Determine missing rows.
            # Case A: row exists in OCR but count is too low (duplicate dropped)
            # Case B: row exists in text layer but completely absent from OCR
            to_inject = {}
            to_inject_new = {}

            for key in tl_freq:
                ocr_count = ocr_freq.get(key, 0)
                tl_count = tl_freq[key]
                if tl_count > ocr_count:
                    missing = tl_count - ocr_count
                    if ocr_count > 0:
                        # Case A: restore duplicates — requires overlap confirmation
                        if overlap:
                            to_inject[key] = missing
                    else:
                        # Case B: entirely new row — requires date format match
                        # (lighter guard: no overlap needed, just structural match)
                        if date_fmt_match:
                            tl_row_data = next(
                                (r for r in tl_rows
                                 if (_norm(r["date"]), _norm(r["desc"]), _norm(r["amount"])) == key),
                                None
                            )
                            if tl_row_data:
                                to_inject_new[key] = (missing, tl_row_data)

            if not to_inject and not to_inject_new:
                continue

            # Build patched grid — inject missing duplicate rows (Case A)
            new_grid = [grid[0]]
            for ri, row in enumerate(grid[1:], start=1):
                new_grid.append(row)
                cells = [str(c or "").strip() for c in row]
                if len(cells) <= max(date_col, desc_col, amt_col):
                    continue
                d = _norm(cells[date_col])
                desc = _norm(cells[desc_col])
                amt = _norm(cells[amt_col])
                key = (d, desc, amt)
                if key in to_inject and to_inject[key] > 0:
                    last_occ = max(idx for idx, k in ocr_rows_indexed if k == key)
                    if ri == last_occ:
                        for _ in range(to_inject[key]):
                            new_grid.append(list(row))
                        to_inject[key] = 0

            new_table_html = _grid_to_html(new_grid)
            result = result.replace(table_html, new_table_html, 1)

            # Case B: completely missing rows → build a SEPARATE new table
            # appended after the patched OCR table. Using a separate table
            # preserves the original PDF structure (e.g. Capital One has
            # separate "Payments" and "Transactions" sections).
            if to_inject_new:
                ncols = len(grid[0])
                header_row = grid[0]

                # Find post_date column index if it exists in the header
                post_date_col = None
                for ci, h in enumerate(header_row):
                    hh = str(h or "").strip().upper()
                    if "POST" in hh and "DATE" in hh:
                        post_date_col = ci
                        break

                new_rows = []
                for key, (count, tl_row_data) in to_inject_new.items():
                    for _ in range(count):
                        new_row = [""] * ncols
                        new_row[date_col] = tl_row_data["date"]
                        new_row[desc_col] = tl_row_data["desc"]
                        new_row[amt_col]  = tl_row_data["amount"]
                        if post_date_col is not None:
                            new_row[post_date_col] = tl_row_data.get("post_date", "")
                        new_rows.append(new_row)
                        log.info(
                            "page %d: new table row from text layer: %s %s",
                            page_num, tl_row_data["date"], tl_row_data["desc"][:40]
                        )

                if new_rows:
                    extra_grid = [header_row] + new_rows
                    extra_html = "\n\n" + _grid_to_html(extra_grid)
                    # Insert immediately after the patched table
                    insert_pos = result.find(new_table_html) + len(new_table_html)
                    result = result[:insert_pos] + extra_html + result[insert_pos:]

        # Case C: page has text-layer rows but NO transaction table in OCR output.
        # Build a new table from the text layer and prepend it.
        # Guard: only fires when the page has NO table with both DATE + DESCRIPTION cols.
        # This is placed AFTER the for-loop so it only runs once per page, not per table.
        if tl_rows:
            _has_txn_table = False
            for _tbl in table_pattern.finditer(result):
                _p2 = TableGridParser()
                _p2.feed(_tbl.group(0))
                _g2 = _build_grid(_p2.rows)
                if len(_g2) < 2:
                    continue
                _h2 = [str(c or "").strip().upper() for c in _g2[0]]
                if (any(re.search(r"\bDATE\b", x) for x in _h2) and
                        any(re.search(r"\b(DESCRIPTION|DETAILS?|NARRATION|PARTICULARS?)\b", x) for x in _h2)):
                    _has_txn_table = True
                    break

            if not _has_txn_table:
                _has_post = any(r.get("post_date") for r in tl_rows)
                if _has_post:
                    _chdr = ["Date", "Post Date", "Transaction Description", "Amount"]
                    _di2, _pi2, _xi2, _ai2 = 0, 1, 2, 3
                else:
                    _chdr = ["Date", "Transaction Description", "Amount"]
                    _di2, _xi2, _ai2 = 0, 1, 2
                _cnew_rows = []
                for r in tl_rows:
                    _crow = [""] * len(_chdr)
                    _crow[_di2] = r["date"]
                    _crow[_xi2] = r["desc"]
                    _crow[_ai2] = r["amount"]
                    if _has_post:
                        _crow[_pi2] = r.get("post_date", "")
                    _cnew_rows.append(_crow)
                result = _grid_to_html([_chdr] + _cnew_rows) + "\n\n" + result
                log.info("page %d: Case C — new table (%d rows) from text layer",
                         page_num, len(_cnew_rows))

        return result

    except Exception as e:
        log.warning("_patch_ocr_with_textlayer failed (page %d): %s", page_num, e)
        return page_md  # always safe — return original on any error

# --------------------------
# Fix 5: Mid-table separator row reconstruction
# --------------------------

# Date pattern for transaction rows: MM/DD, MM/DD/YY, MM/DD/YYYY
_DATE_RE = re.compile(r"^\d{1,2}/\d{2}(?:/\d{2,4})?$")

# Standalone money amount (may be split off a separator label by OCR)
_SPLIT_AMOUNT_RE = re.compile(
    r"^-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{1,4})?$|^\$-?\d{1,3}(?:,\d{3})*(?:\.\d{1,4})?$"
)

def _reconstruct_separator_rows(grid):
    """
    Detect and reconstruct mid-table separator / label rows that OCR has
    incorrectly split across columns.
    The artifact (Bank of America and similar):
      A section label like "Card account # XXXX XXXX XXXX 2889" sits between
      transaction rows as a full-width label. OCR splits the trailing digits
      across columns because they are right-aligned, producing variations like:
        2-cell split:  ["Card account # XXXX XXXX XXXX 2", "",    "889"]
        3-cell split:  ["Card account # XXXX XXXX XXXX",   "2",   "889"]
        clean 1-cell:  ["Card account # XXXX XXXX XXXX 2889", "", ""]
    Detection criteria (ALL must hold — guards safe for all other PDFs):
      1. First cell is NOT a date token (transaction rows always start with date).
      2. First cell contains letters (it is a label, not a bare number).
      3. All cells except the first either:
           a. are empty, OR
           b. are a short pure-digit fragment (1-4 digits, no decimal, no sign)
              — these are the split-off tails of the account/card number.
      4. There must be at least one non-empty cell after the first (to detect
         the split; single-cell rows are also handled as clean label rows).
    The fix:
      Concatenate all non-empty cells in order, place the result in col 0,
      blank all other cells.
    """
    if not grid or len(grid) < 2:
        return grid

    ncols = len(grid[0])
    if ncols < 2:
        return grid

    new_grid = [grid[0]]  # keep header unchanged

    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < ncols:
            cells.append("")

        non_empty = [(i, c) for i, c in enumerate(cells) if c]

        # Completely empty row — leave as-is
        if len(non_empty) == 0:
            new_grid.append(row)
            continue

        first_idx, first_val = non_empty[0]

        # Guard 1: first cell must not be a date token
        if _DATE_RE.match(first_val):
            new_grid.append(row)
            continue

        # Guard 2: first cell must contain letters (label, not a bare number)
        if not re.search(r"[A-Za-z]", first_val):
            new_grid.append(row)
            continue

        # Single non-empty cell — already a clean label row
        if len(non_empty) == 1:
            new_row = [""] * ncols
            new_row[0] = first_val
            new_grid.append(new_row)
            continue

        # Multiple non-empty cells: check that every cell AFTER the first
        # is a short pure-digit fragment (1-4 digits, no decimal, no sign).
        # This is the key generic guard — it allows 2, 3, or more cells
        # as long as all the trailing cells are digit-only fragments.
        # Real transaction rows always have amounts with decimals (-14.19)
        # or descriptions with letters, so they never pass this check.
        trailing = [val for _, val in non_empty[1:]]
        all_trailing_are_digit_fragments = all(
            re.fullmatch(r"\d{1,4}", v) for v in trailing
        )

        if not all_trailing_are_digit_fragments:
            new_grid.append(row)
            continue

        # Reconstruct: concatenate all non-empty cells in order
        reconstructed = "".join(val for _, val in non_empty).strip()
        new_row = [""] * ncols
        new_row[0] = reconstructed
        new_grid.append(new_row)

    return new_grid

# --------------------------
# Normalizer entry point
# --------------------------

def _merge_split_rows(grid):
    """
    Fix OCR artifact where a transaction row is split across two <tr> rows
    because the description wrapped to a second line in the source PDF.
    Two patterns detected (Hardin County Bank and similar monospace statements):
    Pattern A — description + empty continuation:
      Row N:   [desc, '', '', '', '']          ← description, no date/money
      Row N+1: ['', debit, credit, date, bal]  ← money/date, no description
      → Merge into: [desc, debit, credit, date, bal]
    Pattern B — description + text continuation + money:
      Row N:   [desc, '', '', '', '']               ← first line of description
      Row N+1: [desc_cont, debit, credit, date, bal] ← continuation + money
      → Merge into: [desc + ' ' + desc_cont, debit, credit, date, bal]
    Guard conditions (no-op unless both rows match the pattern):
      - Row N must have non-empty col 0 (description).
      - Row N must have empty date column (col 3 or wherever DATE is).
      - Row N must have empty debit AND credit columns.
      - Row N+1 must have non-empty date column.
      - Row N+1 must have non-empty debit OR credit column.
      - Row N balance (if present) must be a short fragment without a decimal
        (1-6 chars, no '.') — confirms it's a split-off ref number, not a balance.
    """
    if not grid or len(grid) < 3:
        return grid

    ncols = len(grid[0])
    if ncols < 3:
        return grid

    # Identify column indices from header
    header = [str(c or "").strip().upper() for c in grid[0]]
    date_col = desc_col = bal_col = None
    debit_cols = []
    credit_cols = []

    for ci, h in enumerate(header):
        if re.search(r"\bDATE\b", h) and date_col is None:
            date_col = ci
        if re.search(r"\b(DESCRIPTION|DETAILS?|NARRATION|PARTICULARS?|TRANSACTION)\b", h) and desc_col is None:
            desc_col = ci
        if re.search(r"\bBALANCE\b", h) and bal_col is None:
            bal_col = ci
        if re.search(r"\b(DEBIT|DEBITS|DR|WITHDRAWAL|SUBTRACTIONS?)\b", h):
            debit_cols.append(ci)
        if re.search(r"\b(CREDIT|CREDITS|CR|DEPOSIT|ADDITIONS?)\b", h):
            credit_cols.append(ci)

    # Need at least date + description + one money column
    if date_col is None or desc_col is None:
        return grid
    if not debit_cols and not credit_cols:
        return grid

    money_cols = debit_cols + credit_cols
    _MONEY_RE2 = re.compile(r"^-?\$?\d{1,3}(?:,\d{3})*\.\d{1,4}$")
    _DATE_RE2  = re.compile(r"^\d{1,2}/\d{2}(?:/\d{2,4})?$|^\d{1,2}-\d{2}(?:-\d{2,4})?$")
    # Short fragment guard: 1-6 non-space chars, no decimal point → split-off ref tail
    _FRAGMENT_RE = re.compile(r"^[^\s\.]{1,6}$")

    new_grid = [grid[0]]
    i = 1
    while i < len(grid):
        row = [str(c or "").strip() for c in grid[i]]
        while len(row) < ncols:
            row.append("")

