# -*- coding: utf-8 -*-
# Question Generator — Final Publishable Build (Lite/Full)
# صفحات ثابتة + Submit لكل سؤال فعليًا + منع تغيّر أبعاد صفحة الإدخال
# طور "فراغ" + طور "فهم مباشر" (mT5) مع fallbacks، صعوبة، BM25، فلترة قوية للمشتّتات، وتنويع على مستوى الفقرات.

import os, json, uuid, random, unicodedata
from dataclasses import dataclass
from pathlib import Path
from typing import List, Tuple, Optional

from PIL import Image
from pypdf import PdfReader
import fitz  # PyMuPDF
import regex as re2
import yake
import gradio as gr

# ------------------ إعدادات عامّة ------------------
random.seed(42)
DEFAULT_NUM_QUESTIONS = 6
DEFAULT_TROCR_MODEL = "microsoft/trocr-base-printed"
DEFAULT_TROCR_ZOOM   = 2.6
QUESTION_MODES = ["فراغ", "فهم مباشر"]
DIFFICULTY_MODES = ["سهل", "متوسط", "صعب"]

# BM25 (اختياري)
try:
    from rank_bm25 import BM25Okapi
    _HAS_BM25 = True
except Exception:
    _HAS_BM25 = False

# ------------------ OCR (تحميل كسول) ------------------
_OCR = {}
def get_ocr(model_id: str):
    try:
        from transformers import pipeline
        import torch
        dev = 0 if torch.cuda.is_available() else -1
        if model_id not in _OCR:
            _OCR[model_id] = pipeline("image-to-text", model=model_id, device=dev)
        return _OCR[model_id]
    except Exception:
        # بديل آمن: دالة تُعيد نصًا فارغًا
        return lambda im: [{"generated_text": ""}]

# ------------------ PDF/TXT → نص ------------------
def extract_text_with_pypdf(path: str) -> str:
    reader = PdfReader(path)
    out = []
    for p in reader.pages:
        try:
            t = p.extract_text() or ""
        except Exception:
            t = ""
        out.append(t)
    return "\n".join(out).strip()

def pdf_to_images(path: str, zoom: float=2.5) -> List[Image.Image]:
    doc = fitz.open(path); M = fitz.Matrix(zoom, zoom)
    imgs = []
    for pg in doc:
        pix = pg.get_pixmap(matrix=M, alpha=False)
        imgs.append(Image.frombytes("RGB",(pix.width,pix.height),pix.samples))
    doc.close()
    return imgs

def extract_text_with_ocr(path: str, model_id: str, zoom: float) -> str:
    ocr = get_ocr(model_id)
    parts = []
    for i, img in enumerate(pdf_to_images(path, zoom=zoom), start=1):
        try:
            out = ocr(img)
            txt = out[0].get("generated_text","").strip() if out else ""
        except Exception:
            txt = ""
        parts.append(f"--- [Page {i}] ---\n{txt}")
    return "\n\n".join(parts).strip()

def is_good(t: str, min_chars=250, min_alpha=0.15) -> bool:
    if len(t) < min_chars: return False
    alnum = sum(ch.isalnum() for ch in t)
    return (alnum/max(1,len(t))) >= min_alpha

def file_to_text(path: str, model_id=DEFAULT_TROCR_MODEL, zoom=DEFAULT_TROCR_ZOOM) -> Tuple[str,str]:
    ext = Path(path).suffix.lower()
    if ext == ".txt":
        with open(path,"r",encoding="utf-8",errors="ignore") as f:
            return f.read(), "plain text"
    raw = extract_text_with_pypdf(path)
    if is_good(raw): return raw, "embedded (pypdf)"
    return extract_text_with_ocr(path, model_id, zoom), "OCR (TrOCR)"

# ------------------ تنظيف عربي ------------------
AR_DIAC = r"[ًٌٍَُِّْ]"
def strip_headers(t:str)->str:
    out=[]
    for ln in t.splitlines():
        if re2.match(r"^\s*--- \[Page \d+\] ---\s*$", ln): continue
        if re2.match(r"^\s*(Page\s*\d+|صفحة\s*\d+)\s*$", ln): continue
        if re2.match(r"^\s*[-–—_*]{3,}\s*$", ln): continue
        out.append(ln)
    return "\n".join(out)

def norm_ar(t:str)->str:
    t = unicodedata.normalize("NFKC", t)
    t = re2.sub(r"[ـ]", "", t)
    t = re2.sub(AR_DIAC, "", t)
    t = re2.sub(r"[إأآا]", "ا", t)
    t = re2.sub(r"[يى]", "ي", t)
    t = re2.sub(r"\s+", " ", t)
    t = re2.sub(r'(\p{L})\1{2,}', r'\1', t)
    t = re2.sub(r'(\p{L})\1', r'\1', t)
    return t.strip()

def postprocess(raw:str)->str:
    t = strip_headers(raw).replace("\r","\n")
    t = re2.sub(r"\n{3,}", "\n\n", t)
    t = re2.sub(r"\d+\s*[\[\(][^\]\)]*[\]\)]", " ", t)
    t = re2.sub(r"\[\d+\]", " ", t)
    return norm_ar(t)

