"""
HandwrittenOCR - محرك التصحيح الإملائي وقاموس التصحيح v5.1
================================================================
المحسنات (مبنية على اقتراحات Gemini + تحسينات يدوية):
- TECHNICAL_KEYWORDS: حماية المصطلحات البرمجية من التصحيح الخاطئ
- PYTHON_KEYWORDS: كلمات بايثون المحجوزة لا تُصحَّح أبداً
- تحميل كلمات مخصصة في word_frequency للمدقق الإملائي
- correct_text() تعترف الآن بالكلمات التقنية وتتجاوزها
- spell_correct_word() محسّن بنفس الطريقة
- تسجيل مفصّل لكل عملية تصحيح مع القرارات والأسباب
"""

import json
import os
import logging
import traceback
import pandas as pd
from datetime import datetime
from collections import Counter, defaultdict
from spellchecker import SpellChecker
from langdetect import detect, DetectorFactory
from dataclasses import dataclass, field

logger = logging.getLogger("HandwrittenOCR")

# استيراد أدوات اللوق المفصّل
try:
    from src.logger import log_step, log_error_full, log_result
except ImportError:
    def log_step(lg, name, data=None):
        lg.info(f"STEP [{name}]")
        if data:
            for k, v in data.items():
                lg.info(f"      {k}: {v}")
    def log_error_full(lg, ctx, err, extra=None):
        lg.error(f"ERROR [{ctx}] {type(err).__name__}: {err}", exc_info=True)
    def log_result(lg, name, result):
        lg.info(f"RESULT [{name}] {result}")

DetectorFactory.seed = 0

_ar_corrector = None
_en_spellchecker = None
_de_spellchecker = None

# ===================== قائمة المصطلحات المحمية =====================
TECHNICAL_KEYWORDS = {
    # مصطلحات برمجية عامة
    "python", "pythonistas", "scraping", "parsing", "ocr",
    "batch", "programming", "script", "database", "configure",
    "setup", "env", "immutable", "concatenation", "tuples",
    "dictionaries", "debugging", "programmatically", "spreadsheet",
    "integers", "float", "boolean", "syntax", "web",
    "etl", "dataframe", "json", "csv", "yaml", "markdown",
    "mermaid", "repository", "clone", "commit", "push",
    # اختصارات تقنية
    "repl", "dpi", "api", "gpu", "cpu", "ram", "rom",
    "lora", "huggingface", "transformers", "pytorch", "tensorboard",
    # كلمات من ملاحظات المستخدم
    "printouts", "involve", "scattered", "skyrocketed", "stacked",
    "affectionately", "serpentine", "cryptic", "sophisticated",
    "intricate", "throwaway", "surreal", "conventions",
    "trade", "off", "boot", "camps",
    # مفاهيم تقنية
    "comprehensions", "replication", "precedence", "modulo",
    "exponent", "traceback", "overriding",
}

# كلمات بايثون المحجوزة — لا تُصحَّح أبدًا
PYTHON_KEYWORDS = {
    "False", "None", "True", "and", "as", "assert", "async", "await",
    "break", "class", "continue", "def", "del", "elif", "else", "except",
    "finally", "for", "from", "global", "if", "import", "in", "is",
    "lambda", "nonlocal", "not", "or", "pass", "raise", "return",
    "try", "while", "with", "yield",
    # دوال مدمجة
    "print", "input", "len", "range", "type", "int", "str", "float",
    "list", "dict", "set", "tuple", "bool", "open", "file", "super",
    "self", "cls", "init", "repr", "main", "name", "args", "kwargs",
    "append", "extend", "pop", "sort", "join", "split", "strip",
    "format", "replace", "lower", "upper", "title", "capitalize",
    "enumerate", "zip", "map", "filter", "sorted", "reversed",
    "isinstance", "issubclass", "hasattr", "getattr", "setattr",
    "import", "from", "as", "module", "package",
}