        # Check if this row is a "description-only" split row candidate
        has_desc    = bool(row[desc_col])
        no_date     = not row[date_col]
        no_money    = all(not row[c] for c in money_cols)
        bal_is_frag = (bal_col is not None and
                       (_FRAGMENT_RE.match(row[bal_col]) or not row[bal_col]))

        if has_desc and no_date and no_money and bal_is_frag and (i + 1) < len(grid):
            next_row = [str(c or "").strip() for c in grid[i + 1]]
            while len(next_row) < ncols:
                next_row.append("")

            next_has_date  = bool(next_row[date_col]) and _DATE_RE2.match(next_row[date_col])
            next_has_money = any(bool(next_row[c]) for c in money_cols)

            if next_has_date and next_has_money:
                # Pattern A or B — merge
                merged = list(next_row)
                # Append description (and possible continuation from next row)
                if next_row[desc_col]:
                    # Pattern B: next row has desc continuation
                    merged[desc_col] = row[desc_col] + " " + next_row[desc_col]
                else:
                    # Pattern A: next row has no description
                    merged[desc_col] = row[desc_col]
                new_grid.append(merged)
                i += 2  # skip both rows, emit merged
                continue

        new_grid.append(row)
        i += 1

    return new_grid

def _extract_fused_desc_amount(grid):
    """
    Fix OCR artifact where a trailing money amount gets fused into the
    description cell instead of its own debit/credit column.
    Example (Hardin County Bank):
      OCR: ['CHASE CREDIT CRD EPAY 8319249882 500.00', '', '', '04/11/25', '16,699.04']
      Fix: ['CHASE CREDIT CRD EPAY 8319249882', '500.00', '', '04/11/25', '16,699.04']
    Guard conditions (all must hold):
      - Description ends with a space + money amount (NNN.NN or N,NNN.NN).
      - ALL debit AND credit columns are empty for this row.
      - Date column is non-empty (confirms it is a complete data row).
    """
    if not grid or len(grid) < 2:
        return grid

    ncols = len(grid[0])
    header = [str(c or "").strip().upper() for c in grid[0]]

    date_col = desc_col = None
    debit_cols = []
    credit_cols = []
    for ci, h in enumerate(header):
        if re.search(r"\bDATE\b", h) and date_col is None:
            date_col = ci
        if re.search(r"\b(DESCRIPTION|DETAILS?|NARRATION|PARTICULARS?|TRANSACTION)\b", h) and desc_col is None:
            desc_col = ci
        if re.search(r"\b(DEBIT|DEBITS|DR|WITHDRAWAL|SUBTRACTIONS?)\b", h):
            debit_cols.append(ci)
        if re.search(r"\b(CREDIT|CREDITS|CR|DEPOSIT|ADDITIONS?)\b", h):
            credit_cols.append(ci)

    if date_col is None or desc_col is None or not debit_cols:
        return grid

    money_cols = debit_cols + credit_cols
    _TRAILING_AMT = re.compile(r"^(.+?)\s+(-?\d{1,3}(?:,\d{3})*\.\d{2})$")
    debit_col = debit_cols[0]

    new_grid = [grid[0]]
    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < ncols:
            cells.append("")

        desc = cells[desc_col]
        no_money = all(not cells[c] for c in money_cols)
        has_date = bool(cells[date_col])

        if desc and no_money and has_date:
            m = _TRAILING_AMT.match(desc)
            if m:
                cells = list(cells)
                cells[desc_col] = m.group(1).strip()
                cells[debit_col] = m.group(2)
                new_grid.append(cells)
                continue

        new_grid.append(cells)

    return new_grid

def _is_junk_table(grid):
    """
    Detect and suppress known junk tables that are OCR artifacts from
    page headers/footers, not real transaction data.
    Current patterns:
    1. CUSTOMER INFORMATION fragment tables (First Horizon Bank):
       Single-column table with header "CUSTOMER INFORMATION" and
       data rows that are short numeric fragments (e.g. "254", "24")
       from the partial account number / year printed in the header band.
    """
    if not grid or len(grid) < 2:
        return False
    ncols = len(grid[0])
    if ncols != 1:
        return False
    header_text = str(grid[0][0] or "").strip().upper()
    if "CUSTOMER INFORMATION" in header_text:
        # All data rows should be short (< 10 chars) numeric/alphanumeric fragments
        data_rows = grid[1:]
        if all(len(str(r[0] or "").strip()) <= 10 for r in data_rows):
            return True
    return False

def _split_fused_multicolumn_header(grid):
    """
    Fix OCR artifact where multiple column headers are fused into one <th> cell.
    Example (First Horizon Bank alternating pages):
      Fused:   ['DATE DESCRIPTION CARD #', 'DEPOSIT', 'WITHDRAWAL']
      Correct: ['DATE', 'DESCRIPTION', 'DEPOSIT', 'WITHDRAWAL', 'CARD #']
    The data rows also have date + description + card# all in col 0:
      Fused:   ['01/16 PURCHASE - UBER TRIP... 4919', '', '$21.02']
      Correct: ['01/16', 'PURCHASE - UBER TRIP...', '', '$21.02', '4919']
    Detection: first header cell contains 2+ keyword terms separated by spaces,
    AND remaining header cells are valid money column keywords.
    """
    if not grid or len(grid) < 2:
        return grid

    ncols = len(grid[0])
    if ncols < 2:
        return grid

    first_header = str(grid[0][0] or "").strip()
    # Split first header cell into parts and check if multiple are keywords
    parts = first_header.split()
    kw_hits = [p for p in parts if _HEADER_KW_EXACT_RE.match(p)]
    if len(kw_hits) < 2:
        return grid  # not a fused multi-keyword header

    # The remaining header cells must all be keyword columns
    rest_headers = [str(c or "").strip() for c in grid[0][1:]]
    if not all(_HEADER_KW_EXACT_RE.match(h) for h in rest_headers if h):
        return grid

    # Build the new expanded header:
    # Split the fused first cell into individual keyword columns
    # Keep non-keyword trailing parts (e.g. "CARD #") as the last new col
    new_header_cols = []
    non_kw_parts = []
    for p in parts:
        if _HEADER_KW_EXACT_RE.match(p):
            if non_kw_parts:
                new_header_cols.append(" ".join(non_kw_parts))
                non_kw_parts = []
            new_header_cols.append(p)
        else:
            non_kw_parts.append(p)
    if non_kw_parts:
        new_header_cols.append(" ".join(non_kw_parts))

    # Full new header = expanded first cell + remaining cells
    new_header = new_header_cols + rest_headers
    new_ncols = len(new_header)
    extra_cols = new_ncols - ncols  # how many new columns were added

    # Identify column roles in the new header
    _DATE_COL_RE   = re.compile(r"\bDATE\b", re.IGNORECASE)
    _CARD_COL_RE   = re.compile(r"\bCARD\b", re.IGNORECASE)
    _DESC_COL_RE   = re.compile(r"\b(DESCRIPTION|DETAILS?|NARRATION|PARTICULARS?)\b", re.IGNORECASE)
    _MONEY_COL_RE  = re.compile(r"\b(DEPOSIT|WITHDRAWAL|DEBIT|CREDIT|AMOUNT|ADDITION|SUBTRACTION)S?\b", re.IGNORECASE)

    new_date_col = next((i for i,h in enumerate(new_header) if _DATE_COL_RE.search(h)), None)
    new_desc_col = next((i for i,h in enumerate(new_header) if _DESC_COL_RE.search(h)), None)
    new_card_col = next((i for i,h in enumerate(new_header) if _CARD_COL_RE.search(h)), None)

    if new_date_col is None or new_desc_col is None:
        return grid  # can't map columns

    # Re-split each data row: col 0 had "date description... card#" fused together
    _DATE_PREFIX = re.compile(r"^(\d{1,2}/\d{2}(?:/\d{2,4})?)\s+(.*)", re.DOTALL)
    _CARD_SUFFIX = re.compile(r"^(.*?)\s+(\d{4})$", re.DOTALL)  # 4-digit card number at end

    def pad(row, n):
        r = list(row)
        while len(r) < n: r.append("")
        return r[:n]

    new_grid = [pad(new_header, new_ncols)]
    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < ncols: cells.append("")

        fused_cell = cells[0]
        rest_cells = cells[1:]

        # Extract date from front
        date_val = ""
        desc_val = fused_cell
        card_val = ""

        dm = _DATE_PREFIX.match(fused_cell)
        if dm:
            date_val = dm.group(1)
            desc_val = dm.group(2).strip()

        # Extract card# from end (4-digit number)
        if new_card_col is not None:
            cm = _CARD_SUFFIX.match(desc_val)
            if cm:
                desc_val = cm.group(1).strip()
                card_val = cm.group(2)

        # Build new row
        new_row = [""] * new_ncols
        new_row[new_date_col] = date_val
        new_row[new_desc_col] = desc_val
        if new_card_col is not None:
            new_row[new_card_col] = card_val
        # Fill remaining money columns from rest_cells
        money_col_indices = [i for i,h in enumerate(new_header) if _MONEY_COL_RE.search(h)]
        for mi, ri in enumerate(range(len(rest_cells))):
            if mi < len(money_col_indices):
                new_row[money_col_indices[mi]] = rest_cells[ri]

        new_grid.append(new_row)

    return new_grid

def _normalize_daily_balance_table(grid):
    """
    Fix OCR artifact in DAILY BALANCE SUMMARY tables where:
    - OCR produces N DATE columns followed by N BALANCE columns
      instead of interleaved DATE|BALANCE|DATE|BALANCE pairs
    - Multiple dates are stacked in one cell "01/02\n01/03\n01/04\n01/05"
    Reorders columns and expands stacked date cells into proper rows.
    Only fires when ALL of these hold:
    - Header has exactly 2N columns where N >= 2
    - First N columns are all DATE, last N columns are all BALANCE
    - At least one data cell contains a newline (stacked dates)
    """
    if not grid or len(grid) < 2:
        return grid

    header = [str(c or "").strip().upper() for c in grid[0]]
    ncols = len(header)

    if ncols < 4 or ncols % 2 != 0:
        return grid

    half = ncols // 2
    date_half  = header[:half]
    bal_half   = header[half:]

    if not all(h == "DATE" for h in date_half):
        return grid
    if not all(h == "BALANCE" for h in bal_half):
        return grid

    # Build new interleaved header: DATE BALANCE DATE BALANCE ...
    new_header = []
    for i in range(half):
        new_header.append("DATE")
        new_header.append("BALANCE")

    # Expand each data row
    new_rows = []
    _DATE_RE3 = re.compile(r"^\d{1,2}/\d{2}(?:/\d{2,4})?$")

    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < ncols:
            cells.append("")

        # Dates are all stacked in cells[0]; balances follow immediately in cells[1..N]
        # (OCR layout: stacked_dates | bal1 | bal2 | bal3 | bal4 | empty...)
        # NOT: date | date | date | date | bal | bal | bal | bal (despite header order)
        date_cells = [cells[0]]
        bal_cells  = cells[1:]

        # Check if dates are stacked in first cell (newline OR space-separated)
        # e.g. "01/02 01/03 01/04 01/05" or "01/02\n01/03\n01/04\n01/05"
        first = date_cells[0]
        tokens = re.split(r"[\n\r\s]+", first)
        date_tokens = [t.strip() for t in tokens if t.strip() and _DATE_RE3.match(t.strip())]
        if len(date_tokens) >= 2:
            stacked = date_tokens
        else:
            stacked = date_cells  # already one date per cell

        # Build ONE interleaved row for this group of stacked dates
        new_row = [""] * ncols
        for i, date_val in enumerate(stacked):
            if not _DATE_RE3.match(date_val):
                continue
            bal_val = bal_cells[i] if i < len(bal_cells) else ""
            new_row[i * 2]     = date_val
            new_row[i * 2 + 1] = bal_val
        new_rows.append(new_row)

    if not new_rows:
        return grid

    return [new_header] + new_rows

def _normalize_checks_paid_table(grid):
    """
    Fix OCR artifact in CHECKS PAID SUMMARY tables where:
    - Header col0 = "DATE CHECK # DATE CHECK # DATE CHECK #" (fused repeating groups)
    - Remaining headers = "AMOUNT AMOUNT AMOUNT"
    - Data col0 = "01/24 4701 01/18 4704 * 01/18 916831 *" (all groups fused)
    - Remaining data cells = amount values
    Expands to proper: DATE | CHECK # | AMOUNT | DATE | CHECK # | AMOUNT | ...
    Detection: first header cell matches pattern (DATE CHECK #)+ 
    and remaining headers are all AMOUNT.
    """
    if not grid or len(grid) < 2:
        return grid

    ncols = len(grid[0])
    if ncols < 2:
        return grid

    first_h = str(grid[0][0] or "").strip()
    _CHECKS_HDR = re.compile(r"^(DATE\s+CHECK\s+#\s*)+$", re.IGNORECASE)
    if not _CHECKS_HDR.match(first_h):
        return grid

    # Remaining headers must be AMOUNT (or empty)
    rest_h = [str(c or "").strip().upper() for c in grid[0][1:]]
    if not all(h in ("AMOUNT", "") for h in rest_h if h):
        return grid

    # Count groups from how many times DATE appears in the fused header
    n_groups = len(re.findall(r"\bDATE\b", first_h, re.IGNORECASE))
    if n_groups < 1:
        return grid

    # Build new header: DATE | CHECK # | AMOUNT repeated n_groups times
    new_header = []
    for _ in range(n_groups):
        new_header.extend(["DATE", "CHECK #", "AMOUNT"])

    _DATE_RE = re.compile(r"^\d{1,2}/\d{2}$")

    new_rows = [new_header]
    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < ncols:
            cells.append("")

        fused_data = cells[0]
        amounts    = cells[1:]

        # Split fused data into groups by date boundary
        tokens = fused_data.split()
        groups = []
        current = []
        for tok in tokens:
            if _DATE_RE.match(tok) and current:
                groups.append(current)
                current = [tok]
            else:
                current.append(tok)
        if current and current[0]:
            groups.append(current)

        # Build one output row
        new_row = [""] * len(new_header)
        for i, grp in enumerate(groups):
            if i >= n_groups:
                break
            new_row[i * 3]     = grp[0]                       # DATE
            new_row[i * 3 + 1] = " ".join(grp[1:]).strip()    # CHECK #
            new_row[i * 3 + 2] = amounts[i] if i < len(amounts) else ""  # AMOUNT

        new_rows.append(new_row)

    return new_rows

# ── Johnson Bank plain-text section converter ─────────────────────────────
# Johnson Bank formats Deposits, Withdrawals, Checks, and Daily Account Balance
# as plain text columns (no HTML tables). This converter detects those sections
# and emits proper <table> HTML.
#
# SAFETY GUARDS — all four must hold before any conversion fires:
#   1. Line matches exact section-header pattern
#   2. Next non-blank line matches exact column-header pattern
#   3. First data row uses MM-DD date format (Johnson Bank's unique date separator)
#   4. The candidate section contains NO existing <table> tags
# Guard 3 is the key discriminator: all other supported banks use MM/DD.