# ------------------ بنية السؤال ------------------
SENT_SPLIT = re2.compile(r"(?<=[\.!؟\?])\s+")
AR_STOP = set("""في على من إلى عن مع لدى ذلك هذه هذا الذين التي الذي أو أم إن أن كان تكون كانوا كانت كنت ثم قد لقد ربما بل لكن إلا سوى حتى حيث كما لما ما لماذا متى أين كيف أي هناك هنا هؤلاء أولئك نحن هو هي هم هن أنت أنتم أنتن""".split())

@dataclass
class MCQ:
    id: str
    question: str
    choices: List[str]
    answer_index: int

def split_sents(t:str)->List[str]:
    s=[x.strip() for x in SENT_SPLIT.split(t) if x.strip()]
    return [x for x in s if len(x)>=25]

# ====== (1) عبارات مفتاحية (YAKE) ======
def yake_keywords(t: str, k: int = 260) -> List[str]:
    phrases = []
    seen = set()
    for n in [3, 2, 1]:
        try:
            ex = yake.KeywordExtractor(lan='ar', n=n, top=k)
            pairs = ex.extract_keywords(t)
        except Exception:
            pairs = []
        for w, _ in pairs:
            w = re2.sub(r"\s+", " ", w.strip())
            if not w or w in seen: 
                continue
            if re2.match(r"^[\p{P}\p{S}\d_]+$", w): 
                continue
            if 2 <= len(w) <= 40:
                phrases.append(w)
                seen.add(w)
    return phrases

def good_kw(kw:str)->bool:
    return kw and len(kw)>=2 and kw not in AR_STOP and not re2.match(r"^[\p{P}\p{S}\d_]+$", kw)

# ====== POS/NER اختياري ======
_HAS_CAMEL = False
try:
    from camel_tools.morphology.analyzer import Analyzer
    from camel_tools.ner import NERecognizer
    _HAS_CAMEL = True
    _AN = Analyzer.builtin_analyzer()
    _NER = NERecognizer.pretrained()
except Exception:
    _HAS_CAMEL = False

NER_TAGS = {"PER","LOC","ORG","MISC"}

def ar_pos(word: str) -> str:
    if not _HAS_CAMEL:
        if re2.match(r"^(في|على|الى|إلى|من|عن|حتى|ثم|بل|لكن|أو|و)$", word): return "PART"
        if re2.match(r"^[\p{N}]+$", word): return "NUM"
        if re2.search(r"(ة|ات|ون|ين|ان)$", word): return "NOUN"
        return "X"
    try:
        ana = _AN.analyze(word)
        if not ana: return "X"
        from collections import Counter
        pos_candidates = [a.get('pos','X') for a in ana]
        return Counter(pos_candidates).most_common(1)[0][0] if pos_candidates else "X"
    except Exception:
        return "X"

def is_named_entity(token: str) -> bool:
    if not _HAS_CAMEL:
        return False
    try:
        tag = _NER.predict_sentence([token])[0]
        return tag in NER_TAGS
    except Exception:
        return False

def is_clean_sentence(s: str) -> bool:
    if not (60 <= len(s) <= 240): return False
    if re2.search(r"https?://|www\.", s): return False
    if re2.search(r"\d{2,}", s): return False
    return True

def safe_keyword(k: str) -> bool:
    if not good_kw(k): return False
    if is_named_entity(k): return False
    if ar_pos(k) in {"PRON","PART"}: return False
    return True

# ====== Embeddings/Masking/Cross-Encoder (اختياري) ======
_EMB = None
def get_embedder():
    global _EMB
    if _EMB is None:
        try:
            from sentence_transformers import SentenceTransformer
            _EMB = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
        except Exception:
            _EMB = False
    return _EMB

def nearest_terms(target: str, pool: List[str], k: int = 24) -> List[Tuple[str, float]]:
    emb = get_embedder()
    if not emb:
        return []
    cand = [w for w in pool if w != target and len(w) >= 2 and not re2.match(r"^[\p{P}\p{S}\d_]+$", w)]
    if not cand:
        return []
    vecs = emb.encode([target] + cand, normalize_embeddings=True)
    t, C = vecs[0], vecs[1:]
    import numpy as np
    sims = (C @ t)
    idx = np.argsort(-sims)[:k]
    return [(cand[i], float(sims[i])) for i in idx]