# كلمات إضافية قابلة للتوسيع
_CUSTOM_VOCAB = set()

# مجموعة داخلية للحصول على أفضل أداء (كلها lowercase)
_PROTECTED_WORDS_LOWER = set()


def _rebuild_protected_set():
    """إعادة بناء مجموعة الكلمات المحمية."""
    global _PROTECTED_WORDS_LOWER
    _PROTECTED_WORDS_LOWER = (
        {k.lower() for k in TECHNICAL_KEYWORDS}
        | {k.lower() for k in PYTHON_KEYWORDS}
        | {k.lower() for k in _CUSTOM_VOCAB}
    )
    logger.debug(f"أُعيد بناء القائمة المحمية: {len(_PROTECTED_WORDS_LOWER)} كلمة")


def _is_protected_word(word: str) -> bool:
    """التحقق مما إذا كانت الكلمة محمية."""
    result = word.lower() in _PROTECTED_WORDS_LOWER
    if result:
        logger.debug(f"  كلمة محمية: '{word}' — يتجاوز التصحيح")
    return result


def init_correctors() -> None:
    """
    تهيئة المدققات الإملائية مع تسجيل مفصّل.
    """
    logger.info("=" * 55)
    logger.info("تهيئة المدققات الإملائية")
    logger.info("=" * 55)

    global _ar_corrector, _en_spellchecker, _de_spellchecker

    # === المدقق العربي ===
    log_step(logger, "تحميل المدقق العربي")
    try:
        from ar_corrector.corrector import Corrector
        _ar_corrector = Corrector()
        logger.info("  تم تحميل المدقق الإملائي العربي (ar-corrector)")
    except ImportError as e:
        logger.warning(f"  ar-corrector غير مثبت — التصحيح العربي لن يكون متاحاً: {e}")
        logger.debug(traceback.format_exc())
    except Exception as e:
        log_error_full(logger, "تحميل المدقق العربي", e)

    # === المدقق الإنجليزي ===
    log_step(logger, "تحميل المدقق الإنجليزي")
    _en_spellchecker = SpellChecker(language="en")
    logger.info("  تم تحميل SpellChecker(language='en')")

    # === المدقق الألماني ===
    log_step(logger, "تحميل المدقق الألماني")
    _de_spellchecker = SpellChecker(language="de")
    logger.info("  تم تحميل SpellChecker(language='de')")

    # === تحميل الكلمات المحمية في قاموس التردد ===
    all_known_words = list(TECHNICAL_KEYWORDS | PYTHON_KEYWORDS | _CUSTOM_VOCAB)
    if all_known_words:
        _en_spellchecker.word_frequency.load_words(all_known_words)
        logger.info(f"  تم تحميل {len(all_known_words)} كلمة محمية في المدقق الإملائي الإنجليزي")

    _rebuild_protected_set()

    log_result(logger, "تهيئة المدققات الإملائية", {
        "arabic": _ar_corrector is not None,
        "english": _en_spellchecker is not None,
        "german": _de_spellchecker is not None,
        "protected_words": len(_PROTECTED_WORDS_LOWER),
    })


def load_custom_vocabulary(vocab_list: list[str]) -> None:
    """
    تحميل مصطلحات إضافية لحمايتها من التصحيح.
    """
    global _CUSTOM_VOCAB
    logger.info(f"تحميل {len(vocab_list)} مصطلح إضافي في القائمة المحمية")

    new_words = [w.strip() for w in vocab_list if w.strip()]
    for w in new_words:
        logger.debug(f"  كلمة جديدة محمية: '{w}'")

    _CUSTOM_VOCAB.update(new_words)
    _rebuild_protected_set()

    if _en_spellchecker and new_words:
        _en_spellchecker.word_frequency.load_words(new_words)

    logger.info(f"المجموع المحمي الآن: {len(_PROTECTED_WORDS_LOWER)} كلمة")


def correct_text(text: str) -> str:
    """
    تصحيح إملائي حسب اللغة المكتشفة مع تسجيل مفصّل.
    """
    if not text or not text.strip():
        logger.debug("correct_text: نص فارغ — يتجاوز")
        return text

    text = text.strip()
    logger.debug(f"correct_text: نص='{text[:80]}...' ({len(text)} حرف)")

    try:
        lang = detect(text)
        logger.debug(f"correct_text: اللغة المكتشفة='{lang}'")

        if lang == "ar":
            result = _correct_arabic(text)
        elif lang == "en":
            result = _correct_english(text)
        elif lang == "de":
            result = _correct_german(text)
        else:
            logger.debug(f"correct_text: لغة غير مدعومة '{lang}' — يُعاد النص كما هو")
            return text