_JB_SECTION_HDR = re.compile(
    r"^(Deposits?|Withdrawals?|Checks?(?:\s+Paid)?|Daily\s+Account\s+Balance)"
    r"\s*(?:\(cont\.?\))?\s*$",
    re.IGNORECASE,
)
_JB_COL_HDR = re.compile(
    r"^Date\s+(Description|Number)\s+Amount\s*$"
    r"|^Date\s+(?:Number\s+)?(?:Balance|Amount)\s*$",
    re.IGNORECASE,
)
# MM-DD date (dash separator) — Johnson Bank's unique format
_JB_DATE_DD  = re.compile(r"^\d{2}-\d{2}\s+")
_JB_TXN_ROW  = re.compile(r"^(\d{2}-\d{2})\s+(.+?)\s+(-?\d{1,3}(?:,\d{3})*\.\d{2})\s*$")
_JB_BAL_ROW  = re.compile(r"^(\d{2}-\d{2})\s+(\d{1,3}(?:,\d{3})*\.\d{2})\s*$")

def _jb_rows_to_html(header_cols, rows):
    th    = "".join("<th>" + h + "</th>" for h in header_cols)
    parts = ["<table>", "<tr>" + th + "</tr>"]
    for row in rows:
        td = "".join("<td>" + str(row.get(h, "")) + "</td>" for h in header_cols)
        parts.append("<tr>" + td + "</tr>")
    parts.append("</table>")
    return "\n".join(parts)

def _jb_section_has_table(lines, start, end):
    """Return True if any line in lines[start:end] contains a <table tag."""
    return any("<table" in l.lower() for l in lines[start:end])

def _find_first_data_row(lines, start):
    """Return index of first non-blank line after start, or None."""
    for i in range(start, min(start + 20, len(lines))):
        if lines[i].strip():
            return i
    return None

def convert_plaintext_bank_sections(text: str) -> str:
    """
    Convert Johnson Bank plain-text transaction sections to HTML tables.
    All four safety guards must pass before conversion fires.
    Safe no-op for all other banks.
    """
    # Quick exit: if the page already has lots of tables, skip entirely
    # (handles pages that are already properly formatted)
    lines = text.splitlines()
    out   = []
    i     = 0

    while i < len(lines):
        line = lines[i].strip()

        # Guard 1: section header
        if not _JB_SECTION_HDR.match(line):
            out.append(lines[i])
            i += 1
            continue

        section_title = line
        j = i + 1

        # Skip blank lines to find column header
        while j < len(lines) and not lines[j].strip():
            j += 1

        # Guard 2: column header
        if j >= len(lines) or not _JB_COL_HDR.match(lines[j].strip()):
            out.append(lines[i])
            i += 1
            continue

        col_hdr_line = lines[j].strip()
        data_start   = j + 1

        # Find first non-blank data line
        first_data_idx = _find_first_data_row(lines, data_start)

        # Guard 3: first data row must use MM-DD date format
        if first_data_idx is None or not _JB_DATE_DD.match(lines[first_data_idx].strip()):
            out.append(lines[i])
            i += 1
            continue

        # Find section end (next section header, page separator, or end of text)
        section_end = len(lines)
        for k in range(data_start, len(lines)):
            l = lines[k].strip()
            if (l != section_title and _JB_SECTION_HDR.match(l)
                    or l.startswith("---page-separator")
                    or l.startswith("Page:")):
                section_end = k
                break

        # Guard 4: no existing <table> tags in this section
        if _jb_section_has_table(lines, i, section_end):
            out.append(lines[i])
            i += 1
            continue

        # All guards passed — parse and convert
        parts_upper = col_hdr_line.upper().split()
        if len(parts_upper) == 2 and parts_upper[1] == "BALANCE":
            header_cols = ["Date", "Balance"]
        elif "NUMBER" in parts_upper:
            header_cols = ["Date", "Number", "Amount"]
        else:
            header_cols = ["Date", "Description", "Amount"]

        out.append(section_title)
        rows    = []
        current = None
        k       = data_start

        while k < section_end:
            raw      = lines[k]
            row_line = raw.strip()

            if not row_line:
                k += 1
                continue

            if (_JB_SECTION_HDR.match(row_line)
                    or row_line.startswith("---page-separator")
                    or row_line.startswith("Page:")):
                break

            # Balance-only row
            mb = _JB_BAL_ROW.match(row_line)
            if mb and header_cols == ["Date", "Balance"]:
                if current:
                    rows.append(current)
                current = {"Date": mb.group(1), "Balance": mb.group(2)}
                k += 1
                continue

            # Full transaction row
            mt = _JB_TXN_ROW.match(row_line)
            if mt:
                if current:
                    rows.append(current)
                desc_key = "Description" if "Description" in header_cols else "Number"
                current  = {
                    "Date":    mt.group(1),
                    desc_key:  mt.group(2).strip(),
                    "Amount":  mt.group(3),
                }
                k += 1
                continue

            # Continuation line
            if current and row_line:
                desc_key = "Description" if "Description" in header_cols else "Number"
                if desc_key in current:
                    current[desc_key] += " " + row_line
                k += 1
                continue

            k += 1

        if current:
            rows.append(current)

        if rows:
            out.append(_jb_rows_to_html(header_cols, rows))

        i = section_end   # jump past the whole section
        continue

    return "\n".join(out)

# --------------------------
# Navy Federal full-page text-layer parser (Fix NF-1)
# --------------------------

def _is_nfcu_document(pdf_path: str) -> bool:
    """
    Strict detector for Navy Federal statements.
    Used to gate NF-specific parsing so other banks are unaffected.
    """
    try:
        import pymupdf as fitz
        doc = fitz.open(pdf_path)
        pages_to_check = min(2, len(doc))
        text = ""
        for i in range(pages_to_check):
            text += "\n" + (doc[i].get_text() or "")
        doc.close()
        t = text.lower()
        # Strict Navy-only guard to avoid affecting other statement types.
        return (
            ("statement of account" in t)
            and ("access no." in t)
            and ("routing number" in t)
            and ("navy federal" in t or "navyfederal.org" in t)
        )
    except Exception:
        return False


def _parse_nfcu_page(pdf_path: str, page_num: int) -> str:
    """
    Full text-layer parser for Navy Federal Credit Union statements.
    GLM-OCR fails on NFCU pages because:
      - The summary table has 5 wide columns with no visible borders
      - Transaction tables have Amount($) and Balance($) spatially far right
      - Multi-line descriptions (wrapped lines) confuse OCR table detection
    This function reads the PDF text layer directly via pdfplumber and produces
    correct, complete HTML for every section: summary table, transaction tables,
    items paid, savings, disclosures.
    Detection guards (all must pass — safe no-op for all other banks):
      1. Page text must contain 'Access No.' AND 'Statement of Account'
         (unique to Navy Federal statement format)
      2. pdfplumber must return extractable text (not a scanned page)
    Returns complete HTML string for the page, or "" if not NFCU / any error.
    """
    try:
        import pymupdf as fitz

        doc = fitz.open(pdf_path)
        if page_num >= len(doc):
            doc.close()
            return ""
        page = doc[page_num]
        page_text = page.get_text() or ""
        words = page.get_text("words") or []
        doc.close()

        if not page_text or not words:
            return ""

        t = page_text.lower()
        is_nfcu = (
            ("statement of account" in t)
            and (("access no." in t) or ("routing number" in t) or ("navy federal" in t) or ("navyfederal.org" in t))
        )
        if not is_nfcu:
            return ""

        # Group words into reading lines by y-position.
        buckets = []
        for w in words:
            if len(w) < 5:
                continue
            x0 = float(w[0])
            y0 = float(w[1])
            token = w[4].strip()
            if not token:
                continue
            bi = None
            for i, (yb, _) in enumerate(buckets):
                if abs(yb - y0) <= 2.0:
                    bi = i
                    break
            if bi is None:
                buckets.append((y0, [(x0, token)]))
            else:
                buckets[bi][1].append((x0, token))

        lines = []
        for y, arr in sorted(buckets, key=lambda x: x[0]):
            toks = [tok for _, tok in sorted(arr, key=lambda x: x[0])]
            xs = [x for x, _ in sorted(arr, key=lambda x: x[0])]
            lines.append((y, xs, toks, " ".join(toks)))

        date_re = re.compile(r"^\d{2}-\d{2}$")
        money_re = re.compile(r"^[\d,]+\.\d{2}-?$")
        acct_re = re.compile(r"^(Business Checking|Mbr Business Savings)\s*-\s*(\d+)")
        stop_markers = (
            "Items Paid",
            "Average Daily Balance",
            "2024 Year to Date",
            "Disclosure",
            "What to Do",
            "Errors",
            "Payments",
            "SAVINGS DIVIDENDS",
            "REMITTANCE RECEIVED",
            "DEPOSIT VOUCHER",
            "ACCOUNT NUMBER",
            "MARK \"X\"",
            "ADDRESS/ORDER",
            "ITEMS ON REVERSE",
            "CHANGE OF ADDRESS",
            "PLEASE PRINT",
            "RANK/RATE",
            "PO BOX",
            "MERRIFIELD",
            "Questions about this Statement",
        )

        def parse_account_rows(start_idx):
            rows = []
            i = start_idx
            pending = None
            while i < len(lines):
                _, _, toks, text = lines[i]
                if not text:
                    i += 1
                    continue

                # Section/account boundaries
                if acct_re.match(text) and i != start_idx:
                    break
                if any(text.startswith(s) for s in stop_markers):
                    break
                # Some boilerplate lines include prefixes/suffixes; match by containment too.
                upper_text = text.upper()
                if (
                    "REMITTANCE RECEIVED" in upper_text
                    or "DEPOSIT VOUCHER" in upper_text
                    or "ACCOUNT NUMBER" in upper_text
                    or "ADDRESS/ORDER" in upper_text
                    or "ITEMS ON REVERSE" in upper_text
                    or "CHANGE OF ADDRESS" in upper_text
                    or "PLEASE PRINT" in upper_text
                ):
                    break
                if text in ("Checking", "Savings"):
                    break
                if text.startswith("Date Transaction Detail"):
                    i += 1
                    continue

                def flush_pending():
                    nonlocal pending
                    if pending is not None:
                        rows.append(pending)
                        pending = None

                if toks and date_re.match(toks[0]):
                    flush_pending()
                    date = toks[0]
                    rest = toks[1:]
                    mvals = [tok for tok in rest if money_re.match(tok)]
                    desc_tokens = [tok for tok in rest if not money_re.match(tok)]

                    amount = ""
                    balance = ""
                    if len(mvals) >= 2:
                        amount = mvals[-2]
                        balance = mvals[-1]
                    elif len(mvals) == 1:
                        # Many NFCU lines can be split; single amount token is usually balance.
                        balance = mvals[-1]

                    pending = {
                        "date": date,
                        "desc": " ".join(desc_tokens).strip(),
                        "amount": amount,
                        "balance": balance,
                    }
                else:
                    if pending is not None:
                        mvals = [tok for tok in toks if money_re.match(tok)]
                        desc_tokens = [tok for tok in toks if not money_re.match(tok)]

