verifier.py

"""Evidence-based verification and source-backed correction of Quran and Hadith quotations.

Pipeline (see ``IslamicContentVerifier``):

    input text -> detection -> BM25 retrieval -> alignment (phonetic skeleton, sliding window, LCS, dynamic gap)
               -> exact match            : verified
               -> altered Quran passage  : mismatch with the exact source text as the correction
               -> anything uncertain     : human review (no correction is produced)
               -> nothing similar found  : no matching source

Safety rule: a correction is only ever the exact text of a retrieved source. Nothing is generated.
"""
from __future__ import annotations

import difflib
import logging
import time
from dataclasses import dataclass, field
from typing import Dict, List, Optional

from alignment import align
from detector import DetectedSpan
from scanner import HybridDetector
from idgham import apply_idgham
from normalization import content_words, normalize_for_matching
from retrieval import SourceRetriever
from similarity import best_match_score, compute_signals

logger = logging.getLogger(__name__)

MAX_INPUT_CHARS = 20_000
SHORT_QUOTE_WORDS = 3   # quotations shorter than this (found by the rules) always go to human review
MIN_EXACT_TOKENS = 3   # shorter quotations are too generic to be verified by exact containment alone


# --------------------------------------------------------------------------------------------------------------
# Configuration (all tunable numbers live here)
# --------------------------------------------------------------------------------------------------------------
@dataclass
class VerifierConfig:
    """Thresholds calibrated in Subtask 1B of the research notebook."""

    quran_correct_threshold: float = 0.94
    quran_uncertain_low: float = 0.45
    quran_min_coverage: float = 0.40
    hadith_correct_threshold: float = 0.88
    hadith_uncertain_low: float = 0.30
    hadith_min_coverage: float = 0.70
    quran_top_k: int = 25
    hadith_top_k: int = 15
    hadith_retrieval_guard: float = 0.20


@dataclass
class CorrectorConfig:
    """Correction is proposed only when match strength >= ``*_strong``; between ``*_low`` and ``*_strong`` the
    case goes to human review. Hadith is never auto-corrected (``hadith_strong`` > 1), by design: the exact
    fragment boundaries of a Hadith quotation cannot be reproduced reliably, and narrations differ legitimately."""

    max_window: int = 8
    hadith_top_k: int = 40
    quran_strong: float = 0.65
    min_full_ratio: float = 0.40
    quran_low: float = 0.55
    hadith_strong: float = 1.01
    hadith_low: float = 0.45


@dataclass
class PipelineConfig:
    verifier: VerifierConfig = field(default_factory=VerifierConfig)
    corrector: CorrectorConfig = field(default_factory=CorrectorConfig)
    verified_min_conf: float = 0.75        # "Correct" verdicts weaker than this -> human review
    unsupported_min_conf: float = 0.70     # "Incorrect + no source" needs this confidence to abstain confidently
    unsupported_strength: float = 0.35     # ...or the best source match is this weak (nothing similar exists)


# --------------------------------------------------------------------------------------------------------------
# Verification (Subtask 1B)
# --------------------------------------------------------------------------------------------------------------
@dataclass
class Verification:
    verdict: str                         # 'Correct' | 'Incorrect'
    confidence: float
    best_score: float
    method: str
    source: Optional[dict] = None        # best matching source record (with 'signals')
    n_candidates: int = 0
    retrieval_top: float = 0.0


class Verifier:
    """Scores a quotation against retrieved candidates and returns a verdict with its supporting source."""

    def __init__(self, retriever: SourceRetriever, config: Optional[VerifierConfig] = None) -> None:
        self.kb = retriever
        self.cfg = config or VerifierConfig()

    def verify(self, span_text: str, content_type: str) -> Verification:
        if not span_text or not span_text.strip():
            return self._result("Incorrect", 0.95, 0.0, None, 0, "empty_span")
        if content_type == "Ayah":
            return self._verify_quran(span_text)
        if content_type == "Hadith":
            return self._verify_hadith(span_text)
        return self._result("Incorrect", 0.5, 0.0, None, 0, "unknown_type")

    def _verify_quran(self, span: str) -> Verification:
        cfg = self.cfg
        candidates = self.kb.search_quran_ayahs(span, top_k=cfg.quran_top_k)
        if not candidates:
            return self._result("Incorrect", 0.8, 0.0, None, 0, "no_candidates")
        score, best = best_match_score(span, candidates, "Ayah")
        signals = best.get("signals", {}) if best else {}
        coverage, is_substring = signals.get("coverage", 0.0), signals.get("is_substring", 0)
        n = len(candidates)

