"""
Inference-time gate for the relevance scorer.

PURPOSE
-------
Some procurement notices have descriptions like "Se konkurransegrunnlag" or are
empty entirely. The model can't classify what isn't there. Instead of returning
a (low-confidence) prediction, the pipeline returns "needs_review" so a human
can fetch the missing content from the linked documents/website.

USE
---
Same gate must be applied in:
  1. Training-data filtering — drop rows where needs_review() is True.
  2. Inference time         — skip the model call, return "needs_review".

This keeps training and serving aligned.

USAGE
-----
    from inference_rules import needs_review
    flag, reason = needs_review(kort_beskrivelse)
    if flag:
        return {"label": "needs_review", "reason": reason}
    # else: run the model
"""

import re

from langdetect import DetectorFactory, detect

DetectorFactory.seed = 0

MIN_LEN = 30  # below this → needs_review

# Languages we consider close enough to Norwegian Bokmål for the model.
# - 'no' (Norwegian) is the obvious one.
# - 'da' (Danish) is mutually intelligible with Norwegian; nb-bert-base handles it.
# - 'sv' (Swedish) is close enough that langdetect often confuses it with Norwegian.
# Anything else → routed to human review (production assumption: scraper translated).
NORWEGIAN_READABLE = {"no", "da", "sv"}

# Lowercased placeholder phrases (text == one of these, after strip+lower).
PLACEHOLDER_PHRASES = {
    "se tittel",
    "se tittel.",
    "tittelen sier vel alt",
    "tittelen sier vel alt.",
    "se konkurransegrunnlag",
    "se konkurransegrunnlag.",
    "se vedlegg",
    "se vedlegg.",
    "se dokumentene",
    "se dokumentene.",
    "se dokumentene som ble sendt på mail",
    "se henvendelse på e-post",
    "se henvendelse på epost",
    "se nettside",
    "se nettside.",
    "se utlysning",
    "se utlysning.",
    "se anbudsdokumenter",
    "se anbudsdokumenter.",
    "rammeavtale",
    "rammeavtale.",
}

# Substring patterns: short descriptions that *contain* these phrases also fail.
PLACEHOLDER_PATTERNS = [
    re.compile(r"^se\s+(tittel|konkurransegrunnlag|vedlegg|dokumenter|nettside|utlysning|anbud|henvendelse)", re.IGNORECASE),
    re.compile(r"tittelen sier vel alt", re.IGNORECASE),
    re.compile(r"sjekk (dokumentene|vedlegg|nettsiden|websiden)", re.IGNORECASE),
    re.compile(r"check (the doc|website|attachment)", re.IGNORECASE),
    re.compile(r"read (website|the website|the doc)", re.IGNORECASE),
]


def needs_review(text):
    """Return (True, reason) if the description should NOT be sent to the model.

    Otherwise returns (False, "ok").
    """
    if text is None:
        return True, "empty"

    s = str(text).strip()
    if s == "" or s.lower() == "nan":
        return True, "empty"

    if len(s) < MIN_LEN:
        return True, f"too_short(len={len(s)})"

    s_lower = s.lower().strip().rstrip(".").strip()
    if s_lower in {p.rstrip(".") for p in PLACEHOLDER_PHRASES}:
        return True, "placeholder_phrase"

    for pat in PLACEHOLDER_PATTERNS:
        if pat.search(s):
            # Only fire as placeholder if the description is also short — a long
            # description that *mentions* "se vedlegg" inside a real sentence is fine.
            if len(s) < 80:
                return True, f"placeholder_match({pat.pattern[:30]})"

    # Last check: language. Production assumption is that the scraper has already
    # translated foreign-language leads into Norwegian. Anything that arrives here
    # in another language is unexpected — route to human review.
    try:
        lang = detect(s[:500])
        if lang not in NORWEGIAN_READABLE:
            return True, f"non_norwegian({lang})"
    except Exception:
        # Detection failure → fall through (assume Norwegian, don't block)
        pass

    return False, "ok"


if __name__ == "__main__":
    tests = [
        "",
        None,
        "   ",
        "Se konkurransegrunnlag",
        "Se tittel.",
        "Tittelen sier vel alt.",
        "Rammeavtale",
        "Sjekk dokumentene",
        "Anskaffelse av samfunnsøkonomisk analyse for transportforskning innen evaluering.",
        "Short text",
        "Se vedlegg for full beskrivelse av kontraktens innhold inkludert alle leveranser.",  # long → ok
        "TRANSQ is a joint qualification system for Scandinavian transport suppliers.",  # English → flagged
        "Hilma on Suomen julkisten hankintojen ilmoituskanava ja keskitetty palvelu.",  # Finnish → flagged
    ]
    for t in tests:
        flag, reason = needs_review(t)
        print(f"{flag!s:<6} {reason:<35} | {t!r}")