                        # Stop if we have already captured a balance and now hit
                        # known non-transaction boilerplate lines.
                        upper = text.upper()
                        if pending["balance"] and (
                            "REMITTANCE" in upper
                            or "DEPOSIT VOUCHER" in upper
                            or "ACCOUNT NUMBER" in upper
                            or "ADDRESS/ORDER" in upper
                            or "ITEMS ON REVERSE" in upper
                            or "PO BOX" in upper
                        ):
                            break

                        # Beginning/Ending balance rows are single logical rows.
                        # Do not absorb unrelated continuation text.
                        if (
                            ("BEGINNING BALANCE" in pending["desc"].upper() or "ENDING BALANCE" in pending["desc"].upper())
                            and not mvals
                            and not (toks and date_re.match(toks[0]))
                        ):
                            break
                        if desc_tokens:
                            pending["desc"] = (pending["desc"] + " " + " ".join(desc_tokens)).strip()
                        if len(mvals) >= 2 and (not pending["amount"] or not pending["balance"]):
                            if not pending["amount"]:
                                pending["amount"] = mvals[-2]
                            if not pending["balance"]:
                                pending["balance"] = mvals[-1]
                        elif len(mvals) == 1 and not pending["balance"]:
                            pending["balance"] = mvals[-1]
                    # else ignore stray line
                i += 1

            if pending is not None:
                rows.append(pending)
            # keep rows that have at least date+desc, and preferably balance
            cleaned = [r for r in rows if r["date"] and r["desc"] and (r["amount"] or r["balance"])]
            return cleaned, i

        out = []
        i = 0
        while i < len(lines):
            _, _, _, text = lines[i]
            if not text:
                i += 1
                continue

            m = acct_re.match(text)
            if m:
                acct_name = m.group(1)
                acct_no = m.group(2)
                title = f"{acct_name} - {acct_no}"
                if i + 1 < len(lines) and "Continued" in lines[i + 1][3]:
                    title += " (Continued from previous page)"
                out.append(f"<b>{title}</b>")
                out.append("<table>")
                out.append("<tr><th>Date</th><th>Transaction Detail</th><th>Amount($)</th><th>Balance($)</th></tr>")
                rows, i2 = parse_account_rows(i + 1)
                for r in rows:
                    out.append(
                        f"<tr><td>{r['date']}</td><td>{r['desc']}</td><td>{r['amount']}</td><td>{r['balance']}</td></tr>"
                    )
                out.append("</table>")
                i = i2
                continue

            # Keep section headers only (avoid noisy paragraph dump)
            if text in ("Checking", "Savings"):
                out.append(f"<h3>{text}</h3>")
            elif text.startswith("Items Paid"):
                out.append("<b>Items Paid</b>")
            i += 1

        return "\n".join(out).strip()

    except Exception as e:
        log.warning("_parse_nfcu_page failed (page %d): %s", page_num, e)
        return ""

def _extract_nfcu_summary_table(pdf_path: str, page_num: int) -> str:
    """
    Navy-only additive helper:
    Rebuild "Summary of your deposit accounts" from PDF text-layer words.
    Returns a clean HTML table or "" (safe no-op on any miss/error).
    """
    try:
        import pymupdf as fitz

        doc = fitz.open(pdf_path)
        if page_num >= len(doc):
            doc.close()
            return ""
        page = doc[page_num]
        words = page.get_text("words") or []
        doc.close()
        if not words:
            return ""

        # Group tokens into reading lines by y-position.
        buckets = []
        for w in words:
            if len(w) < 5:
                continue
            x0 = float(w[0])
            y0 = float(w[1])
            tok = str(w[4] or "").strip()
            if not tok:
                continue
            bi = None
            for i, (yb, _) in enumerate(buckets):
                if abs(yb - y0) <= 2.0:
                    bi = i
                    break
            if bi is None:
                buckets.append((y0, [(x0, tok)]))
            else:
                buckets[bi][1].append((x0, tok))

        lines = []
        for y, arr in sorted(buckets, key=lambda x: x[0]):
            arr2 = sorted(arr, key=lambda x: x[0])
            toks = [t for _, t in arr2]
            text = " ".join(toks).strip()
            lines.append((y, toks, text))

        anchor_idx = None
        for i, (_, _, text) in enumerate(lines):
            tl = text.lower()
            if "summary of your deposit accounts" in tl or ("summary" in tl and "deposit accounts" in tl):
                anchor_idx = i
                break
        if anchor_idx is None:
            return ""

        money_re = re.compile(r"^\$?[\d,]+\.\d{2}-?$")
        acct_no_re = re.compile(r"^\d{8,12}$")
        stop_re = re.compile(
            r"(business checking\s*-|mbr business savings\s*-|date\s+transaction\s+detail|items paid)",
            re.IGNORECASE,
        )

        # NFCU summary table real columns from PDF:
        # Account | Previous Balance | Deposits/Credits | Withdrawals/Debits | Ending Balance | YTD Dividends
        rows = []
        pending_account = ""
        account_name_re = re.compile(
            r"^(business checking|mbr business savings|checking|savings|money market|certificate).*$",
            re.IGNORECASE,
        )

        for _, toks, text in lines[anchor_idx + 1:]:
            if not text:
                continue
            if stop_re.search(text):
                break
            if not toks:
                continue
            t0 = toks[0].lower()
            tl = text.lower()

            # Ignore split header lines ("Previous ...", "Balance Credits ...")
            if (
                "previous" in tl
                or "deposits" in tl
                or "withdrawals" in tl
                or "ending" in tl
                or "dividends" in tl
            ) and not any(money_re.match(t) for t in toks):
                continue

            # Account name can appear on its own line, followed by acct# + numeric columns.
            if account_name_re.match(text.strip()) and not any(money_re.match(t) for t in toks):
                pending_account = text.strip()
                continue

            mvals = [t.replace("$", "") for t in toks if money_re.match(t)]
            acct_tokens = [t for t in toks if acct_no_re.match(t)]

            # Totals row: "Totals <five amounts>"
            if t0 == "totals" and len(mvals) >= 5:
                rows.append(["Totals", mvals[0], mvals[1], mvals[2], mvals[3], mvals[4]])
                continue

            # Account numeric row: "<acctno> <five amounts>"
            if acct_tokens and len(mvals) >= 5:
                acct = acct_tokens[0]
                acct_label = (pending_account + " - " + acct).strip(" -") if pending_account else acct
                rows.append([acct_label, mvals[0], mvals[1], mvals[2], mvals[3], mvals[4]])
                pending_account = ""
                continue

        if not rows:
            return ""

        hdr = [
            "Account",
            "Previous Balance",
            "Deposits/Credits",
            "Withdrawals/Debits",
            "Ending Balance",
            "YTD Dividends",
        ]
        return _grid_to_html([hdr] + rows)
    except Exception:
        return ""

def _replace_nfcu_summary_table(page_md: str, summary_table_html: str) -> str:
    """
    Replace the first table after 'Summary of your deposit accounts' with
    a rebuilt NFCU summary table. Safe no-op if anchor/table is missing.
    """
    if not page_md or not summary_table_html:
        return page_md
    table_pattern = re.compile(r"<table[^>]*>.*?</table>", re.DOTALL | re.IGNORECASE)
    lower = page_md.lower()
    anchor = lower.find("summary of your deposit accounts")

    # Primary path: replace first table after the summary heading.
    if anchor >= 0:
        for m in table_pattern.finditer(page_md):
            if m.start() > anchor:
                old_table = m.group(0)
                return page_md.replace(old_table, summary_table_html, 1)

    # Fallback path: heading may be malformed/absent in OCR text.
    # For NFCU summary, header should contain balance/deposit/withdrawal/ytd signals.
    for m in table_pattern.finditer(page_md):
        tbl = m.group(0)
        p = TableGridParser()
        p.feed(tbl)
        g = _build_grid(p.rows)
        if not g:
            continue
        h = " ".join(str(c or "").strip().lower() for c in g[0])
        if (
            "previous" in h
            and "deposits" in h
            and "withdrawals" in h
            and "ending" in h
            and "dividends" in h
        ):
            return page_md.replace(tbl, summary_table_html, 1)

    return page_md

def _split_fused_date_transaction_header(grid):
    """
    Fix fused header "Date Transaction Detail" -> separate Date + Transaction Detail.
    Strict guard: only when header has that exact fused phrase and also has
    Amount/Balance columns, so unrelated tables are untouched.
    """
    if not grid or len(grid) < 2:
        return grid
    ncols = max(len(r) for r in grid if isinstance(r, list))
    if ncols < 3:
        return grid

    header_row_idx = None
    fused_col_idx = None
    for ri in range(min(4, len(grid))):
        r = [str(c or "").strip() for c in (grid[ri] + [""] * (ncols - len(grid[ri])))]
        for ci, c in enumerate(r):
            cc = re.sub(r"\s+", " ", c).strip().lower()
            if cc == "date transaction detail":
                row_text = " ".join(re.sub(r"\s+", " ", x).strip().lower() for x in r)
                if "amount" in row_text and "balance" in row_text:
                    header_row_idx = ri
                    fused_col_idx = ci
                    break
        if header_row_idx is not None:
            break
    if header_row_idx is None or fused_col_idx is None:
        return grid

    date_tok = re.compile(r"^\d{1,2}(?:[-/])\d{2}(?:[-/]\d{2,4})?$")

    new_grid = []
    for ri, row in enumerate(grid):
        cells = [str(c or "").strip() for c in (row + [""] * (ncols - len(row)))]
        first = cells[fused_col_idx]
        dval = ""
        tval = first
        if first:
            parts = first.split()
            if parts and date_tok.match(parts[0]):
                dval = parts[0]
                tval = " ".join(parts[1:]).strip()
        if ri == header_row_idx:
            split_row = (
                cells[:fused_col_idx]
                + ["Date", "Transaction Detail"]
                + cells[fused_col_idx + 1:]
            )
        elif ri < header_row_idx:
            split_row = cells[:fused_col_idx] + [cells[fused_col_idx], ""] + cells[fused_col_idx + 1:]
        else:
            split_row = cells[:fused_col_idx] + [dval, tval] + cells[fused_col_idx + 1:]
        new_grid.append(split_row)
    return new_grid

def _split_combined_summary_daily_balance_grid(grid):
    """
    Some OCR outputs fuse an account summary table and a Daily Balance block
    into one HTML table. Split them into two tables when structure is clear.
    Generic guards:
    - A row containing "Daily Balance" exists
    - There are rows after it with date-like + amount-like tokens
    """
    if not grid or len(grid) < 4:
        return None

    def _norm(s):
        return re.sub(r"\s+", " ", str(s or "").strip().lower())

    daily_idx = None
    for i, row in enumerate(grid):
        row_text = " ".join(_norm(c) for c in row if str(c or "").strip())
        if "daily balance" in row_text:
            daily_idx = i
            break
    if daily_idx is None or daily_idx <= 1 or daily_idx >= len(grid) - 2:
        return None

    date_re = re.compile(r"^\d{1,2}/\d{2}(?:/\d{2,4})?$")
    money_re = re.compile(r"^-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{2})-?$")

    daily_rows = []
    for row in grid[daily_idx + 1:]:
        tokens = []
        for cell in row:
            ct = str(cell or "").strip()
            if not ct:
                continue
            tokens.extend(re.split(r"\s+", ct))
        dates = [t for t in tokens if date_re.match(t)]
        amts = [t for t in tokens if money_re.match(t)]
        if not dates or not amts:
            continue
        for d, a in zip(dates, amts):
            daily_rows.append([d, a])

    # Need meaningful daily table to split safely.
    if len(daily_rows) < 2:
        return None

    summary = [r for r in grid[:daily_idx] if any(str(c or "").strip() for c in r)]
    if len(summary) < 2:
        return None

    daily = [["Date", "Ledger Balance"]] + daily_rows
    return [summary, daily]

def _normalize_double_desc_amount_header_table(grid):
    """
    Fix 13: Collapse OCR tables with a duplicated column group:
      Date | Description | Amount | Description | Amount
    (or 4 cols if trailing Amount column was dropped). UCB-style summary tables
    put labels like "9 Credit(s) This Period" in the Date column.
    Output: Date | Description | Amount
    """
    if not grid or len(grid) < 2:
        return grid

    def _hdr_tight(s):
        return re.sub(r"\s+", "", str(s or "").strip().lower())