_MLM = None
def get_masker():
    global _MLM
    if _MLM is None:
        try:
            from transformers import pipeline
            _MLM = pipeline("fill-mask", model="aubmindlab/bert-base-arabertv02")
        except Exception:
            _MLM = False
    return _MLM

def mlm_distractors(sentence_with_blank: str, correct: str, k: int = 18) -> List[str]:
    masker = get_masker()
    if not masker:
        return []
    masked = sentence_with_blank.replace("_____", masker.tokenizer.mask_token)
    try:
        outs = masker(masked, top_k=max(25, k+7))
        cands = []
        for o in outs:
            tok = o["token_str"].strip()
            if tok and tok != correct and len(tok) >= 2 and not re2.match(r"^[\p{P}\p{S}\d_]+$", tok):
                cands.append(tok)
        uniq, seen = [], set()
        for w in cands:
            if w not in seen:
                uniq.append(w); seen.add(w)
        return uniq[:k]
    except Exception:
        return []

_CE = None
def get_cross_encoder():
    global _CE
    if _CE is None:
        try:
            from sentence_transformers import CrossEncoder
            _CE = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
        except Exception:
            _CE = False
    return _CE

def rank_by_ce(sentence_with_blank: str, candidates: List[str]) -> List[str]:
    ce = get_cross_encoder()
    if not ce or not candidates:
        return candidates
    pairs = [(sentence_with_blank.replace("_____", c), c) for c in candidates]
    try:
        scores = ce.predict([p[0] for p in pairs])
        ranked = [c for _, c in sorted(zip(scores, [p[1] for p in pairs]), key=lambda x:-x[0])]
        return ranked
    except Exception:
        return candidates

# --------- أدوات مساعدة للمشتّتات ---------
def word_tokens(s: str) -> List[str]:
    s = norm_ar(s)
    return re2.findall(r"\p{L}+", s)

def token_set(s: str) -> set:
    return set([t for t in word_tokens(s) if t not in AR_STOP])

def jaccard(a: str, b: str) -> float:
    A, B = token_set(a), token_set(b)
    if not A or not B: return 0.0
    return len(A & B) / max(1, len(A | B))

def is_sub_or_super(a: str, b: str) -> bool:
    A, B = norm_ar(a), norm_ar(b)
    return (A in B) or (B in A)

def appears_as_long_fragment_in_sentence(w: str, sentence: str) -> bool:
    toks = word_tokens(w)
    if len(toks) < 3: 
        return False
    return re2.search(rf"(?<!\p{{L}}){re2.escape(norm_ar(w))}(?!\p{{L}})", norm_ar(sentence)) is not None

def choice_length_ok(w: str) -> bool:
    n = len(word_tokens(w))
    return 1 <= n <= 6

def paragraph_index_map(text: str, sentences: List[str]) -> dict:
    paras = [norm_ar(p) for p in re2.split(r"\n{2,}", text) if p.strip()]
    mapping = {}
    for i, s in enumerate(sentences):
        ns = norm_ar(s)
        pid = None
        for j, p in enumerate(paras):
            if ns and ns in p:
                pid = j; break
        mapping[s] = pid if pid is not None else -1
    return mapping

def looks_like_title_fragment(s: str) -> bool:
    return ":" in s and s.index(":") < max(10, len(s)//6)

def is_nouny_phrase(w: str) -> bool:
    toks = word_tokens(w)
    if not (1 <= len(toks) <= 4): return False
    if re2.search(r"(يفعل|تفعل|يشهد|تقوم|يمكن|قد|سوف)$", w): return False
    return True

def best_keyword_in_sentence(sentence: str, global_text: str) -> Optional[str]:
    if looks_like_title_fragment(sentence):
        parts = sentence.split(":", 1)
        sentence = parts[1] if len(parts) > 1 else sentence
    try:
        ex = yake.KeywordExtractor(lan='ar', n=3, top=24)
        pairs = ex.extract_keywords(sentence)
    except Exception:
        pairs = []
    cands = []
    for w, _ in pairs:
        w = re2.sub(r"\s+", " ", w.strip())
        if not w or not good_kw(w) or not safe_keyword(w): 
            continue
        if not is_nouny_phrase(w): 
            continue
        if not re2.search(rf"(?<!\p{{L}}){re2.escape(w)}(?!\p{{L}})", sentence):
            continue
        freq_weight = global_text.count(w)
        cands.append((w, len(w) + 0.7*freq_weight))
    if not cands:
        toks = [t for t in re2.findall(r"\p{L}+", sentence) if good_kw(t) and safe_keyword(t)]
        toks = [t for t in toks if is_nouny_phrase(t)]
        toks.sort(key=len, reverse=True)
        return toks[0] if toks else None
    cands.sort(key=lambda x: -x[1])
    return cands[0][0]

def similarity_caps(difficulty: str):
    if difficulty == "سهل":
        return 0.88
    if difficulty == "صعب":
        return 0.95
    return 0.92

def tokenize_ar(s: str) -> List[str]:
    s = norm_ar(s)
    toks = re2.findall(r"\p{L}+", s)
    return [t for t in toks if len(t) >= 2 and t not in AR_STOP]