        result = enhance_digit_recognition(result)

        if result != text:
            logger.debug(f"correct_text: تم التصحيح: '{text[:60]}' => '{result[:60]}'")
        else:
            logger.debug("correct_text: لم يتغير النص بعد التصحيح")
        return result

    except Exception as e:
        logger.debug(f"correct_text: فشل كشف اللغة: {type(e).__name__}: {e}")
        return text


def _correct_arabic(text: str) -> str:
    """تصحيح النص العربي مع تسجيل مفصّل."""
    if _ar_corrector is None:
        logger.debug("_correct_arabic: المدقق العربي غير متاح — يتجاوز")
        return text
    try:
        original = text
        result = _ar_corrector.contextual_correct(text)
        if result != original:
            logger.info(f"  [تصحيح عربي] '{original[:50]}' => '{result[:50]}'")
        return result
    except Exception as e:
        log_error_full(logger, "التصحيح العربي", e, extra={"text_preview": text[:100]})
        return text


def _correct_german(text: str) -> str:
    """تصحيح الجمل الألمانية كلمة بكلمة مع حفظ الترقيم وتسجيل مفصّل."""
    if _de_spellchecker is None:
        logger.debug("_correct_german: المدقق الألماني غير متاح — يتجاوز")
        return text
    try:
        words = text.split()
        corrected = []
        changes = []
        for word in words:
            if _is_protected_word(word):
                corrected.append(word)
                continue
            clean = word.strip(".,;:!?\"'()-")
            if clean:
                if _is_protected_word(clean):
                    corrected.append(word)
                    continue
                fixed = _de_spellchecker.correction(clean)
                if fixed and fixed != clean:
                    corrected_word = word.replace(clean, fixed)
                    corrected.append(corrected_word)
                    changes.append(f"'{clean}' => '{fixed}'")
                else:
                    corrected.append(word)
            else:
                corrected.append(word)
        if changes:
            logger.info(f"  [تصحيح ألماني] {len(changes)} تعديل: {', '.join(changes[:5])}")
        return " ".join(corrected)
    except Exception as e:
        log_error_full(logger, "التصحيح الألماني", e, extra={"text_preview": text[:100]})
        return text


def _correct_english(text: str) -> str:
    """
    تصحيح الجمل الإنجليزية كلمة بكلمة مع حفظ الترقيم وتسجيل مفصّل.
    """
    if _en_spellchecker is None:
        logger.debug("_correct_english: المدقق الإنجليزي غير متاح — يتجاوز")
        return text
    try:
        words = text.split()
        corrected = []
        changes = []
        for word in words:
            if _is_protected_word(word):
                corrected.append(word)
                continue

            clean = word.strip(".,;:!?\"'()-")
            if clean:
                if _is_protected_word(clean):
                    corrected.append(word)
                    continue

                fixed = _en_spellchecker.correction(clean)
                if fixed and fixed != clean:
                    corrected_word = word.replace(clean, fixed)
                    corrected.append(corrected_word)
                    changes.append(f"'{clean}' => '{fixed}'")
                else:
                    corrected.append(word)
            else:
                corrected.append(word)
        if changes:
            logger.info(f"  [تصحيح إنجليزي] {len(changes)} تعديل: {', '.join(changes[:5])}")
        return " ".join(corrected)
    except Exception as e:
        log_error_full(logger, "التصحيح الإنجليزي", e, extra={"text_preview": text[:100]})
        return text


# قاموس تصحيح الأرقام — يحول الحروف المشابهة للأرقام
_DIGIT_CORRECTIONS = {
    "O": "0", "o": "0",
    "I": "1", "l": "1", "|": "1",
    "Z": "2", "z": "2",
    "S": "5", "s": "5",
    "G": "6",
    "T": "7", "t": "7",
    "B": "8",
}