        if is_substring and coverage >= cfg.quran_min_coverage:
            return self._result("Correct", min(0.98, 0.85 + score * 0.15), score, best, n, "substring_match")
        if score >= cfg.quran_correct_threshold and coverage >= cfg.quran_min_coverage:
            return self._result("Correct", min(0.95, 0.70 + score * 0.25), score, best, n, "threshold_pass")
        if score <= cfg.quran_uncertain_low:
            return self._result("Incorrect", min(0.95, 0.70 + (1 - score) * 0.25), score, best, n, "threshold_fail")

        strong = sum(
            1 for cand in candidates[:10]
            if (s := compute_signals(span, cand.get("text", ""), "Ayah"))["coverage"] >= 0.80 and s["lcs_ratio"] >= 0.75
        )
        if strong >= 2:
            return self._result("Correct", 0.60 + min(0.20, strong * 0.05), score, best, n, "borderline_multi_cov")
        return self._result("Incorrect", 0.58, score, best, n, "borderline_default")

    def _verify_hadith(self, span: str) -> Verification:
        cfg = self.cfg
        candidates = self.kb.search_hadith(span, top_k=cfg.hadith_top_k)
        if not candidates:
            return self._result("Incorrect", 0.75, 0.0, None, 0, "no_candidates")
        top_retrieval = candidates[0].get("retrieval_score", 0.0)
        score, best = best_match_score(span, candidates, "Hadith")
        signals = best.get("signals", {}) if best else {}
        coverage, is_substring = signals.get("coverage", 0.0), signals.get("is_substring", 0)
        n = len(candidates)

        if is_substring and coverage >= cfg.hadith_min_coverage and top_retrieval >= cfg.hadith_retrieval_guard:
            return self._result("Correct", min(0.97, 0.80 + score * 0.17), score, best, n, "substring_match", top_retrieval)
        if score >= cfg.hadith_correct_threshold and coverage >= cfg.hadith_min_coverage:
            return self._result("Correct", min(0.92, 0.65 + score * 0.27), score, best, n, "threshold_pass", top_retrieval)
        if score <= cfg.hadith_uncertain_low:
            return self._result("Incorrect", min(0.90, 0.65 + (1 - score) * 0.25), score, best, n, "threshold_fail", top_retrieval)

        moderate = sum(
            1 for cand in candidates[:8]
            if (s := compute_signals(span, cand.get("text", ""), "Hadith"))["coverage"] >= 0.65 and s["lcs_ratio"] >= 0.55
        )
        if moderate >= 2 and top_retrieval >= 0.30:
            return self._result("Correct", 0.58 + min(0.22, moderate * 0.06), score, best, n, "borderline_multi_cov", top_retrieval)
        if top_retrieval < 0.25 or score < 0.45:
            return self._result("Incorrect", 0.60, score, best, n, "borderline_low_retrieval", top_retrieval)
        return self._result("Incorrect", 0.55, score, best, n, "borderline_default", top_retrieval)

    @staticmethod
    def _result(verdict, confidence, score, best, n_candidates, method, top_retrieval=0.0) -> Verification:
        return Verification(verdict, round(confidence, 4), round(score, 4), method, best, n_candidates, round(top_retrieval, 4))


# --------------------------------------------------------------------------------------------------------------
# Correction (Subtask 1C): locate the true ayah window / Hadith record and return its exact text
# --------------------------------------------------------------------------------------------------------------
@dataclass
class CorrectionMatch:
    kind: str                  # 'Ayah' | 'Hadith'
    strength: float            # coverage (Quran) / symmetric containment (Hadith)
    full_ratio: float
    text: str                  # proposed correction in the official 1C format (idgham + '(n)' ayah markers)
    source: dict               # reference metadata
    display: str = ""          # clean human-readable version


class Corrector:
    def __init__(self, retriever: SourceRetriever, config: Optional[CorrectorConfig] = None) -> None:
        self.kb = retriever
        self.cfg = config or CorrectorConfig()

    def match(self, span_text: str, content_type: str) -> Optional[CorrectionMatch]:
        return self.match_quran(span_text) if content_type == "Ayah" else self.match_hadith(span_text)