    # OCR often inserts spaces inside words; compare whitespace-stripped tokens.
    expected5_t = ["date", "description", "amount", "description", "amount"]
    expected4_t = ["date", "description", "amount", "description"]

    def _row_is_ucb_header(cells):
        if len(cells) == 5:
            return [_hdr_tight(c) for c in cells] == expected5_t
        if len(cells) == 4:
            return [_hdr_tight(c) for c in cells] == expected4_t
        return False

    hdr_idx = None
    ncols = 0
    for i in range(min(5, len(grid))):
        raw_try = [str(c or "").strip() for c in grid[i]]
        if _row_is_ucb_header(raw_try):
            hdr_idx = i
            ncols = len(raw_try)
            break
    if hdr_idx is None:
        return grid

    date_only_re = re.compile(r"^\d{1,2}/\d{1,2}/\d{2,4}$")
    date_prefix_re = re.compile(r"^(\d{1,2}/\d{1,2}/\d{2,4})\s*(.*)$")
    money_cell_re = re.compile(r"^-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{2})-?$")

    def _is_money(s):
        if not s or not str(s).strip():
            return False
        return bool(money_cell_re.match(str(s).strip()))

    def _extract_money(s):
        if not s:
            return ""
        m = re.search(r"(-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{2})-?)", str(s))
        return m.group(1).strip() if m else ""

    out = [["Date", "Description", "Amount"]]

    for row in grid[hdr_idx + 1 :]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < ncols:
            cells.append("")
        c0, c1, c2 = cells[0], cells[1] if len(cells) > 1 else "", cells[2] if len(cells) > 2 else ""
        c3 = cells[3] if len(cells) > 3 else ""
        c4 = cells[4] if len(cells) > 4 else ""

        if not any(cells[:ncols]):
            continue

        # A) Fused date + label in col0, amount in col1 (Beginning/Ending Balance, etc.)
        mpre = date_prefix_re.match(c0)
        if mpre and _is_money(c1):
            date_s = mpre.group(1).strip()
            low = (c0 + " " + (mpre.group(2) or "")).lower()
            if "beginning balance" in low:
                desc = "Beginning Balance"
            elif "ending balance" in low:
                desc = "Ending Balance"
            else:
                desc = (mpre.group(2) or "").strip() or "Transaction"
            out.append([date_s, desc, c1])
            continue

        # B) Date-only col0; description may contain Beginning Balance + amount; amount col wrong ($0)
        if date_only_re.match(c0.strip()):
            date_s = c0.strip()
            desc = c1
            amt = c2
            if desc and "beginning balance" in desc.lower():
                mx = _extract_money(desc)
                if mx and (not amt or amt in ("$0.00", "0.00", "$0", ".00", "$.00")):
                    amt = mx
                    desc = re.sub(
                        r"(?i)beginning\s+balance\s*\$?[\d,]+\.?\d*\s*",
                        "Beginning Balance ",
                        desc,
                    )
                    desc = re.sub(r"(?i)\s*average\s+ledger\s+balance\s*", " ", desc).strip()
                    if not desc or desc == "Beginning Balance":
                        desc = "Beginning Balance"
            out.append([date_s, desc, amt])
            continue

        # D) Summary / label rows: first column is NOT a date, second is money (Credits/Debits/Service)
        if not date_prefix_re.match(c0) and not date_only_re.match(c0.strip()) and _is_money(c1):
            out.append(["", c0, c1])
            continue

        # C) Default: first Date|Description|Amount triple (ignore duplicate pair)
        out.append([c0, c1, c2])

    return out if len(out) >= 2 else grid


def _normalize_ucb_three_col_beginning_balance_fusion(grid):
    """
    Fix 14: UCB sometimes OCRs the summary as 3 columns (not 5), with the real
    beginning balance inside the description and $0.00 in the amount column:
      02/01/2025 | Beginning Balance $3,562.49 Average Ledger Balance | $0.00
    Normalize to: Date | Beginning Balance | $3,562.49
    Guard: standard Date|Description|Amount header only.
    """
    if not grid or len(grid) < 2:
        return grid

    def _tight(s):
        return re.sub(r"\s+", "", str(s or "").strip().lower())

    h0 = [str(c or "").strip() for c in grid[0]]
    # Do not use max row width — OCR sometimes adds an extra empty column on some rows only.
    if len(h0) < 3:
        return grid
    if _tight(h0[0]) != "date" or _tight(h0[1]) != "description" or _tight(h0[2]) != "amount":
        return grid

    date_only_re = re.compile(r"^\d{1,2}/\d{1,2}/\d{2,4}$")

    def _extract_money(s):
        if not s:
            return ""
        m = re.search(r"(-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{2})-?)", str(s))
        return m.group(1).strip() if m else ""

    zeroish = {"", "$0.00", "0.00", "$0", ".00", "$.00", "0"}

    out = [list(grid[0])]
    changed = False
    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < 3:
            cells.append("")
        c0, c1, c2 = cells[0], cells[1], cells[2]
        if (
            date_only_re.match(c0.strip())
            and c1
            and "beginning balance" in c1.lower()
        ):
            mx = _extract_money(c1)
            c2n = c2.strip() if c2 else ""
            if mx and (not c2n or c2n in zeroish):
                out.append([c0.strip(), "Beginning Balance", mx])
                changed = True
                continue
        out.append([c0, c1, c2])

    return out if changed else grid


def _fix_three_col_date_desc_amount_left_shift(grid):
    """
    Fix 15: OCR shifts transaction rows one column left:
      col0 = "02/24/2025 ITM DEPOSIT", col1 = "$1,440.00", col2 = empty
    Expected: Date | Description | Amount
    Activates only on Date|Description|Amount headers and strict money in col1 with empty col2.
    """
    if not grid or len(grid) < 2:
        return grid

    def _tight(s):
        return re.sub(r"\s+", "", str(s or "").strip().lower())

    h0 = [str(c or "").strip() for c in grid[0]]
    if len(h0) < 3:
        return grid
    if _tight(h0[0]) != "date" or _tight(h0[1]) != "description" or _tight(h0[2]) != "amount":
        return grid

    date_then_desc = re.compile(
        r"^(\d{1,2}/\d{1,2}/\d{2,4})\s+(.+)$"
    )
    money_re = re.compile(r"^-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{2})-?$")

    out = [list(grid[0])]
    changed = False
    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < 3:
            cells.append("")
        c0, c1, c2 = cells[0], cells[1], cells[2]
        if not c0:
            out.append([c0, c1, c2])
            continue
        m = date_then_desc.match(c0)
        if not m:
            out.append([c0, c1, c2])
            continue
        desc_part = (m.group(2) or "").strip()
        if not desc_part:
            out.append([c0, c1, c2])
            continue
        if not (c1 and money_re.match(c1.strip())):
            out.append([c0, c1, c2])
            continue
        if c2 and str(c2).strip():
            out.append([c0, c1, c2])
            continue
        out.append([m.group(1).strip(), desc_part, c1.strip()])
        changed = True

    return out if changed else grid


def _normalize_fused_date_posted_amount_table(grid):
    """
    Normalize 2-column OCR tables with fused header like:
      col1: "Date posted Transaction description [Reference number]"
      col2: "Amount"
    into stable 3-column transaction layout:
      Date | Transaction Description | Amount
    Strict guard: only activates on that specific fused-header signature.
    """
    if not grid or len(grid) < 2:
        return grid

    ncols = max(len(r) for r in grid if isinstance(r, list))
    if ncols < 2:
        return grid

    def _row_cells(r):
        return [str(c or "").strip() for c in (r + [""] * (ncols - len(r)))]

    hdr_idx = None
    for ri in range(min(6, len(grid))):
        cells = _row_cells(grid[ri])
        row_text = " ".join(cells).lower()
        if (
            "date posted transaction description" in row_text
            and "amount" in row_text
        ):
            hdr_idx = ri
            break
    if hdr_idx is None:
        return grid

    date_re = re.compile(r"^\d{1,2}[/-]\d{1,2}(?:[/-]\d{2,4})?$")
    out = []

    for ri, row in enumerate(grid):
        cells = _row_cells(row)
        if ri == hdr_idx:
            out.append(["Date", "Transaction Description", "Amount"])
            continue
        if ri < hdr_idx:
            # Keep pre-header text as non-transaction row.
            joined = " ".join(c for c in cells if c).strip()
            out.append(["", joined, ""])
            continue

        c1 = cells[0].strip()
        c2 = cells[1].strip() if len(cells) > 1 else ""

        # Skip duplicated inline sub-headers that often appear in fused OCR tables.
        lower_c1 = c1.lower()
        if (
            lower_c1.startswith("date posted transaction description")
            or lower_c1 in {"ach additions", "ach deductions", "other additions", "other deductions", "service charges and fees"}
        ):
            continue

        date = ""
        desc = c1
        if c1:
            parts = c1.split()
            if parts and date_re.match(parts[0]):
                date = parts[0]
                desc = " ".join(parts[1:]).strip()

        # Amount may live in any shifted column for malformed OCR tables.
        # Prefer explicit second-column value, then rightmost strict money token in row.
        amount = c2
        if not amount:
            money_cells = []
            for x in cells[1:]:
                t = str(x or "").strip()
                if re.match(r"^-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{2})-?$", t):
                    money_cells.append(t)
            if money_cells:
                amount = money_cells[-1]

        # If still empty, try extracting trailing amount from description.
        if not amount and desc:
            m = re.search(r"(-?\$?\d{1,3}(?:,\d{3})*(?:\.\d{2})-?)\s*$", desc)
            if m:
                amount = m.group(1)
                desc = desc[:m.start()].strip()

        if not (date or desc or amount):
            continue
        out.append([date, desc, amount])

    # Keep at least header + 1 row to avoid degrading clean tables on accidental match.
    if len(out) < 2:
        return grid
    return out

def normalize_html_tables(text: str) -> str:
    """
    For every <table>...</table>:
      1. Parse to grid (expand colspan/rowspan).
      2. Drop truly-empty columns.
      3. Merge blank-header text columns into the left column.
      4. Fix 1 — clean fused header artifacts and recover the lost first data row.
      5. Fix 2 — promote a misplaced header row when real column headers landed
                  in a <td> data row instead of the <th> header row.
      6. Fix 3 — split fused key-value rows in summary sections.
      7. Emit normalised <table> HTML.
    """
    if not text or "<table" not in text.lower():
        return text

    pattern = re.compile(r"<table[^>]*>.*?</table>", re.DOTALL | re.IGNORECASE)
    out = []
    last = 0

    for m in pattern.finditer(text):
        out.append(text[last : m.start()])
        table_html = m.group(0)
        try:
            p = TableGridParser()
            p.feed(table_html)
            grid = _build_grid(p.rows)

            # Suppress known junk tables (OCR artifacts from page headers)
            if _is_junk_table(grid):
                # Append nothing (suppress the table), update last, skip rest of pipeline
                out.append("")
                last = m.end()  # must set here before continue since line below is outside try
                continue

            # Fix 13: MUST run before _drop_truly_empty_columns — otherwise the 5th column
            # can be removed and the UCB double-pair header no longer matches.
            grid = _normalize_double_desc_amount_header_table(grid)
            # Fix 14: same bank, 3-col OCR fused Beginning Balance + $0.00 amount column.
            grid = _normalize_ucb_three_col_beginning_balance_fusion(grid)
            # Fix 15: "Deposits (continued)" etc. — date+desc in col1, amount in col2, empty col3.
            grid = _fix_three_col_date_desc_amount_left_shift(grid)

            grid = _drop_truly_empty_columns(grid)
            grid = _merge_blank_header_text_columns(grid)

            # Fix 5: reconstruct mid-table separator rows split across columns by OCR
            grid = _reconstruct_separator_rows(grid)

            # Fix 1: fused-header artifact (two-signal guard — safe on clean PDFs)
            grid, recovered_row = _clean_header_artifacts(grid)
            if recovered_row is not None:
                grid.insert(1, recovered_row)

            # Fix 2: misplaced header row promotion (keyword-score guard — safe on clean PDFs)
            grid = _promote_misplaced_header_row(grid)

            # Fix NF-2: split fused "Date Transaction Detail" header/cell.
            grid = _split_fused_date_transaction_header(grid)

            # Fix 11: normalize fused "Date posted Transaction description ... / Amount"
            # two-column tables into stable Date/Description/Amount structure.
            grid = _normalize_fused_date_posted_amount_table(grid)

            # Fix 8: split fused multi-keyword header cell (First Horizon Bank)
            grid = _split_fused_multicolumn_header(grid)

            # Fix 9: normalize DAILY BALANCE SUMMARY tables with stacked dates
            grid = _normalize_daily_balance_table(grid)

            # Fix 10: normalize CHECKS PAID SUMMARY repeating-group tables
            grid = _normalize_checks_paid_table(grid)

            # Fix 6: merge split rows where description wraps to next line
            # (Hardin County Bank and similar monospace statement formats)
            grid = _merge_split_rows(grid)