def bm25_build(sentences: List[str]):
    if not _HAS_BM25 or not sentences:
        return None, []
    corpus_tokens = [tokenize_ar(s) for s in sentences]
    bm = BM25Okapi(corpus_tokens)
    return bm, corpus_tokens

def bm25_candidates(correct: str, sentences: List[str], bm, corpus_tokens, top: int = 20) -> List[str]:
    if not bm: return []
    q = tokenize_ar(correct)
    scores = bm.get_scores(q)
    idxs = sorted(range(len(scores)), key=lambda i: -scores[i])[:min(top, len(scores))]
    pool = set()
    for i in idxs:
        for tok in corpus_tokens[i]:
            if tok != correct and good_kw(tok):
                pool.add(tok)
    return list(pool)

def typo_like_variants(answer: str, k: int = 4) -> List[str]:
    """مشتّتات شكلية: تعريف/تنكير، ي/ى، ة/ه، حذف حرف."""
    a = norm_ar(answer)
    vars = set()
    if a.startswith("ال"):
        vars.add(a[2:])
    else:
        vars.add("ال" + a)
    vars.add(a.replace("ي", "ى"))
    vars.add(a.replace("ى", "ي"))
    vars.add(a.replace("ة", "ه"))
    vars.add(a.replace("ه", "ة"))
    if len(a) > 5:
        mid = len(a)//2
        vars.add(a[:mid] + a[mid+1:])
    out = [v for v in vars if v and norm_ar(v) != norm_ar(a)]
    return out[:k]

# ====== مشتّتات ذكية ======
def pos_compatible(a: str, b: str) -> bool:
    pa, pb = ar_pos(a), ar_pos(b)
    if "X" in (pa, pb): 
        return True
    return pa == pb

def length_close(a: str, b: str) -> bool:
    return abs(len(a) - len(b)) <= max(6, len(b)//2)

def smart_distractors(correct: str, phrase_pool: List[str], sentence: str, k: int = 3,
                      all_sentences: Optional[List[str]] = None, difficulty: str = "متوسط") -> List[str]:
    base: List[str] = []

    # (0) مشتّتات شكلية أولاً
    base.extend(typo_like_variants(correct, k=4))

    # (أ) جيران دلاليين
    base.extend([w for w,_ in nearest_terms(correct, phrase_pool, k=24)])

    # (ب) FILL-MASK
    for w in mlm_distractors(sentence.replace(correct, "_____"), correct, k=18):
        if w not in base:
            base.append(w)

    # (ج) BM25
    if all_sentences:
        bm, corp = bm25_build(all_sentences)
        for w in bm25_candidates(correct, all_sentences, bm, corp, top=18):
            if w not in base:
                base.append(w)

    # فلترة صارمة
    clean: List[str] = []
    for w in base:
        w = (w or "").strip()
        if not w or w == correct:
            continue
        if not choice_length_ok(w):
            continue
        if appears_as_long_fragment_in_sentence(w, sentence):
            continue
        if is_named_entity(w):
            continue
        if not pos_compatible(w, correct):
            continue
        if not length_close(w, correct):
            continue
        if is_sub_or_super(w, correct):
            continue
        if jaccard(w, correct) >= 0.5:
            continue
        clean.append(w)

    # ترتيب (اختياري) + فلتر قرب دلالي
    clean = rank_by_ce(sentence.replace(correct, "_____"), clean)[:max(k*4, k)]
    cap = similarity_caps(difficulty)
    try:
        emb = get_embedder()
        if emb and clean:
            vecs = emb.encode([correct] + clean, normalize_embeddings=True)
            c, others = vecs[0], vecs[1:]
            import numpy as np
            sims = others @ c
            filtered = [w for w, s in zip(clean, sims) if s < cap]
            if len(filtered) >= k:
                clean = filtered
    except Exception:
        pass

    # تجميع أخير
    out, seen = [], set()
    for w in clean:
        if w in seen: 
            continue
        seen.add(w); out.append(w)
        if len(out) >= k:
            break

    # تعويض إذا لزم
    if len(out) < k:
        extras = [w for w in phrase_pool 
                  if w not in out and w != correct and choice_length_ok(w) 
                  and not appears_as_long_fragment_in_sentence(w, sentence)
                  and not is_sub_or_super(w, correct)
                  and jaccard(w, correct) < 0.5]
        out.extend(extras[:(k-len(out))])
    if len(out) < k:
        out.extend([w for w in ["…"]*(k-len(out))])  # لن تُقبل لاحقًا إن لم نكمل 4 خيارات
    return out[:k]