    def match_quran(self, query_text: str) -> Optional[CorrectionMatch]:
        """Best window of 1-8 consecutive ayahs (dynamic sliding window over BM25 seeds, exact-result pruning)."""
        kb = self.kb
        query_norm = normalize_for_matching(query_text)
        query_words = content_words(query_norm.split())
        if not query_words:
            return None
        query_len = len(query_norm)
        memo: Dict[tuple, tuple] = {}
        best = None   # (key, coverage, ratio, surah, start, end)
        for seed in kb.quran_seed_ayahs(query_words, top_k=25):
            surah, ayah = kb.quran[seed]["surah_id"], kb.quran[seed]["ayah_id"]
            ayahs = kb.quran_by_surah[surah]
            min_ayah, max_ayah = min(ayahs), max(ayahs)
            for offset in range(3):
                start = ayah - offset
                if start < min_ayah:
                    continue
                window_len = -1
                for length in range(1, self.cfg.max_window + 1):
                    end = start + length - 1
                    if end > max_ayah:
                        break
                    window_len += len(kb.q_norm_match[ayahs[end]]) + 1
                    len_diff = abs(window_len - query_len)
                    upper_bound = min(1.0, window_len / max(query_len, 1))
                    if best is not None:   # exact-result pruning
                        best_cov, best_neg = best[0][0], best[0][1]
                        if upper_bound < best_cov or (upper_bound == best_cov and -len_diff < best_neg):
                            continue
                    key_pos = (surah, start, end)
                    if key_pos in memo:
                        continue
                    window = " ".join(kb.q_norm_match[ayahs[a]] for a in range(start, end + 1))
                    matcher = difflib.SequenceMatcher(None, query_norm, window, autojunk=False)
                    matched = sum(b.size for b in matcher.get_matching_blocks() if b.size >= 4)
                    coverage = matched / max(query_len, 1)
                    key = (coverage, -len_diff, matcher.ratio())
                    memo[key_pos] = key
                    if best is None or key > best[0]:
                        best = (key, coverage, key[2], surah, start, end)
        if best is None:
            return None
        _, coverage, ratio, surah, start, end = best
        display = " ".join(kb.quran[kb.quran_by_surah[surah][a]]["text"] for a in range(start, end + 1))
        return CorrectionMatch(
            "Ayah", coverage, ratio, self._ayah_text(surah, start, end),
            {"type": "Quran", "surah_id": surah, "surah_name": kb.quran[kb.quran_by_surah[surah][start]]["surah_name"],
             "ayah_start": start, "ayah_end": end},
            display,
        )

    def _ayah_text(self, surah: int, start: int, end: int) -> str:
        kb, multi = self.kb, end > start
        parts = []
        for a in range(start, end + 1):
            text = kb.quran[kb.quran_by_surah[surah][a]]["text"]
            parts.append(f"{text} ({a})" if multi else text)
        return apply_idgham(" ".join(parts)).replace("\u0640", "")

    def match_hadith(self, query_text: str) -> Optional[CorrectionMatch]:
        kb = self.kb
        query_norm = normalize_for_matching(query_text)
        query_words = content_words(query_norm.split())
        if not query_words:
            return None
        query_len = len(query_norm)
        best = None   # (key, idx, field, ratio)
        for idx in kb.hadith_candidates(query_words, self.cfg.hadith_top_k):
            for field_name in ("matn", "full"):
                text = kb.hadith_norm(idx, field_name)
                if not text:
                    continue
                upper_bound = min(1.0, query_len / max(len(text), 1))
                if best is not None and upper_bound < best[0][0]:
                    continue
                matcher = difflib.SequenceMatcher(None, query_norm, text, autojunk=False)
                matched = sum(b.size for b in matcher.get_matching_blocks() if b.size >= 4)
                coverage, candidate_cov = matched / max(query_len, 1), matched / max(len(text), 1)
                key = (min(coverage, candidate_cov), matcher.ratio())
                if best is None or key > best[0]:
                    best = (key, idx, field_name, key[1])
        if best is None:
            return None
        key, idx, field_name, ratio = best
        record = kb.hadith[idx]
        text = record[field_name].strip()
        return CorrectionMatch(
            "Hadith", key[0], ratio, text,
            {"type": "Hadith", "hadithID": record["hadithID"], "book": record["book"], "title": record["title"],
             "field": "matn" if field_name == "matn" else "full_text"},
            text,
        )