def enhance_digit_recognition(text: str) -> str:
    """
    تصحيح حرفي للأرقام في النص.
    يحوّل الحروف المشابهة بصرياً للأرقام: O→0, I→1, S→5, ...
    يعمل فقط على الكلمات الخالصة من الأرقام والحروف المشابهة.
    """
    if not text:
        return text
    # تطبيق فقط على كلمات تبدو كأرقام (تتكون من أحرف وأرقام فقط)
    words = text.split()
    corrected = []
    for word in words:
        clean = word.strip(".,;:!?\"'()-")
        if clean and all(c.isalnum() or c in "_-/" for c in clean):
            # إذا كانت الكلمة أرقام مختلطة بأحرف مشابهة
            if any(c.isdigit() for c in clean):
                fixed = clean
                for letter, digit in _DIGIT_CORRECTIONS.items():
                    fixed = fixed.replace(letter, digit)
                if fixed != clean and fixed.isdigit():
                    corrected_word = word.replace(clean, fixed)
                    corrected.append(corrected_word)
                    continue
        corrected.append(word)
    return " ".join(corrected)


def spell_correct_word(text: str) -> str:
    """
    تصحيح سريع كلمة بكلمة مع تسجيل مفصّل.
    """
    text = text.strip()
    if not text:
        return ""

    if _is_protected_word(text):
        logger.debug(f"spell_correct_word: '{text}' كلمة محمية — يتجاوز")
        return text

    try:
        lang = detect(text)
        logger.debug(f"spell_correct_word: '{text}' لغة='{lang}'")

        if lang == "ar" and _ar_corrector:
            original = text
            result = _ar_corrector.contextual_correct(text)
            if result != original:
                logger.debug(f"  [تصحيح عربي] '{original}' => '{result}'")
            return result
        elif lang == "de" and _de_spellchecker:
            words = text.split()
            corrected = []
            for w in words:
                if _is_protected_word(w):
                    corrected.append(w)
                else:
                    fixed = _de_spellchecker.correction(w)
                    if fixed and fixed != w:
                        logger.debug(f"  [تصحيح ألماني] '{w}' => '{fixed}'")
                    corrected.append(fixed or w)
            return " ".join(corrected)

        # إنجليزي أو افتراضي
        words = text.split()
        corrected = []
        for w in words:
            if _is_protected_word(w):
                corrected.append(w)
            else:
                fixed = _en_spellchecker.correction(w)
                if fixed and fixed != w:
                    logger.debug(f"  [تصحيح إنجليزي] '{w}' => '{fixed}'")
                corrected.append(fixed or w)
        result = enhance_digit_recognition(" ".join(corrected))
        return result
    except Exception as e:
        logger.debug(f"spell_correct_word: فشل: {type(e).__name__}: {e}")
        return text


# ===================== قواعد التصحيح المتقدمة =====================

@dataclass
class CorrectionRule:
    """قاعدة تصحيح ببيانات وصفية كاملة لتتبع الاستخدام والمراجعة."""
    original: str
    correction: str
    votes: int = 1
    first_seen: str = field(default_factory=lambda: datetime.now().isoformat())
    last_used: str = None
    usage_count: int = 0
    last_reviewed: str = None
    reviewer: str = None
    confidence: float = 1.0
    contexts: list = field(default_factory=list)
    flagged: bool = False
    notes: str = ""

    def to_dict(self) -> dict:
        return {
            "original": self.original, "correction": self.correction,
            "votes": self.votes, "first_seen": self.first_seen,
            "last_used": self.last_used, "usage_count": self.usage_count,
            "last_reviewed": self.last_reviewed, "reviewer": self.reviewer,
            "confidence": self.confidence, "contexts": self.contexts,
            "flagged": self.flagged, "notes": self.notes,
        }

    @classmethod
    def from_dict(cls, data: dict, key: str = "") -> "CorrectionRule":
        if isinstance(data, str):
            # Simple dict: just original -> correction
            return cls(original=key, correction=data)
        return cls(
            original=data.get("original", key), correction=data.get("correction", data.get(key, "")),
            votes=data.get("votes", 1), first_seen=data.get("first_seen", datetime.now().isoformat()),
            last_used=data.get("last_used"), usage_count=data.get("usage_count", 0),
            last_reviewed=data.get("last_reviewed"), reviewer=data.get("reviewer"),
            confidence=data.get("confidence", 1.0), contexts=data.get("contexts", []),
            flagged=data.get("flagged", False), notes=data.get("notes", ""),
        )


