"""Batch upload proofread training corpus to style DB.

Two-phase workflow:
  --preview    Extract metadata from all .md files, print review table, don't upload
  --upload     Actually upload all files (with optional --only FILE to run one)

Metadata extraction:
  * decision_number: from filename (ARAR-YY-NNNN / ערר NNNN-YY) or decision date year
  * decision_date:   from "ניתנה ... <day> ב<Hebrew month> <YYYY>" near end of text
  * categories:      keyword heuristics on body text
"""

from __future__ import annotations

import argparse
import asyncio
import os
import re
import sys
from pathlib import Path

PROOFREAD_DIR = Path("/home/chaim/legal-ai/data/training/proofread")

# Manual metadata overrides for files where auto-extraction can't determine values.
METADATA_OVERRIDES: dict[str, dict] = {
    "ARAR-25-1067 - יחיעם יפה ואח׳.md": {
        "decision_date": "2025-11-27",  # no "ניתנה" signature in file; user-provided
    },
}

# Files to skip — already in style_corpus from legacy ingestion
# (verified by exact character-count match with existing DB rows).
SKIP_FILES = {
    "תמא 38-בית הכרם-1126+1141-החלטה.md",       # → corpus: 1126/1141
    "היתר בניה-בית שמש-1180+1181-החלטה.md",     # → corpus: 1180/1181
    "היתר בניה-הראל-1043+1054-החלטה.md",        # → corpus: 1043/1054
    "היתר בניה-הראל-1071+1077-החלטה.md",        # → corpus: 1071/1077
}

# Load env vars needed by mcp-server
ENV_FILE = Path.home() / ".env"
if ENV_FILE.exists():
    for line in ENV_FILE.read_text().splitlines():
        if "=" in line and not line.startswith("#"):
            k, v = line.split("=", 1)
            os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'"))

# Make mcp-server package importable
sys.path.insert(0, "/home/chaim/legal-ai/mcp-server/src")


# ── Decision number extraction ───────────────────────────────────

FILENAME_NUMBER_PATTERNS = [
    # ARAR-YY-NNNN[-X] - title.md
    re.compile(r"^ARAR-(\d{2})-(\d{3,4})"),
    # ערר NNNN-YY title.md  or  ערר NNNN-YY title
    re.compile(r"^ערר\s+(\d{3,4})-(\d{2})"),
    # ערר NNNN - title (no year in filename — needs date lookup)
    re.compile(r"^ערר\s+(\d{3,4})\s*-"),
]

LEGACY_MULTI_PATTERN = re.compile(r"(\d{3,4})\+(\d{3,4})")


def decision_number_from_filename(stem: str) -> tuple[str | None, str | None]:
    """Return (number, year_short) or (multi_number, None) or (None, None).

    year_short is YY (last 2 digits) if extractable from filename.
    For legacy files with 'NNNN+NNNN' or no year, returns partial info
    that must be completed from decision date.
    """
    # ARAR-YY-NNNN
    m = FILENAME_NUMBER_PATTERNS[0].match(stem)
    if m:
        year, num = m.group(1), m.group(2)
        return f"{num}/{year}", year

    # ערר NNNN-YY
    m = FILENAME_NUMBER_PATTERNS[1].match(stem)
    if m:
        num, year = m.group(1), m.group(2)
        return f"{num}/{year}", year

    # ערר NNNN - title (no year)
    m = FILENAME_NUMBER_PATTERNS[2].match(stem)
    if m:
        num = m.group(1)
        return f"{num}/??", None

    # Legacy: "NNNN+NNNN" merged decisions
    m = LEGACY_MULTI_PATTERN.search(stem)
    if m:
        return f"{m.group(1)}+{m.group(2)}/??", None

    return None, None


# ── Decision date extraction ─────────────────────────────────────

HEBREW_MONTHS = {
    "ינואר": 1, "בינואר": 1,
    "פברואר": 2, "בפברואר": 2,
    "מרץ": 3, "מרס": 3, "במרץ": 3, "במרס": 3,
    "אפריל": 4, "באפריל": 4,
    "מאי": 5, "במאי": 5,
    "יוני": 6, "ביוני": 6,
    "יולי": 7, "ביולי": 7,
    "אוגוסט": 8, "באוגוסט": 8,
    "ספטמבר": 9, "בספטמבר": 9,
    "אוקטובר": 10, "באוקטובר": 10,
    "נובמבר": 11, "בנובמבר": 11,
    "דצמבר": 12, "בדצמבר": 12,
}