            # Fix 7: extract trailing amount fused into description cell
            # (Hardin County Bank: "CHASE CREDIT CRD EPAY 8319249882 500.00")
            grid = _extract_fused_desc_amount(grid)

            # Fix 19: junk Amount cell but money token still present in row text (Truist MSBILL/PIN).
            grid = _repair_da3_garbled_amount_cells(grid)

            # Fix 3: fused key-value rows in summary sections (no-space guard — safe on all PDFs)
            grid = _fix_fused_keyvalue_rows(grid)
            # Fix 16/20: duplicate data rows inside same Date|Description|Amount table (OCR repeats).
            grid = _dedupe_duplicate_rows_in_da3_table(grid)
            # Fix 12: split fused summary+daily-balance combined table, if detected.
            split_tables = _split_combined_summary_daily_balance_grid(grid)
            if split_tables:
                out.append("\n\n".join(_grid_to_html(g) for g in split_tables if g))
            else:
                out.append(_grid_to_html(grid) if grid else table_html)
        except Exception:
            out.append(table_html)
        last = m.end()

    out.append(text[last:])
    return "".join(out)


def _transaction_row_dedupe_key(cells):
    """Stable key for Date|Description|Amount rows (OCR variants, HTML entities)."""
    parts = [str(c or "").strip() for c in cells[:3]]
    while len(parts) < 3:
        parts.append("")
    date_s, desc_s, amt_s = parts[0], parts[1], parts[2]
    date_s = re.sub(r"\s+", "", date_s)
    amt_s = re.sub(r"[\s$,]", "", amt_s).lower().replace("−", "-")
    desc_s = html.unescape(desc_s)
    desc_s = desc_s.replace("`", "'").replace("’", "'")
    desc_s = re.sub(r"\s+", "", desc_s.lower())
    return (date_s, desc_s, amt_s)


def _da3_loose_key(cells):
    """
    (date, amount) only — for detecting duplicate DA3 tables when description drifts between OCR passes.
    Uses strict currency + float amount so junk cells do not create keys; 1,234.56 vs 1234.56 match.
    """
    parts = [str(c or "").strip() for c in cells[:3]]
    while len(parts) < 3:
        parts.append("")
    date_s, desc_s, amt_s = parts[0], parts[1], parts[2]
    if not _DA3_LEADING_DATE.match(date_s):
        return None
    comb = f"{date_s} {desc_s}".lower()
    if re.search(r"\btotal\b", comb) and len(desc_s) > 25:
        return None
    if not _DA3_STRICT_AMOUNT.match(amt_s.strip()):
        return None
    t_amt = re.sub(r"[\s$,]", "", amt_s).replace("−", "-").replace("$", "")
    try:
        af = round(float(t_amt), 2)
    except ValueError:
        return None
    d_norm = re.sub(r"\s+", "", date_s)
    return (d_norm, af)


def _row_counter_loose_da3(g):
    """Multiset of (date, amount) for DA3 data rows with strict amounts."""
    c = Counter()
    if not g or len(g) < 2:
        return c
    for r in g[1:]:
        cells = [str(x or "").strip() for x in r]
        if not any(cells):
            continue
        k = _da3_loose_key(cells)
        if k:
            c[k] += 1
    return c


def _overlap_ratio_counter(c_num, c_den):
    """Fraction of c_num's multiset mass matched in c_den (min counts per key)."""
    tot = sum(c_num.values())
    if tot == 0:
        return 0.0
    hit = sum(min(c_num[k], c_den.get(k, 0)) for k in c_num)
    return hit / tot


def _multiset_leq(c_small, c_big):
    return all(c_small[k] <= c_big.get(k, 0) for k in c_small)


def _is_da3_header(grid):
    if not grid or len(grid[0]) < 3:
        return False
    h = [re.sub(r"\s+", " ", str(c or "").strip().lower()) for c in grid[0]]
    if len(h) != 3:
        return False
    return h[0] == "date" and "description" in h[1] and (
        h[2] == "amount" or h[2].startswith("amount")
    )


_DA3_STRICT_AMOUNT = re.compile(r"^-?\$?\d{1,3}(?:,\d{3})*\.\d{2}-?$")
_DA3_MONEY_TOKEN = re.compile(
    r"(?<![\d,])(-?\$?\d{1,3}(?:,\d{3})*\.\d{2}|-?\$?\d{2,6}\.\d{2})(?!\d)"
)
_MSFT_PIN_G_REF = re.compile(r"MICROSOFT#(G\d{6,16})", re.IGNORECASE)
_DA3_LEADING_DATE = re.compile(r"^\d{1,2}/\d{1,2}(?:/\d{2,4})?$")


def _repair_da3_garbled_amount_cells(grid):
    """
    Fix 19: Amount column shows fused junk (letters, card digits) while the real debit/credit
    still appears as a money token in the row text — use the rightmost plausible amount.
    Only for Date|Description|Amount tables and strict MM/DD dates in column 0.
    """
    if not grid or len(grid) < 2 or not _is_da3_header(grid):
        return grid

    date_re = re.compile(r"^\d{1,2}/\d{1,2}(?:/\d{2,4})?$")
    out = [list(grid[0])]
    changed = False

    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < 3:
            cells.append("")
        c0, c1, c2 = cells[0], cells[1], cells[2]
        if not date_re.match(c0):
            out.append(cells)
            continue
        c2s = c2.strip()
        if _DA3_STRICT_AMOUNT.match(c2s):
            out.append(cells)
            continue
        blob = f"{c1} {c2}".strip()
        if not blob:
            out.append(cells)
            continue
        found = _DA3_MONEY_TOKEN.findall(blob)
        if not found:
            out.append(cells)
            continue
        pick_raw = None
        for m in reversed(found):
            t = m.replace("$", "").replace(",", "")
            try:
                v = float(t)
            except ValueError:
                continue
            if 0.01 <= v <= 9_999_999.99:
                pick_raw = m.replace("$", "").strip()
                break
        if not pick_raw:
            out.append(cells)
            continue
        new_desc = c1
        for suf in (
            pick_raw,
            pick_raw.replace(",", ""),
            f"${pick_raw}",
            f"${pick_raw.replace(',', '')}",
        ):
            if suf and new_desc.rstrip().endswith(suf):
                new_desc = new_desc[: -len(suf)].rstrip()
                break
        out.append([c0, new_desc, pick_raw])
        changed = True

    return out if changed else grid


def _dedupe_duplicate_rows_in_da3_table(grid):
    """
    Fix 16 + 20: Drop long runs of OCR-identical rows; allow up to 2 consecutive rows with the
    same normalized Date|Description|Amount key (legitimate duplicate charges). Non-consecutive
    repeats are kept (each run capped separately).
    """
    if not grid or len(grid) < 2:
        return grid
    if not _is_da3_header(grid):
        return grid
    data_rows = []
    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        if not any(cells):
            continue
        data_rows.append(row)

    if not data_rows:
        return grid

    out = [grid[0]]
    i = 0
    while i < len(data_rows):
        row = data_rows[i]
        cells = [str(c or "").strip() for c in row]
        k = _transaction_row_dedupe_key(cells)
        j = i + 1
        while j < len(data_rows):
            ncells = [str(c or "").strip() for c in data_rows[j]]
            if _transaction_row_dedupe_key(ncells) != k:
                break
            j += 1
        run_len = j - i
        take = min(run_len, 2)
        for t in range(take):
            out.append(data_rows[i + t])
        i = j

    return out


def _row_text_full_ucb(grid, r: int) -> str:
    """Lowercased join of all cells in row (for UCB section split heuristics)."""
    if not grid or r < 0 or r >= len(grid):
        return ""
    parts = [str(c or "").strip().lower() for c in grid[r]]
    return " ".join(parts)


def _is_itm_deposit_row_ucb(grid, r: int) -> bool:
    if not grid or r >= len(grid) or len(grid[r]) < 2:
        return False
    desc = str(grid[r][1] or "").strip().lower()
    return "itm deposit" in desc


def _ucb_verify_electronic_credits_rows(grid, ec_start: int) -> bool:
    """POS Return, Acct Fund (Apple instant), DDA Transfer IN — PDF Electronic Credits block."""
    if ec_start + 2 >= len(grid):
        return False
    t0 = _row_text_full_ucb(grid, ec_start)
    t1 = _row_text_full_ucb(grid, ec_start + 1)
    t2 = _row_text_full_ucb(grid, ec_start + 2)
    ok0 = "pos return" in t0 or ("pos" in t0 and "return" in t0)
    ok1 = "acct fund" in t1 or ("apple" in t1 and "inst" in t1)
    ok2 = "dda transfer" in t2 and "in" in t2
    return bool(ok0 and ok1 and ok2)


def _ucb_split_grid_deposits_ec_ed(grid):
    """
    Return (grid_dep, grid_ec, grid_ed) each with same Date|Description|Amount header,
    or None if this does not look like the merged UCB page-3 pattern.
    """
    if not grid or len(grid) < 2:
        return None
    if not _is_da3_header(grid):
        return None
    data_start = 1
    i = data_start
    while i < len(grid) and _is_itm_deposit_row_ucb(grid, i):
        i += 1
    if i == data_start:
        return None
    if i + 3 > len(grid):
        return None
    if not _ucb_verify_electronic_credits_rows(grid, i):
        return None
    ec_end = i + 3
    if ec_end >= len(grid):
        return None
    hdr = [list(grid[0])]
    g_dep = hdr + grid[data_start:i]
    g_ec = hdr + grid[i:ec_end]
    g_ed = hdr + grid[ec_end:]
    if len(g_ed) <= 1:
        return None
    return g_dep, g_ec, g_ed


def _try_split_ucb_deposits_table_to_three(prefix: str, table_html: str):
    """
    Build HTML: Deposits (continued) + table, Electronic Credits + table, Electronic Debits + table.
    Returns None if split heuristics do not match.
    """
    try:
        p = TableGridParser()
        p.feed(table_html)
        grid = _build_grid(p.rows)
    except Exception:
        return None
    triple = _ucb_split_grid_deposits_ec_ed(grid)
    if not triple:
        return None
    g_dep, g_ec, g_ed = triple
    sect = '<div align="center">\n\n{title}\n\n</div>\n\n'
    parts = [prefix.rstrip(), _grid_to_html(g_dep), sect.format(title="Electronic Credits"), _grid_to_html(g_ec)]
    parts.append(sect.format(title="Electronic Debits"))
    parts.append(_grid_to_html(g_ed))
    return "\n\n".join(parts)


_UCB_DEP_CONT_BLOCK = re.compile(
    r'(<div\s+align=["\']center["\']\s*>\s*Deposits\s*\(continued\)\s*</div>\s*)'
    r'(<table[^>]*>.*?</table>)'
    r'(?:\s*<div\s+align=["\']center["\']\s*>\s*Electronic\s+Credits\s*</div>\s*'
    r'<div\s+align=["\']center["\']\s*>\s*Electronic\s+Debits\s*</div>)?',
    re.DOTALL | re.IGNORECASE,
)


def _split_ucb_deposits_electronic_sections_html(text: str) -> str:
    """
    Fix 17: Split merged UCB Deposits (continued) table into Deposits + Electronic Credits + Electronic Debits.
    Removes orphan empty Electronic Credits / Electronic Debits div pairs when replaced by real tables.
    Safe no-op when heuristics do not match.
    """
    if not text or "deposits (continued)" not in text.lower():
        return text

    def _repl(m):
        prefix = m.group(1)
        table_html = m.group(2)
        new = _try_split_ucb_deposits_table_to_three(prefix, table_html)
        if new is None:
            return m.group(0)
        return new

    return _UCB_DEP_CONT_BLOCK.sub(_repl, text)


def _parse_da3_grid_from_table_html(table_html: str):
    """Return grid if table is Date|Description|Amount; else None."""
    try:
        p = TableGridParser()
        p.feed(table_html)
        grid = _build_grid(p.rows)
        if grid and _is_da3_header(grid):
            return grid
    except Exception:
        pass
    return None


def _strip_orphan_continued_da3_flatline(text: str) -> str:
    """
    Fix 22: Remove one-line OCR dumps like
    '…(continued) DATE DESCRIPTION AMOUNT($) 04/09 … 04/11 … 239.55' that duplicate the
    following HTML table and pollute markdown.
    """
    if not text or "(continued)" not in text.lower():
        return text
    lines = text.splitlines(keepends=True)
    out = []
    for line in lines:
        core = line.rstrip("\r\n")
        date_hits = len(re.findall(r"\d{2}/\d{2}", core)) + len(
            re.findall(r"\b\d{2}-\d{2}-\d{2}\b", core)
        )
        if (
            len(core) > 80
            and "(continued)" in core.lower()
            and "description" in core.lower()
            and "amount" in core.lower()
            and date_hits >= 3
        ):
            continue
        out.append(line)
    return "".join(out)