# ====== mT5 (اختياري) ======
_MT5 = {"tok": None, "model": None, "ok": False}
def get_mt5():
    if _MT5["tok"] is not None or _MT5["model"] is not None or _MT5["ok"]:
        return _MT5["tok"], _MT5["model"], _MT5["ok"]
    try:
        from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
        _MT5["tok"] = AutoTokenizer.from_pretrained("google/mt5-small")
        _MT5["model"] = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small")
        _MT5["ok"] = True
    except Exception:
        _MT5["tok"] = None; _MT5["model"] = None; _MT5["ok"] = False
    return _MT5["tok"], _MT5["model"], _MT5["ok"]

def parse_json_block(s: str) -> Optional[dict]:
    try:
        return json.loads(s)
    except Exception:
        pass
    m = re2.search(r"\{.*\}", s, flags=re2.DOTALL)
    if m:
        try:
            return json.loads(m.group(0))
        except Exception:
            return None
    return None

def comp_prompt(sentence: str) -> str:
    return (
        "أنت منشئ أسئلة متعددة الخيارات باللغة العربية.\n"
        "من الجملة التالية، أنشئ سؤال فهم مباشر واحدًا مع أربع خيارات وإشارة للجواب الصحيح.\n"
        "أعد فقط JSON بهذا الشكل:\n"
        "{\n"
        "\"question\": \"...\",\n"
        "\"choices\": [\"...\",\"...\",\"...\",\"...\"],\n"
        "\"answer_index\": 0\n"
        "}\n\n"
        f"الجملة: {sentence}"
    )

def gen_one_comp_q(sentence: str, tok, model, max_new_tokens=128) -> Optional[MCQ]:
    try:
        import torch
        device = "cuda" if torch.cuda.is_available() else "cpu"
        model = model.to(device)
        inp = tok(comp_prompt(sentence), return_tensors="pt").to(device)
        out = model.generate(
            **inp,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            temperature=0.8,
            top_p=0.9,
            num_return_sequences=1,
            eos_token_id=tok.eos_token_id
        )
        text = tok.decode(out[0], skip_special_tokens=True)
        data = parse_json_block(text) or {}
        q = str(data.get("question","")).strip()
        choices = data.get("choices", [])
        ai = data.get("answer_index", 0)
        if not q or not isinstance(choices, list) or len(choices) < 4:
            return None
        choices = [str(c).strip() for c in choices][:4]
        ai = ai if isinstance(ai, int) and 0 <= ai < 4 else 0
        return MCQ(id=str(uuid.uuid4())[:8], question=q, choices=choices, answer_index=ai)
    except Exception:
        return None

def make_comp_mcqs(text: str, n: int = 6, difficulty: str = "متوسط") -> List[MCQ]:
    tok, model, ok = get_mt5()
    if not ok:
        return make_mcqs(text, n, difficulty=difficulty)

    sents_all = split_sents(text)
    sents = [s for s in sents_all if is_clean_sentence(s)] or sents_all[:]
    if not sents:
        return make_mcqs(text, n, difficulty=difficulty)

    # دمج جمل قصيرة لمقاطع مفيدة
    def make_chunks(sents, max_len=220):
        chunks = []
        i = 0
        while i < len(sents):
            cur = sents[i]
            j = i + 1
            while j < len(sents) and len(cur) + 1 + len(sents[j]) <= max_len:
                cur = cur + " " + sents[j]
                j += 1
            chunks.append(cur)
            i = j
        return chunks

    candidates = sents[:] + make_chunks(sents, max_len=220)
    random.shuffle(candidates)

    items: List[MCQ] = []
    tried = 0
    for s in candidates:
        if len(items) >= n: break
        mcq = gen_one_comp_q(s, tok, model)
        tried += 1
        if mcq:
            q = re2.sub(r"\s+", " ", mcq.question).strip()
            if not (12 <= len(q) <= 220): 
                continue
            choices = [re2.sub(r"\s+", " ", c).strip() for c in mcq.choices]
            seen=set(); clean=[]
            for c in choices:
                if c and c not in seen:
                    seen.add(c); clean.append(c)
            clean = (clean + ["…","…","…","…"])[:4]
            ai = mcq.answer_index if isinstance(mcq.answer_index,int) and 0<=mcq.answer_index<4 else 0
            items.append(MCQ(id=str(uuid.uuid4())[:8], question=q, choices=clean, answer_index=ai))
        if tried >= n * 12:
            break

    if not items:
        return make_mcqs(text, n, difficulty=difficulty)
    return items[:n]

# ------------------ مُولّد أسئلة "فراغ" (نهائي) ------------------
def make_mcqs(text: str, n: int = 6, difficulty: str = "متوسط") -> List[MCQ]:
    all_sents = split_sents(text)
    sents = [s for s in all_sents if is_clean_sentence(s)] or all_sents[:]
    if not sents:
        raise ValueError("النص قصير أو غير صالح.")