# Matches "<day> ב<month>, <year>" or "<day> <month>, <year>" (with optional commas)
DATE_RE = re.compile(
    r"(\d{1,2})\s+(ב?(?:ינואר|פברואר|מרץ|מרס|אפריל|מאי|יוני|יולי|אוגוסט|ספטמבר|אוקטובר|נובמבר|דצמבר))\s*[,.]?\s*(\d{4})"
)

NITNA_RE = re.compile(r"ניתנ[הו]?\s+(?:פה\s+אחד|בדעת\s+רוב|היום)?")


def decision_date_from_text(text: str) -> str | None:
    """Extract decision date in YYYY-MM-DD format from 'ניתנה... DATE' section.

    Searches the last ~2000 chars where the signing block lives.
    """
    tail = text[-2500:] if len(text) > 2500 else text

    # Prefer dates near "ניתנה" marker
    nitna_match = NITNA_RE.search(tail)
    search_text = tail[nitna_match.start():] if nitna_match else tail

    m = DATE_RE.search(search_text)
    if not m:
        # Fall back: search whole tail
        m = DATE_RE.search(tail)
    if not m:
        return None

    day = int(m.group(1))
    month = HEBREW_MONTHS.get(m.group(2))
    year = int(m.group(3))
    if not month:
        return None
    try:
        from datetime import date
        return date(year, month, day).isoformat()
    except ValueError:
        return None


# ── Subject category extraction ──────────────────────────────────

# Categories as defined in the tool signature.
ALL_CATEGORIES = [
    "בנייה", "שימוש חורג", "תכנית", "היתר", "הקלה",
    "חלוקה", 'תמ"א 38', "היטל השבחה", "פיצויים 197",
]


def categorize(text: str) -> list[str]:
    """Heuristic category detection based on subject matter, not incidental mentions.

    Strategy: the real subject is established in the opening 2000 chars
    (first decision-opening paragraph). Secondary signal is repetition count
    — casual mentions in law citations don't repeat.
    """
    opening = text[:2000]  # subject is stated up front
    t = text

    cats: list[str] = []

    # תמ"א 38 — very specific marker, single mention is fine
    if re.search(r'תמ[״"\']?א\s*38|תמא\s*38', t):
        cats.append('תמ"א 38')

    # היטל השבחה — require real engagement: must appear in opening OR 3+ times
    hsbacha_count = len(re.findall(r"היטל(?:י)?\s+השבחה", t))
    if hsbacha_count >= 3 or re.search(r"היטל(?:י)?\s+השבחה", opening):
        cats.append("היטל השבחה")

    # פיצויים 197 — require multiple mentions OR in opening
    p197_re = r"פיצויים\s+לפי\s+(?:ס(?:עיף|')\s*)?197|סעיף\s*197|ס['\"]?\s*197"
    p197_count = len(re.findall(p197_re, t))
    if p197_count >= 2 or re.search(p197_re, opening):
        cats.append("פיצויים 197")

    # שימוש חורג — must appear in opening OR 3+ times (avoids law-quote false positives)
    shimush_count = t.count("שימוש חורג")
    if shimush_count >= 3 or "שימוש חורג" in opening:
        cats.append("שימוש חורג")

    # הקלה — real subject if 3+ mentions AND appears in opening
    hakala_count = len(re.findall(r"\bהקלה\b|\bהקלות\b", t))
    if hakala_count >= 3 and re.search(r"\bהקלה\b|\bהקלות\b", opening):
        cats.append("הקלה")

    # חלוקה — "איחוד וחלוקה" or "חלוקה חדשה" (specific phrases)
    if re.search(r"איחוד\s+וחלוקה|חלוקה\s+חדשה|תכנית\s+לחלוקה", t):
        cats.append("חלוקה")

    # תכנית — plan-level appeal (primary subject). Allow ה/ב/ל prefixes on תכנית.
    tochnit_opening = bool(re.search(
        r"הפקדת\s+ה?תכנית|"
        r"אישור\s+ה?תכנית|"
        r"המלצה\s+להפקיד|"
        r"להפקיד\s+את\s+ה?תכנית|"
        r"לדון\s+בתכנית|"
        r"דנה\s+בתכנית|"
        r"החלטה\s+לאשר\s+ה?תכנית",
        opening,
    ))
    if tochnit_opening:
        cats.append("תכנית")