def _patch_da3_msft_g_ref_amounts_across_tables(text: str) -> str:
    """
    Fix 21: Build MICROSOFT#G… -> strict Amount from any DA3 row, then patch other DA3 rows
    with the same id whose Amount cell is not strict currency (junk fused into Amount).
    """
    if not text or "<table" not in text.lower():
        return text
    if "microsoft#" not in text.lower():
        return text

    tbl_pat = re.compile(r"<table[^>]*>.*?</table>", re.DOTALL | re.IGNORECASE)

    def _collect():
        g_map = {}
        ambiguous = set()
        for m in tbl_pat.finditer(text):
            g = _parse_da3_grid_from_table_html(m.group(0))
            if not g:
                continue
            for row in g[1:]:
                cells = [str(c or "").strip() for c in row]
                while len(cells) < 3:
                    cells.append("")
                c0, c1, c2 = cells[0], cells[1], cells[2]
                if not _DA3_LEADING_DATE.match(c0):
                    continue
                if not _DA3_STRICT_AMOUNT.match(c2):
                    continue
                blob = f"{c0} {c1} {c2}"
                for gid in _MSFT_PIN_G_REF.findall(blob):
                    k = gid.upper()
                    if k in ambiguous:
                        continue
                    trip = (c0, c1, c2)
                    prev = g_map.get(k)
                    if prev is not None and prev != trip:
                        ambiguous.add(k)
                        g_map.pop(k, None)
                    elif prev is None:
                        g_map[k] = trip
        return g_map, ambiguous

    g_map, ambiguous = _collect()
    if not g_map:
        return text

    out_chunks = []
    last = 0
    changed_any = False
    for m in tbl_pat.finditer(text):
        out_chunks.append(text[last : m.start()])
        table_html = m.group(0)
        grid = _parse_da3_grid_from_table_html(table_html)
        if not grid:
            out_chunks.append(table_html)
            last = m.end()
            continue
        new_grid = [list(grid[0])]
        changed = False
        for row in grid[1:]:
            cells = [str(c or "").strip() for c in row]
            while len(cells) < 3:
                cells.append("")
            c0, c1, c2 = cells[0], cells[1], cells[2]
            if not _DA3_LEADING_DATE.match(c0):
                new_grid.append(row)
                continue
            if _DA3_STRICT_AMOUNT.match(c2):
                new_grid.append(row)
                continue
            blob = f"{c0} {c1} {c2}"
            ids = _MSFT_PIN_G_REF.findall(blob)
            if len(ids) != 1:
                new_grid.append(row)
                continue
            k = ids[0].upper()
            if k in ambiguous or k not in g_map:
                new_grid.append(row)
                continue
            d0, d1, d2 = g_map[k]
            new_grid.append([d0, d1, d2])
            changed = True
        if changed:
            out_chunks.append(_grid_to_html(new_grid))
            changed_any = True
        else:
            out_chunks.append(table_html)
        last = m.end()
    out_chunks.append(text[last:])
    return "".join(out_chunks) if changed_any else text


def _trim_adjacent_da3_suffix_duplicating_next_table_prefix(text: str) -> str:
    """
    Remove trailing data rows from a DA3 table when they are repeated as the opening
    rows of the immediately following DA3 table (OCR/LLM glued the next section onto
    the prior table). Requires overlap length >= 2 and leaves at least one data row
    in the first table. Safe no-op when patterns do not match.
    """
    if not text or "<table" not in text.lower():
        return text

    pattern = re.compile(r"<table[^>]*>.*?</table>", re.DOTALL | re.IGNORECASE)

    for _ in range(48):
        matches = list(pattern.finditer(text))
        if len(matches) < 2:
            break
        changed = False
        for i in range(len(matches) - 1):
            g1 = _parse_da3_grid_from_table_html(matches[i].group(0))
            g2 = _parse_da3_grid_from_table_html(matches[i + 1].group(0))
            if not g1 or not g2 or len(g1) < 3 or len(g2) < 2:
                continue
            d1 = g1[1:]
            d2 = g2[1:]
            if len(d1) < 2 or len(d2) < 2:
                continue
            max_k = min(len(d1), len(d2))
            best_k = 0
            for k in range(max_k, 1, -1):
                ok = True
                for j in range(k):
                    c1 = [str(x or "").strip() for x in d1[-k + j][:3]]
                    c2 = [str(x or "").strip() for x in d2[j][:3]]
                    while len(c1) < 3:
                        c1.append("")
                    while len(c2) < 3:
                        c2.append("")
                    if _transaction_row_dedupe_key(c1) != _transaction_row_dedupe_key(c2):
                        ok = False
                        break
                if ok:
                    best_k = k
                    break
            if best_k < 2:
                continue
            if len(d1) - best_k < 1:
                continue
            new_grid = [g1[0]] + d1[:-best_k]
            new_html = _grid_to_html(new_grid)
            m = matches[i]
            text = text[: m.start()] + new_html + text[m.end() :]
            changed = True
            break
        if not changed:
            break
    return text


def _dedupe_subset_transaction_tables_html(text: str) -> str:
    """
    Remove duplicate 3-column Date|Description|Amount tables:
      - Later table is a strict subset of an earlier one → drop later (original).
      - Earlier table is a strict subset of a later one → drop earlier (UCB-style
        "Deposits (continued)" preview before the same rows appear in the full list).
      - Identical row sets → drop the later table.
      - Fuzzy overlap (≥92% of later rows match earlier) → drop later (Electronic Debits vs main).
      - Fix 23: multiset overlap on (date, amount) only — drop when OCR varies description
        between two copies of the same section (e.g. withdrawals pasted twice).
      - Runs multiple passes until stable.
    Row matching uses normalized keys so OCR variants (e.g. "0 27" vs "027", backticks)
    do not prevent duplicate tables from being detected.
    """
    if not text or "<table" not in text.lower():
        return text

    pattern = re.compile(r"<table[^>]*>.*?</table>", re.DOTALL | re.IGNORECASE)

    def _parse_grid(html: str):
        try:
            p = TableGridParser()
            p.feed(html)
            return _build_grid(p.rows)
        except Exception:
            return None

    def _row_set(g):
        s = set()
        for r in g[1:]:
            if not any(str(c or "").strip() for c in r):
                continue
            cells = [str(c or "").strip() for c in r]
            if not any(cells):
                continue
            s.add(_transaction_row_dedupe_key(cells))
        return s

    def _one_pass(t: str) -> str:
        matches = list(pattern.finditer(t))
        if len(matches) < 2:
            return t

        grids = []
        for m in matches:
            grids.append((m, _parse_grid(m.group(0))))

        drop_idx = set()
        for i in range(1, len(grids)):
            if i in drop_idx:
                continue
            mi, gi = grids[i]
            if not _is_da3_header(gi):
                continue
            si = _row_set(gi)
            if not si:
                continue

            for j in range(i):
                if j in drop_idx:
                    continue
                mj, gj = grids[j]
                if not _is_da3_header(gj):
                    continue
                sj = _row_set(gj)
                if not sj:
                    continue

                # Earlier j fully contained in later i (preview + full list) → drop earlier.
                if sj <= si and len(sj) < len(si):
                    drop_idx.add(j)
                    continue

                # Later i strict subset of earlier j → drop later (e.g. Electronic Debits).
                if si <= sj and len(si) < len(sj):
                    drop_idx.add(i)
                    break

                if si == sj:
                    drop_idx.add(i)
                    break

                inter = si & sj
                # Later table's rows almost all appear in earlier table (OCR drift).
                ri = len(inter) / len(si) if si else 0.0
                rj = len(inter) / len(sj) if sj else 0.0
                if ri >= 0.88 and len(si) <= len(sj):
                    drop_idx.add(i)
                    break
                if rj >= 0.88 and len(sj) < len(si):
                    drop_idx.add(j)
                    continue

                # Fix 23: multiset (date, amount) overlap — duplicate tables when description OCR differs.
                ci = _row_counter_loose_da3(gi)
                cj = _row_counter_loose_da3(gj)
                ni, nj = sum(ci.values()), sum(cj.values())
                if ni >= 6 and nj >= 6:
                    oi_j = _overlap_ratio_counter(ci, cj)
                    oj_i = _overlap_ratio_counter(cj, ci)
                    if oi_j >= 0.88 and oj_i >= 0.88:
                        drop_idx.add(i)
                        break
                    if _multiset_leq(ci, cj) and ni < nj and oi_j >= 0.9:
                        drop_idx.add(i)
                        break
                    if _multiset_leq(cj, ci) and nj < ni and oj_i >= 0.9:
                        drop_idx.add(j)
                        continue

        if not drop_idx:
            return t

        out = []
        last = 0
        for idx, m in enumerate(matches):
            out.append(t[last : m.start()])
            if idx not in drop_idx:
                out.append(m.group(0))
            last = m.end()
        out.append(t[last:])
        return "".join(out)

    out = text
    for _ in range(12):
        nxt = _one_pass(out)
        if nxt == out:
            break
        out = nxt
    return out


_RE_CENTER_DIV_TWO_DA3_TABLES = re.compile(
    r'(<div\s+align=["\']center["\']\s*>\s*[^<]*?</div>\s*)'
    r'(<table[^>]*>.*?</table>)'
    r'(\s*<table[^>]*>.*?</table>)',
    re.IGNORECASE | re.DOTALL,
)


def _da3_row_credit_vs_debit_category(desc: str) -> str:
    """
    Generic classification for Date|Description|Amount rows (middle column only).
    Used to detect OCR pasting a withdrawals table after a deposits heading.
    """
    d = (desc or "").lower()
    if "debit card return" in d:
        return "credit"
    if "debit card" in d and "return" in d:
        return "credit"
    if "deposit" in d and "debit card" not in d[:60]:
        return "credit"
    if "incoming wire" in d or "wire ref" in d:
        return "credit"
    if "zelle business reversal" in d or ("reversal" in d and "zelle" in d):
        return "credit"
    if "lrm claims" in d or ("claims" in d and "adj" in d):
        return "credit"
    # Credits / tax refunds that omit the word "deposit" (common on statement continuations)
    if "adp tax" in d and "customer" in d:
        return "credit"
    if "debit card" in d:
        return "debit"
    if "ach corp debit" in d:
        return "debit"
    if "bus online ach settlement" in d:
        return "debit"
    if "zelle business payment to" in d:
        return "debit"
    if "visa money transfer debit" in d:
        return "debit"
    if "telephone payment" in d or "service charges" in d:
        return "debit"
    if "merch fee" in d:
        return "debit"
    if "deposit" in d:
        return "credit"
    return "unknown"


def _da3_table_credit_debit_fractions(grid):
    """Returns (debit_frac, credit_frac, unknown_frac, scored_row_count)."""
    deb = cre = unk = 0
    if not grid or len(grid) < 2:
        return 0.0, 0.0, 0.0, 0
    for row in grid[1:]:
        cells = [str(c or "").strip() for c in row]
        while len(cells) < 3:
            cells.append("")
        if not _DA3_LEADING_DATE.match(cells[0]):
            continue
        if not _DA3_STRICT_AMOUNT.match((cells[2] or "").strip()):
            continue
        cat = _da3_row_credit_vs_debit_category(cells[1])
        if cat == "debit":
            deb += 1
        elif cat == "credit":
            cre += 1
        else:
            unk += 1
    tot = deb + cre + unk
    if tot == 0:
        return 0.0, 0.0, 0.0, 0
    return deb / tot, cre / tot, unk / tot, tot


def _drop_da3_misplaced_debit_after_deposit_heading_tables(text: str) -> str:
    """
    Fix 25: PDF order is withdrawals → then deposits. OCR sometimes emits two DA3 tables
    after the deposits heading: real deposits, then a duplicate withdrawals block.
    Drop the second table when it is overwhelmingly debit-like and the first is credit-like.
    Repeats until no more matches (multiple duplicate blocks).
    """
    if not text or "<table" not in text.lower():
        return text
    if "deposit" not in text.lower():
        return text

    for _ in range(12):
        replaced = False
        pos = 0
        while True:
            m = _RE_CENTER_DIV_TWO_DA3_TABLES.search(text, pos)
            if not m:
                break
            div = m.group(1)
            t1 = m.group(2)
            t2 = m.group(3)
            sl = div.lower()
            if "deposit" not in sl or not any(k in sl for k in ("credit", "interest", "credits")):
                pos = m.start() + 1
                continue

