office_translator/services/quality/pattern_leak.py

"""
Pattern leak detection for the L0 quality layer.

Detects common failure modes where the LLM translator:
  1. Leaks parts of the system prompt (e.g. starts with "Translation:" or "Voici la traduction :")
  2. Hallucinates by repeating the same word/phrase many times in a row
  3. Returns a chain-of-thought / explanation instead of a translation

These checks are pure regex / counting — no model, no network.
"""

from __future__ import annotations

import re
from typing import Dict, List


# ---------- Prompt leak patterns ----------

# Phrases that strongly suggest the LLM leaked its prompt or a thought process
# into the output. We check only at the START of the translation (after
# stripping whitespace) to avoid false positives on legitimate text.
LEAK_PREFIX_PATTERNS: List[re.Pattern] = [
    re.compile(r"^(translation|translated text|here is the translation|here'?s the translation)\s*[:：-]", re.IGNORECASE),
    re.compile(r"^(voici (la |ma )?traduction|traduction\s*[:：-])\b", re.IGNORECASE),
    re.compile(r"^(原文|译|翻译|译为|以下是)\s*[:：]?", re.UNICODE),
    re.compile(r"^(sure,?\s+here'?s?\s+(the\s+)?translation|of course,?\s+here)", re.IGNORECASE),
    re.compile(r"^(\*\*|__|\#)\s*translation", re.IGNORECASE),
    re.compile(r"^translated from\s+\w+\s+to\s+\w+\s*[:：-]", re.IGNORECASE),
]


# ---------- Repetition detection ----------

# A "word" for the repetition check — uses Unicode word boundaries so
# it works on Chinese, Japanese, Korean, etc.
_WORD_RE = re.compile(r"\S+", re.UNICODE)

# Threshold: a word (or token) repeated 5+ times consecutively is almost
# always a hallucination. We allow up to 4 to handle legitimate text
# like "the the" (typo) without false positives.
REPETITION_THRESHOLD = 5
# Same character repeated 20+ times in a row is also a hallucination
# (catches cases like "xxxxxxxxxxx" or "==========").
CHAR_REPETITION_THRESHOLD = 20


def check(text: str) -> Dict:
    """
    Returns a dict like:
        {
            "issue": None | "prompt_leak" | "repetition_hallucination",
            "matched_pattern": "..." | None,
            "repetition_count": int | None,
        }
    Never raises.
    """
    if not text or not text.strip():
        return {"issue": None, "matched_pattern": None, "repetition_count": None}

    stripped = text.lstrip()

    # 1. Prompt leak
    for pat in LEAK_PREFIX_PATTERNS:
        m = pat.match(stripped)
        if m:
            return {
                "issue": "prompt_leak",
                "matched_pattern": pat.pattern,
                "repetition_count": None,
            }

    # 2. Token-level repetition
    tokens = _WORD_RE.findall(stripped)
    rep_count = _max_consecutive_repetition(tokens)
    if rep_count >= REPETITION_THRESHOLD:
        return {
            "issue": "repetition_hallucination",
            "matched_pattern": None,
            "repetition_count": rep_count,
        }

    # 3. Character-level repetition (catches "xxxxxx" without spaces)
    char_rep = _max_consecutive_char_repetition(stripped)
    if char_rep >= CHAR_REPETITION_THRESHOLD:
        return {
            "issue": "repetition_hallucination",
            "matched_pattern": None,
            "repetition_count": char_rep,
        }

    return {"issue": None, "matched_pattern": None, "repetition_count": max(rep_count, char_rep) or None}


def _max_consecutive_repetition(tokens: List[str]) -> int:
    """Return the maximum number of times the same token appears consecutively."""
    if not tokens:
        return 0
    # Normalize: lower-case + strip basic punctuation for comparison
    norm = [t.lower().strip(".,!?;:\"'`()[]{}") for t in tokens]
    max_run = 1
    current_run = 1
    for i in range(1, len(norm)):
        if norm[i] and norm[i] == norm[i - 1]:
            current_run += 1
            if current_run > max_run:
                max_run = current_run
        else:
            current_run = 1
    return max_run


def _max_consecutive_char_repetition(text: str) -> int:
    """Return the maximum number of times the same character appears consecutively."""
    if not text:
        return 0
    max_run = 1
    current_run = 1
    for i in range(1, len(text)):
        if text[i] == text[i - 1] and not text[i].isspace():
            current_run += 1
            if current_run > max_run:
                max_run = current_run
        else:
            current_run = 1
    return max_run