    keyphrases = yake_keywords(text, k=260)
    keyphrases = [kp for kp in keyphrases if safe_keyword(kp) and 2 <= len(kp) <= 40]

    sent_for: dict = {}
    for s in sents:
        for kp in keyphrases:
            if kp in sent_for:
                continue
            if re2.search(rf"(?<!\p{{L}}){re2.escape(kp)}(?!\p{{L}})", s):
                sent_for[kp] = s
        if len(sent_for) >= n * 5:
            break

    para_map = paragraph_index_map(text, sents)
    used_sentences: set = set()
    items: List[MCQ] = []

    MAX_PER_PARA = 2
    para_count: dict = {}

    def add_item_from_pair(sentence: str, kp: str) -> bool:
        nonlocal items, used_sentences, para_count
        pid = para_map.get(sentence, -1)
        if para_count.get(pid, 0) >= MAX_PER_PARA:
            return False
        if not re2.search(rf"(?<!\p{{L}}){re2.escape(kp)}(?!\p{{L}})", sentence):
            return False
        q = re2.sub(rf"(?<!\p{{L}}){re2.escape(kp)}(?!\p{{L}})", "_____", sentence, count=1)
        pool = [x for x in keyphrases if x != kp] or keyphrases[:]
        ch = smart_distractors(kp, pool, sentence, k=3,
                               all_sentences=all_sents, difficulty=difficulty) + [kp]

        choices, seen = [], set()
        for c in ch:
            c = (c or "").strip()
            if not c or c in seen:
                continue
            if not choice_length_ok(c):
                continue
            if appears_as_long_fragment_in_sentence(c, sentence):
                continue
            if is_sub_or_super(c, kp) or jaccard(c, kp) >= 0.5:
                continue
            seen.add(c); choices.append(c)

        if kp not in choices:
            choices.append(kp); seen.add(kp)
        if len(choices) < 4:
            return False

        choices = choices[:4]
        random.shuffle(choices)
        ans = choices.index(kp)

        items.append(MCQ(id=str(uuid.uuid4())[:8], question=q, choices=choices, answer_index=ans))
        used_sentences.add(sentence)
        para_count[pid] = para_count.get(pid, 0) + 1
        return True

    # تمريرة أولى: تنويع على الفقرات
    for kp in sorted(sent_for.keys(), key=lambda x: (-len(x), x)):
        if len(items) >= n: break
        s = sent_for[kp]
        if s in used_sentences: 
            continue
        _ = add_item_from_pair(s, kp)

    def fill_from_sentences(candidates: List[str]):
        for s in candidates:
            if len(items) >= n: break
            if s in used_sentences: 
                continue
            kp = None
            for kpp, ss in sent_for.items():
                if ss == s:
                    kp = kpp; break
            if kp is None:
                kp = best_keyword_in_sentence(s, text)
            if not kp:
                continue
            _ = add_item_from_pair(s, kp)

    if len(items) < n:
        remaining_new_para = [s for s in sents if para_count.get(para_map.get(s, -1), 0) < MAX_PER_PARA]
        fill_from_sentences(remaining_new_para)
    if len(items) < n:
        leftovers = [s for s in sents if s not in used_sentences]
        fill_from_sentences(leftovers)

    if not items:
        raise RuntimeError("تعذّر توليد أسئلة.")
    return items[:n]

# ------------------ تحويل إلى سجلات العرض ------------------
def clean_option_text(t: str) -> str:
    t = (t or "").strip()
    t = re2.sub(AR_DIAC, "", t)
    t = re2.sub(r"\s+", " ", t)
    t = re2.sub(r"^[\p{P}\p{S}_-]+|[\p{P}\p{S}_-]+$", "", t)
    # قصّ لطول معقول
    t = re2.sub(r"^(.{,60})(?:\s.*)?$", r"\1", t)
    return t or "…"

def to_records(items:List[MCQ])->List[dict]:
    recs=[]
    for it in items:
        opts=[]
        used=set()
        for i,lbl in enumerate(["A","B","C","D"]):
            txt=(it.choices[i] if i<len(it.choices) else "…")
            txt=clean_option_text(txt.replace(",", "،").replace("?", "؟").replace(";", "؛"))
            if txt in used:
                txt = f"{txt}‌{i+1}"
            used.add(txt)
            opts.append({"id":lbl,"text":txt,"is_correct":(i==it.answer_index)})
        recs.append({"id":it.id,"question":it.question.strip(),"options":opts})
    return recs