def build_correction_dict_v2(feedback_csv: str, correction_dict_path: str, min_votes: int = 1) -> dict:
    """بناء قاموس تصحيح متقدم مع CorrectionRule ببيانات وصفية."""
    log_step(logger, "build_correction_dict_v2", {"feedback_csv": feedback_csv, "min_votes": min_votes})
    if not os.path.exists(feedback_csv):
        return {}
    try:
        df_fb = pd.read_csv(feedback_csv, encoding="utf-8-sig")
        if df_fb.empty:
            return {}
        buckets = defaultdict(list)
        for _, row in df_fb.iterrows():
            orig = str(row.get("original_text", "")).strip()
            corr = str(row.get("corrected_text", "")).strip()
            if orig and corr and orig != corr:
                buckets[orig].append({
                    "correction": corr,
                    "timestamp": str(row.get("timestamp", "")),
                    "image_id": row.get("image_id"),
                    "status": row.get("status"),
                })
        result = {}
        for orig, entries in buckets.items():
            # Pick most common correction
            counts = Counter(e["correction"] for e in entries)
            best_corr, best_count = counts.most_common(1)[0]
            if best_count >= min_votes:
                rule = CorrectionRule(
                    original=orig, correction=best_corr,
                    votes=best_count,
                    first_seen=min(e["timestamp"] for e in entries if e["timestamp"]) or datetime.now().isoformat(),
                    contexts=[e["image_id"] for e in entries if e.get("image_id")],
                )
                result[orig] = rule
        os.makedirs(os.path.dirname(correction_dict_path), exist_ok=True)
        with open(correction_dict_path, "w", encoding="utf-8") as f:
            json.dump({k: v.to_dict() for k, v in result.items()}, f, ensure_ascii=False, indent=2)
        logger.info(f"build_correction_dict_v2: {len(result)} قاعدة من {len(df_fb)} سجل")
        return result
    except Exception as e:
        log_error_full(logger, "build_correction_dict_v2", e)
        return {}


def track_correction_usage(correction_dict_path: str, word: str) -> None:
    """تحديث عداد الاستخدام لقاعدة تصحيح عند تطبيقها."""
    if not word or not os.path.exists(correction_dict_path):
        return
    try:
        with open(correction_dict_path, "r", encoding="utf-8") as f:
            data = json.load(f)
        if word in data:
            entry = data[word]
            entry["usage_count"] = entry.get("usage_count", 0) + 1
            entry["last_used"] = datetime.now().isoformat()
            with open(correction_dict_path, "w", encoding="utf-8") as f:
                json.dump(data, f, ensure_ascii=False, indent=2)
    except Exception:
        pass


def auto_calibrate_dict_thresholds(correction_dict_path: str, method: str = "percentile") -> dict:
    """معايرة تلقائية لعتبات مؤشرات القاموس."""
    if not os.path.exists(correction_dict_path):
        return {}
    try:
        with open(correction_dict_path, "r", encoding="utf-8") as f:
            data = json.load(f)
        if not data:
            return {}
        confs = [v.get("confidence", 1.0) for v in data.values()]
        usages = [v.get("usage_count", 0) for v in data.values()]
        if not confs:
            return {}
        if method == "std_dev":
            import numpy as np
            c_low = max(0.0, float(np.mean(confs) - np.std(confs)))
            c_mid = float(np.mean(confs))
            u_mid = float(np.median(usages))
            u_high = float(np.percentile(usages, 90)) if usages else 50
        else:  # percentile
            import numpy as np
            c_low, c_mid = np.percentile(confs, [25, 50])
            u_mid, u_high = np.percentile(usages, [75, 90]) if usages else (20, 50)

        thresholds = {
            "conf_low": round(c_low, 3), "conf_mid": round(c_mid, 3),
            "usage_high": int(u_high), "usage_mid": int(u_mid),
            "calibrate_method": method,
        }
        logger.info(f"auto_calibrate: {thresholds}")
        return thresholds
    except Exception as e:
        log_error_full(logger, "auto_calibrate_dict_thresholds", e)
        return {}