            g1 = _parse_da3_grid_from_table_html(t1)
            g2 = _parse_da3_grid_from_table_html(t2)
            if not g1 or not g2 or not _is_da3_header(g1) or not _is_da3_header(g2):
                pos = m.start() + 1
                continue

            db1, cr1, unk1, n1 = _da3_table_credit_debit_fractions(g1)
            db2, cr2, _unk2, n2 = _da3_table_credit_debit_fractions(g2)
            if n1 < 5 or n2 < 10:
                pos = m.start() + 1
                continue
            # First table: deposits often have ADP/merchant lines scored as unknown — blend unk toward credit.
            credit_like_first = cr1 + 0.42 * unk1
            if credit_like_first < 0.30 and credit_like_first <= db1 + 0.05:
                pos = m.start() + 1
                continue
            if db2 < 0.74 or db2 <= cr2 + 0.07:
                pos = m.start() + 1
                continue

            text = text[: m.start()] + div + t1 + text[m.end() :]
            replaced = True
            break
        if not replaced:
            break

    return text


def stabilize_tables_and_text(page_md: str) -> str:
    if not page_md:
        return page_md

    page_md = normalize_money_glyphs(page_md)

    blocks = re.split(r"\n\s*\n", page_md.strip())
    out_blocks = []
    for b in blocks:
        if looks_like_markdown_table(b):
            out_blocks.append(md_table_to_html(b))
        else:
            out_blocks.append(b)

    stabilized = "\n\n".join(out_blocks)
    stabilized = convert_plaintext_bank_sections(stabilized)
    stabilized = _strip_orphan_continued_da3_flatline(stabilized)
    stabilized = normalize_html_tables(stabilized)
    stabilized = _trim_adjacent_da3_suffix_duplicating_next_table_prefix(stabilized)
    stabilized = _split_ucb_deposits_electronic_sections_html(stabilized)
    stabilized = _patch_da3_msft_g_ref_amounts_across_tables(stabilized)
    stabilized = _dedupe_subset_transaction_tables_html(stabilized)
    stabilized = _drop_da3_misplaced_debit_after_deposit_heading_tables(stabilized)
    stabilized = _strip_orphan_continued_da3_flatline(stabilized)
    return close_unclosed_html(stabilized)

# --------------------------
# PDF rendering with padding
# --------------------------
def render_pdf_pages_to_images(pdf_path: str) -> Tuple[List[str], List[int]]:
    import pymupdf as fitz
    from PIL import Image, ImageEnhance

    doc = fitz.open(pdf_path)
    page_images: List[str] = []
    page_heights: List[int] = []

    for i in range(len(doc)):
        page = doc[i]
        pix = page.get_pixmap(matrix=fitz.Matrix(RENDER_SCALE, RENDER_SCALE), alpha=False)

        img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)

        if ENABLE_CONTRAST:
            img = ImageEnhance.Contrast(img).enhance(1.12)

        w, h = img.size
        pad_l = int(w * PAD_LEFT_FRAC)
        pad_r = int(w * PAD_RIGHT_FRAC)
        pad_t = int(h * PAD_TOP_FRAC)
        pad_b = int(h * PAD_BOTTOM_FRAC)

        if any(p > 0 for p in (pad_l, pad_r, pad_t, pad_b)):
            canvas = Image.new("RGB", (w + pad_l + pad_r, h + pad_t + pad_b), (255, 255, 255))
            canvas.paste(img, (pad_l, pad_t))
            img = canvas

        img_path = os.path.join(tempfile.gettempdir(), f"glmocr_page_{os.getpid()}_{i}.png")
        img.save(img_path, "PNG", compress_level=6)
        page_images.append(img_path)
        page_heights.append(img.height)

    doc.close()
    return page_images, page_heights

# --------------------------
# GLM-OCR result extraction
# --------------------------
def get_page_md_and_regions(page_result):
    md = ""
    if hasattr(page_result, "markdown_result") and page_result.markdown_result:
        md = (page_result.markdown_result or "").strip()
    regions = []
    if hasattr(page_result, "json_result"):
        jr = page_result.json_result
        if isinstance(jr, dict) and "regions" in jr:
            regions = jr.get("regions") or []
        elif isinstance(jr, list) and len(jr) > 0:
            r = jr[0] if isinstance(jr[0], list) else jr
            if isinstance(r, list):
                regions = r
            elif isinstance(r, dict) and "regions" in r:
                regions = r.get("regions") or []
    return md, regions

# --------------------------
# Main entry
# --------------------------
def run_ocr(uploaded_file):
    if uploaded_file is None:
        return "Please upload a file."

    page_images = []
    try:
        path = uploaded_file.name if hasattr(uploaded_file, "name") else str(uploaded_file)
        is_pdf = path.lower().endswith(".pdf")
        is_nfcu_doc = _is_nfcu_document(path) if is_pdf else False
        parser = get_parser()

        page_heights = []

        if is_pdf:
            page_images, page_heights = render_pdf_pages_to_images(path)
            results = parser.parse(page_images)
        else:
            page_images = [path]
            page_heights = [1000]
            results = parser.parse(path)

        if not isinstance(results, list):
            results = [results]

        all_pages = []
        for page_num, page_result in enumerate(results):
            page_md, regions = get_page_md_and_regions(page_result)
            img_h = page_heights[page_num] if page_num < len(page_heights) else 1000
            header_end_frac, footer_start_frac = get_header_footer_zones(regions, img_h)

            he = header_end_frac if header_end_frac is not None else DEFAULT_ZONE_FRAC
            fs = footer_start_frac if footer_start_frac is not None else (1.0 - DEFAULT_ZONE_FRAC)

            he = max(0.02, min(0.25, he))
            fs = max(0.75, min(0.98, fs))

            parts = []

            # Keep GLM-OCR as the primary parser for all pages.
            # Navy-specific helpers run only as additive post-processing.

            # Header inclusion: PDF text -> band words -> OCR band
            hdr = ""
            if is_pdf:
                hdr = extract_zone_text_pdf(path, page_num, 0, he)
                if not (hdr and hdr.strip()):
                    hdr = extract_pdf_text_in_band(path, page_num, 0, PDF_HEADER_BAND_FRAC)
            if not (hdr and hdr.strip()) and page_num < len(page_images):
                hdr = ocr_zone(page_images[page_num], 0, he)
            if hdr and hdr.strip():
                parts.append(normalize_html_tables(fix_account_number(normalize_money_glyphs(hdr.strip()))))

            # Main OCR body: stabilize then patch missing duplicate rows from text layer
            if page_md and page_md.strip():
                stabilized = stabilize_tables_and_text(page_md.strip())
                # Fix 4: restore any rows OCR dropped by cross-checking the PDF text layer.
                # Safe no-op for scanned PDFs (no text layer) and non-transaction pages.
                if is_pdf and not is_nfcu_doc:
                    stabilized = _patch_ocr_with_textlayer(stabilized, path, page_num)
                    stabilized = _strip_orphan_continued_da3_flatline(stabilized)
                    # _patch_ocr_with_textlayer may append Case B rows from the PDF text layer
                    # with fused UCB-style cells; run the same HTML table pass again (Fix 13/14).
                    stabilized = normalize_html_tables(stabilized)
                    stabilized = _trim_adjacent_da3_suffix_duplicating_next_table_prefix(stabilized)
                    stabilized = _split_ucb_deposits_electronic_sections_html(stabilized)
                    stabilized = _patch_da3_msft_g_ref_amounts_across_tables(stabilized)
                    stabilized = _dedupe_subset_transaction_tables_html(stabilized)
                    stabilized = _drop_da3_misplaced_debit_after_deposit_heading_tables(stabilized)
                elif is_pdf and is_nfcu_doc:
                    # Navy-only additive fix: rebuild malformed summary table from
                    # text layer, but keep GLM body/header/footer unchanged.
                    nfcu_summary = _extract_nfcu_summary_table(path, page_num)
                    if nfcu_summary:
                        stabilized = _replace_nfcu_summary_table(stabilized, nfcu_summary)

                # Fix 4D: append Johnson Bank Checks + Daily Account Balance
                # Run OUTSIDE _patch_ocr_with_textlayer so exceptions there don't block it.
                if is_pdf:
                    try:
                        needs_checks = 'class="jb-summary-checks"' not in stabilized
                        needs_dab    = "daily account balance" not in stabilized.lower()
                        if needs_checks or needs_dab:
                            # Try pdfminer first (column-aware), fall back to pymupdf
                            _txt4d = ""
                            try:
                                from pdfminer.high_level import extract_text as _pm_extract
                                _txt4d = _pm_extract(path, page_numbers=[page_num])
                            except Exception:
                                pass
                            if not _txt4d:
                                try:
                                    import pymupdf as _fitz4d
                                    _d4 = _fitz4d.open(path)
                                    _txt4d = _d4[page_num].get_text()
                                    _d4.close()
                                except Exception:
                                    pass
                            if _txt4d:
                                jb_extra = _extract_jb_summary_sections(_txt4d, needs_checks, needs_dab)
                                if jb_extra:
                                    stabilized = stabilized.rstrip() + "\n\n" + jb_extra
                    except Exception:
                        pass  # safe no-op — never break other PDFs

                parts.append(stabilized)

            # Footer extraction: PDF text layer -> band words -> OCR crop
            # Deduplication guard prevents double-printing when body OCR already captured it.
            if ENABLE_FOOTER_OCR and page_num < len(page_images):
                ftr = ""
                if is_pdf:
                    ftr = extract_zone_text_pdf(path, page_num, fs, 1.0)
                    if not (ftr and ftr.strip()):
                        ftr = extract_pdf_text_in_band(path, page_num, PDF_FOOTER_BAND_FRAC, 1.0)
                if not (ftr and ftr.strip()):
                    ftr = ocr_zone(page_images[page_num], fs, 1.0)
                if ftr and ftr.strip():
                    ftr_clean = normalize_money_glyphs(ftr.strip())

                    # Guard 1: first-line dedup (original check)
                    ftr_first_line = next(
                        (ln.strip().lower() for ln in ftr_clean.splitlines() if ln.strip()),
                        ""
                    )
                    already_present = ftr_first_line and any(
                        ftr_first_line in part.lower() for part in parts
                    )

                    # Guard 2: suppress footer that is a raw text-layer dump of
                    # transaction rows (e.g. East West Bank two-column layout).
                    # Detected when the footer contains 3+ date tokens (MM/DD or MM-DD)
                    # AND the footer itself contains amounts — meaning it is transaction
                    # data, not a legitimate page footer like an address or disclaimer.
                    _footer_date_re = re.compile(r"\b\d{1,2}[-/]\d{2}\b")
                    _footer_amt_re  = re.compile(r"\b\d{1,3}(?:,\d{3})*\.\d{2}\b")
                    _date_hits = len(_footer_date_re.findall(ftr_clean))
                    _amt_hits  = len(_footer_amt_re.findall(ftr_clean))
                    is_txn_dump = _date_hits >= 3 and _amt_hits >= 3

                    if not already_present and not is_txn_dump:
                        parts.append(ftr_clean)

            if parts:
                all_pages.append("\n\n".join(parts))

        merged = "\n\n---page-separator---\n\n".join(all_pages) if all_pages else "(No content)"
        # Cross-page: UCB section split + same transaction tables dedupe once on full doc.
        if all_pages and merged and not merged.startswith("Error") and "<table" in merged.lower():
            merged = _strip_orphan_continued_da3_flatline(merged)
            merged = _trim_adjacent_da3_suffix_duplicating_next_table_prefix(merged)
            merged = _split_ucb_deposits_electronic_sections_html(merged)
            merged = _patch_da3_msft_g_ref_amounts_across_tables(merged)
            merged = _dedupe_subset_transaction_tables_html(merged)
            merged = _drop_da3_misplaced_debit_after_deposit_heading_tables(merged)
            merged = _strip_orphan_continued_da3_flatline(merged)
        return merged

    except Exception as e:
        import traceback
        log.exception("run_ocr failed: %s", e)
        return f"Error: {e}\n\n{traceback.format_exc()}"

    finally:
        for p in page_images:
            try:
                if isinstance(p, str) and p.endswith(".png") and "glmocr_page_" in os.path.basename(p):
                    os.unlink(p)
            except Exception:
                pass

with gr.Blocks(title="GLM-OCR") as demo:
    gr.Markdown("# GLM-OCR\nUpload a PDF or image. Headers included; tables stabilized.")
    file_in = gr.File(label="Upload PDF or image", file_types=[".pdf", ".png", ".jpg", ".jpeg", ".tiff", ".bmp"])
    run_btn = gr.Button("Run OCR", variant="primary")
    out = gr.Textbox(lines=40, label="Output (markdown)")
    run_btn.click(fn=run_ocr, inputs=file_in, outputs=out)

if __name__ == "__main__":
    demo.launch()