# ------------------ صفحة الأسئلة (HTML فقط) ------------------
def render_quiz_html(records: List[dict]) -> str:
    parts=[]
    for i, rec in enumerate(records, start=1):
        qid  = rec["id"]
        qtxt = rec["question"]
        cor  = next((o["id"] for o in rec["options"] if o["is_correct"]), "")
        opts_html=[]
        for o in rec["options"]:
            lid, txt = o["id"], o["text"]
            opts_html.append(f"""
                <label class="opt" data-letter="{lid}">
                    <input type="radio" name="q_{qid}" value="{lid}">
                    <span class="opt-letter">{lid}</span>
                    <span class="opt-text">{txt}</span>
                </label>
            """)
        parts.append(f"""
        <div class="q-card" data-qid="{qid}" data-correct="{cor}">
            <div class="q-header">
                <div class="q-title">السؤال {i}</div>
                <div class="q-badge" id="b_{qid}" hidden></div>
            </div>
            <div class="q-text">{qtxt}</div>
            <div class="opts">{''.join(opts_html)}</div>
            <div class="q-actions">
                <button class="q-submit">Submit</button>
                <span class="q-note" id="n_{qid}"></span>
            </div>
        </div>
        """)
    return f"""<div id="quiz" class="quiz-wrap">{''.join(parts)}</div>"""

# ------------------ بناء الامتحان وتبديل الصفحات ------------------
def build_quiz(text_area, file_path, n, model_id, zoom, mode, difficulty):
    text_area = (text_area or "").strip()
    if not text_area and not file_path:
        return "", gr.update(visible=True), gr.update(visible=False), "🛈 الصق نصًا أو ارفع ملفًا أولًا."
    raw = text_area if text_area else file_to_text(file_path, model_id=model_id, zoom=float(zoom))[0]
    cleaned = postprocess(raw)

    used_mode = mode
    try:
        if mode == "فهم مباشر":
            tok, model, ok = get_mt5()
            if ok:
                items = make_comp_mcqs(cleaned, n=int(n), difficulty=difficulty)
            else:
                items = make_mcqs(cleaned, n=int(n), difficulty=difficulty)
                used_mode = "فراغ (fallback)"
        else:
            items = make_mcqs(cleaned, n=int(n), difficulty=difficulty)
    except Exception:
        items = make_mcqs(cleaned, n=int(n), difficulty=difficulty)
        used_mode = "فراغ (fallback)"

    recs = to_records(items)
    warn = f"نمط مُستخدَم: **{used_mode}** — عدد الأسئلة: {len(items)}"
    return render_quiz_html(recs), gr.update(visible=False), gr.update(visible=True), warn

# ------------------ CSS ------------------
CSS = """
:root{
  --bg:#0e0e11; --panel:#15161a; --card:#1a1b20; --muted:#a7b0be;
  --text:#f6f7fb; --accent:#6ee7b7; --accent2:#34d399; --danger:#ef4444; --border:#262833;
}
body{direction:rtl; font-family:system-ui,'Cairo','IBM Plex Arabic',sans-serif; background:var(--bg);}
.gradio-container{max-width:980px;margin:0 auto;padding:12px 12px 40px;}
h2.top{color:#eaeaf2;margin:6px 0 16px}

/* صفحة الإدخال ثابتة الارتفاع ولا تتغير أبعادها */
.input-panel{background:var(--panel);border:1px solid var(--border);border-radius:14px;padding:16px;
  box-shadow:0 16px 38px rgba(0,0,0,.35); min-height:360px; display:flex; flex-direction:column; gap:12px;}
.small{opacity:.9;color:#d9dee8}

/* إخفاء معاينة الملف */
[data-testid="file"] .file-preview, [data-testid="file"] .file-preview * { display:none !important; }
[data-testid="file"] .grid-wrap { display:block !important; }
.upload-like{border:2px dashed #3b3f52;background:#121318;border-radius:12px;padding:12px;color:#cfd5e3;min-height:90px}

.button-primary>button{background:linear-gradient(180deg,var(--accent),var(--accent2));border:none;color:#0b0d10;font-weight:800}
.button-primary>button:hover{filter:brightness(.95)}
textarea{min-height:120px}

/* صفحة الأسئلة */
.q-card{background:var(--card);border:1px solid var(--border);border-radius:14px;padding:14px;margin:12px 0}
.q-header{display:flex;gap:10px;align-items:center;justify-content:space-between;margin-bottom:6px}
.q-title{color:#eaeaf2;font-weight:800}
.q-badge{padding:8px 12px;border-radius:10px;font-weight:700}
.q-badge.ok{background:#083a2a;color:#b6f4db;border:1px solid #145b44}
.q-badge.err{background:#3a0d14;color:#ffd1d6;border:1px solid #6a1e2b}

.q-text{color:#eaeaf2;font-size:1.06rem;line-height:1.8;margin:8px 0 12px}
.opts{display:flex;flex-direction:column;gap:8px}
.opt{display:flex;gap:10px;align-items:center;background:#14161c;border:1px solid #2a2d3a;border-radius:12px;padding:10px;transition:background .15s,border-color .15s}
.opt input{accent-color:var(--accent2)}
.opt-letter{display:inline-flex;width:28px;height:28px;border-radius:8px;background:#0f1116;border:1px solid #2a2d3a;align-items:center;justify-content:center;font-weight:800;color:#dfe6f7}
.opt-text{color:#eaeaf2}
.opt.ok{background:#0f2f22;border-color:#1b6a52}
.opt.err{background:#3a0d14;border-color:#6a1e2b}