def calculate_rule_indicator(rule: CorrectionRule, thresholds: dict = None) -> dict:
    """حساب مؤشر بصري لقاعدة تصحيح: 🟢 موثوق / 🟡 مراجعة / 🔴 عاجل / ⏳ جديد."""
    if thresholds is None:
        thresholds = {
            "conf_low": 0.60, "conf_mid": 0.80,
            "usage_high": 50, "usage_mid": 20,
            "days_critical": 30, "days_warning": 14, "new_days_warning": 3,
        }
    score = 0
    if rule.confidence < thresholds.get("conf_low", 0.60):
        score += 3
    elif rule.confidence < thresholds.get("conf_mid", 0.80):
        score += 1
    if rule.flagged:
        score += 2
    # Days since last review
    days_review = 999
    if rule.last_reviewed:
        try:
            days_review = (datetime.now() - datetime.fromisoformat(rule.last_reviewed)).days
        except Exception:
            pass
    if rule.usage_count > thresholds.get("usage_high", 50) and days_review > thresholds.get("days_critical", 30):
        score += 2
    # First seen
    days_seen = 999
    try:
        days_seen = (datetime.now() - datetime.fromisoformat(rule.first_seen)).days
    except Exception:
        pass
    if score >= 5:
        visual = "🔴 عاجل"
    elif score >= 3:
        visual = "🟡 مراجعة مقترحة"
    elif score == 0 and days_seen <= thresholds.get("new_days_warning", 3):
        visual = "⏳ جديد"
    else:
        visual = "🟢 موثوق"

    return {
        "visual": visual, "score": score,
        "confidence": rule.confidence, "usage_count": rule.usage_count,
        "days_since_review": days_review, "days_since_seen": days_seen,
        "votes": rule.votes, "flagged": rule.flagged,
    }


def get_dictionary_audit_queue(correction_dict_path: str, priority: str = "all", limit: int = 20) -> list:
    """جلب قائمة انتظار مراجعة القاموس."""
    if not os.path.exists(correction_dict_path):
        return []
    try:
        with open(correction_dict_path, "r", encoding="utf-8") as f:
            data = json.load(f)
        if not data:
            return []
        rules = []
        for k, v in data.items():
            rule = CorrectionRule.from_dict(v, k)
            indicator = calculate_rule_indicator(rule)
            rules.append({"key": k, "rule": rule, "indicator": indicator})

        # Sort by priority
        if priority == "flagged":
            rules = [r for r in rules if r["rule"].flagged]
        elif priority == "new":
            rules = sorted(rules, key=lambda r: r["indicator"]["days_since_seen"], reverse=True)
        elif priority == "low_conf":
            rules = sorted(rules, key=lambda r: r["rule"].confidence)
        else:
            rules = sorted(rules, key=lambda r: r["indicator"]["score"], reverse=True)

        return rules[:limit]
    except Exception as e:
        log_error_full(logger, "get_dictionary_audit_queue", e)
        return []


def archive_correction_rule(correction_dict_path: str, key: str, reason: str = "") -> bool:
    """أرشفة قاعدة تصحيح بدلاً من حذفها."""
    if not os.path.exists(correction_dict_path):
        return False
    try:
        with open(correction_dict_path, "r", encoding="utf-8") as f:
            data = json.load(f)
        if key not in data:
            return False
        rule_data = data.pop(key)
        rule_data["archived_reason"] = reason
        rule_data["archived_at"] = datetime.now().isoformat()
        # Save archive
        archive_path = correction_dict_path.replace(".json", "_archived.json")
        archive = {}
        if os.path.exists(archive_path):
            with open(archive_path, "r", encoding="utf-8") as f:
                archive = json.load(f)
        archive[key] = rule_data
        with open(archive_path, "w", encoding="utf-8") as f:
            json.dump(archive, f, ensure_ascii=False, indent=2)
        # Save updated dict
        with open(correction_dict_path, "w", encoding="utf-8") as f:
            json.dump(data, f, ensure_ascii=False, indent=2)
        logger.info(f"archive_correction_rule: '{key}' reason='{reason}'")
        return True
    except Exception as e:
        log_error_full(logger, "archive_correction_rule", e)
        return False