    # היתר — "בקשה להיתר" or "היתר בניה" as subject in opening
    if re.search(r"בקשה\s+להיתר|היתר\s+בני(?:י)?ה", opening):
        cats.append("היתר")

    # בנייה — default/fallback for building-permit cases
    # (not for plan-level תכנית-only cases)
    has_permit_subject = "היתר" in cats or "הקלה" in cats or 'תמ"א 38' in cats
    if has_permit_subject and "בנייה" not in cats:
        cats.append("בנייה")

    # If nothing matched, default to בנייה
    return cats or ["בנייה"]


# ── Year fallback from date ──────────────────────────────────────


def finalize_decision_number(number: str | None, date_iso: str | None) -> str:
    """If filename number is missing year, fill it from decision date."""
    if not number:
        if date_iso:
            # Extract last 2 digits of Hebrew year via Gregorian year
            return f"??/{date_iso[2:4]}"
        return ""
    if number.endswith("/??"):
        if date_iso:
            yy = date_iso[2:4]
            return number.replace("/??", f"/{yy}")
        return number.replace("/??", "")
    return number


# ── Main metadata extraction ─────────────────────────────────────


def extract_metadata(path: Path) -> dict:
    text = path.read_text(encoding="utf-8")
    num_from_name, _ = decision_number_from_filename(path.stem)
    date_iso = decision_date_from_text(text)
    decision_number = finalize_decision_number(num_from_name, date_iso)
    cats = categorize(text)
    meta = {
        "file": path.name,
        "decision_number": decision_number,
        "decision_date": date_iso or "??",
        "categories": cats,
        "chars": len(text),
    }
    # Apply manual overrides
    if path.name in METADATA_OVERRIDES:
        meta.update(METADATA_OVERRIDES[path.name])
    return meta


def print_preview(results: list[dict]) -> None:
    """Print review table of metadata for all files."""
    print(f"\n{'#':<3} {'FILE':<55} {'NUMBER':<15} {'DATE':<12} {'CATEGORIES'}")
    print("-" * 130)
    for i, r in enumerate(results, 1):
        file_short = r["file"] if len(r["file"]) <= 53 else r["file"][:50] + "..."
        cats = ", ".join(r["categories"])
        print(f"{i:<3} {file_short:<55} {r['decision_number']:<15} {r['decision_date']:<12} {cats}")
    print()
    # Highlight issues
    issues = [r for r in results if r["decision_date"] == "??" or not r["decision_number"] or "??" in r["decision_number"]]
    if issues:
        print(f"⚠️  {len(issues)} files with incomplete metadata:")
        for r in issues:
            print(f"   - {r['file']}  → number={r['decision_number']!r} date={r['decision_date']!r}")


# ── Upload ───────────────────────────────────────────────────────


async def upload_one(meta: dict) -> dict:
    from legal_mcp.tools.documents import document_upload_training

    path = PROOFREAD_DIR / meta["file"]
    result = await document_upload_training(
        file_path=str(path),
        decision_number=meta["decision_number"],
        decision_date=meta["decision_date"] if meta["decision_date"] != "??" else "",
        subject_categories=meta["categories"],
        title=path.stem,
    )
    return {"file": meta["file"], "result": result}


async def upload_all(results: list[dict]) -> None:
    for i, meta in enumerate(results, 1):
        try:
            r = await upload_one(meta)
            print(f"[{i}/{len(results)}] ✓ {meta['file']}")
            print(f"    {r['result'][:200]}")
        except Exception as e:
            print(f"[{i}/{len(results)}] ✗ {meta['file']}: {e}")


# ── CLI ──────────────────────────────────────────────────────────


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--preview", action="store_true", help="Show metadata table without uploading")
    ap.add_argument("--upload", action="store_true", help="Upload all files to style corpus")
    ap.add_argument("--only", help="Only process this specific filename")
    args = ap.parse_args()

    files = sorted(PROOFREAD_DIR.glob("*.md"))
    files = [f for f in files if f.name not in SKIP_FILES]
    if args.only:
        files = [f for f in files if f.name == args.only]
        if not files:
            print(f"File not found: {args.only}")
            return 1

    results = [extract_metadata(f) for f in files]

    if args.preview or not args.upload:
        print_preview(results)
        if not args.upload:
            return 0

    if args.upload:
        print(f"\n>>> Uploading {len(results)} files to style corpus...\n")
        asyncio.run(upload_all(results))

    return 0


if __name__ == "__main__":
    sys.exit(main())