.q-actions{display:flex;gap:10px;align-items:center;margin-top:10px}
.q-actions .q-submit{
  background:#2dd4bf;border:none;color:#0b0د10;font-weight:800;border-radius:10px;padding:8px 14px;cursor:pointer;
}
.q-actions .q-submit:disabled{opacity:.5;cursor:not-allowed}
.q-note{color:#ffd1d6}
.q-note.warn{color:#ffd1d6}
"""

# ------------------ JS: ربط Submit + إبراز الصح ------------------
ATTACH_LISTENERS_JS = """
() => {
  if (window.__q_submit_bound_multi2) { return 'already'; }
  window.__q_submit_bound_multi2 = true;

  document.addEventListener('click', function(e){
    if (!e.target || !e.target.classList || !e.target.classList.contains('q-submit')) return;

    const card    = e.target.closest('.q-card');
    if (!card) return;

    const qid      = card.getAttribute('data-qid');
    const correct  = card.getAttribute('data-correct');
    const note     = document.getElementById('n_'+qid);
    const badge    = document.getElementById('b_'+qid);
    const chosen   = card.querySelector('input[type="radio"]:checked');

    if (!chosen) {
      if (note){ note.textContent = 'اختر إجابة أولاً'; note.className='q-note warn'; }
      return;
    }

    const chosenLabel = chosen.closest('.opt');

    if (chosen.value === correct) {
      chosenLabel.classList.add('ok');
      if (badge){ badge.hidden=false; badge.className='q-badge ok'; badge.textContent='Correct!'; }
      card.querySelectorAll('input[type="radio"]').forEach(i => i.disabled = true);
      e.target.disabled = true;
      if (note) note.textContent = '';

      const qNode = card.querySelector('.q-text');
      if (qNode){
        const full = qNode.textContent || qNode.innerText || '';
        const correctText = [...card.querySelectorAll('.opt')].find(o =>
          o.querySelector('input').value === correct
        )?.querySelector('.opt-text')?.textContent || '';
        if (full && correctText && full.includes('_____')){
          const highlighted = full.replace('_____', `<mark style="background:#2dd4bf22;border:1px solid #2dd4bf55;border-radius:6px;padding:0 4px">${correctText}</mark>`);
          qNode.innerHTML = highlighted;
        }
      }
      return;
    }

    chosenLabel.classList.add('err');
    if (badge){ badge.hidden=false; badge.className='q-badge err'; badge.textContent='Incorrect.'; }
    if (note) note.textContent = '';
  });

  return 'wired-multi2';
}
"""

# ------------------ واجهة Gradio ------------------
with gr.Blocks(title="Question Generator", css=CSS) as demo:
    gr.Markdown("<h2 class='top'>Question Generator</h2>")

    page1 = gr.Group(visible=True, elem_classes=["input-panel"])
    with page1:
        gr.Markdown("اختر **أحد** الخيارين ثم اضغط الزر.", elem_classes=["small"])
        text_area = gr.Textbox(lines=6, placeholder="ألصق نصك هنا...", label="لصق نص")
        file_comp = gr.File(label="أو ارفع ملف (PDF / TXT)", file_count="single",
                            file_types=[".pdf",".txt"], type="filepath", elem_classes=["upload-like"])
        num_q = gr.Slider(4, 20, value=DEFAULT_NUM_QUESTIONS, step=1, label="عدد الأسئلة")

        mode_radio = gr.Radio(choices=QUESTION_MODES, value="فراغ", label="نوع السؤال")
        difficulty_radio = gr.Radio(choices=DIFFICULTY_MODES, value="متوسط", label="درجة الصعوبة")

        with gr.Accordion("خيارات PDF المصوّر (اختياري)", open=False):
            trocr_model = gr.Dropdown(
                choices=[
                    "microsoft/trocr-base-printed",
                    "microsoft/trocr-large-printed",
                    "microsoft/trocr-base-handwritten",
                    "microsoft/trocr-large-handwritten",
                ],
                value=DEFAULT_TROCR_MODEL, label="نموذج TrOCR"
            )
            trocr_zoom = gr.Slider(2.0, 3.5, value=DEFAULT_TROCR_ZOOM, step=0.1, label="Zoom OCR")

        btn_build = gr.Button("generate quistion", elem_classes=["button-primary"])
        warn = gr.Markdown("", elem_classes=["small"])

    page2 = gr.Group(visible=False)
    with page2:
        quiz_html = gr.HTML("")
        js_wired  = gr.Textbox(visible=False)

    btn_build.click(
        build_quiz,
        inputs=[text_area, file_comp, num_q, trocr_model, trocr_zoom, mode_radio, difficulty_radio],
        outputs=[quiz_html, page1, page2, warn]
    ).then(
        None, inputs=None, outputs=[js_wired], js=ATTACH_LISTENERS_JS
    )

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