# ===================== قاموس التصحيح المستمر =====================

def build_correction_dict(
    feedback_csv: str,
    correction_dict_path: str,
    min_votes: int = 1,
) -> dict:
    """
    بناء قاموس تصحيح من تصحيحات المستخدم مع تسجيل مفصّل.
    """
    log_step(logger, "بناء قاموس التصحيح", {
        "feedback_csv": feedback_csv,
        "output_path": correction_dict_path,
        "min_votes": min_votes,
    })

    if not os.path.exists(feedback_csv):
        logger.info("  ملف feedback غير موجود — قاموس فارغ")
        return {}

    try:
        df_fb = pd.read_csv(feedback_csv, encoding="utf-8-sig")
        if df_fb.empty:
            logger.info("  ملف feedback فارغ — قاموس فارغ")
            return {}

        logger.info(f"  عدد سجلات feedback: {len(df_fb)}")

        buckets = defaultdict(Counter)
        for _, row in df_fb.iterrows():
            orig = str(row.get("original_text", "")).strip()
            corr = str(row.get("corrected_text", "")).strip()
            if orig and corr and orig != corr:
                buckets[orig][corr] += 1

        result = {
            orig: cnt.most_common(1)[0][0]
            for orig, cnt in buckets.items()
            if cnt.most_common(1)[0][1] >= min_votes
        }

        os.makedirs(os.path.dirname(correction_dict_path), exist_ok=True)
        with open(correction_dict_path, "w", encoding="utf-8") as f:
            json.dump(result, f, ensure_ascii=False, indent=2)

        # تسجيل بعض الأمثلة
        examples = list(result.items())[:5]
        if examples:
            logger.info(f"  أمثلة من القاموس: {examples}")

        logger.info(f"تم تحديث قاموس التصحيح: {len(result)} كلمة")
        log_result(logger, "قاموس التصحيح", {
            "entries": len(result),
            "source_rows": len(df_fb),
            "min_votes": min_votes,
        })
        return result

    except Exception as e:
        log_error_full(logger, "بناء قاموس التصحيح", e, extra={
            "feedback_csv": feedback_csv,
        })
        return {}


def load_correction_dict(correction_dict_path: str) -> dict:
    """تحميل قاموس التصحيح من الملف مع تسجيل."""
    if not os.path.exists(correction_dict_path):
        logger.debug(f"load_correction_dict: الملف غير موجود: {correction_dict_path}")
        return {}
    try:
        with open(correction_dict_path, "r", encoding="utf-8") as f:
            result = json.load(f)
        logger.info(f"تم تحميل قاموس التصحيح: {len(result)} كلمة من {correction_dict_path}")
        return result
    except Exception as e:
        log_error_full(logger, "تحميل قاموس التصحيح", e, extra={"path": correction_dict_path})
        return {}


def apply_correction_dict(text: str, correction_dict: dict) -> str:
    """تطبيق قاموس التصحيح على نص مع تسجيل التعديلات."""
    if not correction_dict or not text:
        return text
    words = text.split()
    corrected = [correction_dict.get(w, w) for w in words]
    changes = [(w, corrected[i]) for i, w in enumerate(words) if w != corrected[i]]
    if changes:
        logger.debug(f"apply_correction_dict: {len(changes)} تعديل من القاموس: {changes[:5]}")
    return " ".join(corrected)


def append_feedback(
    feedback_csv: str,
    image_id: int,
    original: str,
    corrected: str,
    status: str = "verified",
) -> None:
    """تسجيل تصحيح في ملف CSV مع تسجيل."""
    os.makedirs(os.path.dirname(feedback_csv), exist_ok=True)
    ts = datetime.now().isoformat()
    record = {
        "timestamp": ts,
        "image_id": image_id,
        "original_text": original,
        "corrected_text": corrected,
        "status": status,
    }
    file_exists = os.path.exists(feedback_csv)
    pd.DataFrame([record]).to_csv(
        feedback_csv, mode="a",
        header=not file_exists,
        index=False, encoding="utf-8-sig",
    )
    logger.debug(f"append_feedback: image_id={image_id}, '{original[:30]}' => '{corrected[:30]}', status={status}")


def get_protected_words_count() -> dict:
    """إرجاع عدد الكلمات المحمية لكل فئة."""
    return {
        "technical_keywords": len(TECHNICAL_KEYWORDS),
        "python_keywords": len(PYTHON_KEYWORDS),
        "custom_vocabulary": len(_CUSTOM_VOCAB),
        "total_protected": len(_PROTECTED_WORDS_LOWER),
    }