# --------------------------------------------------------------------------------------------------------------
# End-to-end pipeline
# --------------------------------------------------------------------------------------------------------------
STATUS_INFO = {
    "VERIFIED": {"ar": "موثّق", "group": "verified"},
    "CORRECTED": {"ar": "غير مطابق — يوجد تصحيح من المصدر", "group": "mismatch"},
    "UNSUPPORTED": {"ar": "غير مطابق — لا يوجد مصدر مطابق", "group": "mismatch"},
    "HUMAN_REVIEW": {"ar": "يحتاج مراجعة بشرية", "group": "review"},
}


class IslamicContentVerifier:
    """Detect quotations, verify them against the corpora and decide: verified, corrected, unsupported or review."""

    def __init__(self, retriever: Optional[SourceRetriever] = None, config: Optional[PipelineConfig] = None,
                 use_scanner: bool = True, decouple_triggers: bool = True) -> None:
        self.cfg = config or PipelineConfig()
        self.retriever = retriever or SourceRetriever()
        self.verifier = Verifier(self.retriever, self.cfg.verifier)
        self.corrector = Corrector(self.retriever, self.cfg.corrector)
        self.detector = HybridDetector(self.retriever, use_scanner, rules_use_corpus=decouple_triggers,
                                       decouple_triggers=decouple_triggers)
        self.detector_name = type(self.detector).__name__

    # ---- public API -----------------------------------------------------------------------------------------
    def detect(self, text: str) -> List[DetectedSpan]:
        return self.detector.detect(self._validate(text)) if text.strip() else []

    def needs_hadith(self, text: str) -> bool:
        """True if the text contains a Hadith quotation (so the large Hadith index must be loaded)."""
        return any(span.label == "Hadith" for span in self.detect(text))

    def analyze(self, text: str) -> dict:
        """Run the full pipeline on a generated text."""
        text = self._validate(text)
        started = time.time()
        spans = self.detector.detect(text) if text.strip() else []
        detect_seconds = time.time() - started
        result = self._analyze_spans(text, spans)
        result["timings"] = {"detect_s": round(detect_seconds, 3), "total_s": round(time.time() - started, 3)}
        return result

    def analyze_spans(self, text: str, spans: List[dict]) -> dict:
        """Skip detection and use given spans ``[{label, start, end}]`` (evaluation / oracle mode)."""
        given = [DetectedSpan(s["start"], s["end"], s["label"], None, "given", text[s["start"]:s["end"]]) for s in spans]
        return self._analyze_spans(self._validate(text), given)

    def analyze_detected(self, text: str, spans: List[DetectedSpan]) -> dict:
        """Verify spans produced elsewhere (e.g. detector + hosted model merged by ``camelbert_adapter.merge_spans``)."""
        return self._analyze_spans(self._validate(text), spans)

    # ---- internals ------------------------------------------------------------------------------------------
    @staticmethod
    def _validate(text: str) -> str:
        if not isinstance(text, str):
            raise TypeError("Input text must be a string")
        if len(text) > MAX_INPUT_CHARS:
            raise ValueError(f"Input is too long ({len(text)} characters); the limit is {MAX_INPUT_CHARS}")
        return text

    def _analyze_spans(self, text: str, spans: List[DetectedSpan]) -> dict:
        reports = [self._process_span(i + 1, span) for i, span in enumerate(sorted(spans, key=lambda s: s.start))]
        counts = {status: 0 for status in STATUS_INFO}
        for report in reports:
            counts[report["status"]] += 1
        return {
            "input_text": text,
            "detector": self.detector_name,
            "spans": reports,
            "corrected_text": self._apply_corrections(text, reports),
            "summary": {
                "n_spans": len(reports),
                "n_ayah": sum(r["type"] == "Ayah" for r in reports),
                "n_hadith": sum(r["type"] == "Hadith" for r in reports),
                **counts,
                "needs_human_review": counts["HUMAN_REVIEW"] > 0,
            },
        }

    def _process_span(self, index: int, span: DetectedSpan) -> dict:
        try:
            return self._decide(index, span)
        except Exception:   # a single failing quotation must not break the whole report
            logger.exception("Failed to process span %d", index)
            report = self._empty_report(index, span)
            self._finalize(report, "HUMAN_REVIEW", {"code": "internal_error"})
            return report

    @staticmethod
    def _empty_report(index: int, span: DetectedSpan) -> dict:
        return {
            "id": index, "type": span.label, "start": span.start, "end": span.end, "text": span.text,
            "detection": {"backend": span.source, "confidence": None if span.confidence is None else round(span.confidence, 4)},
            "verification": {"verdict": "Incorrect", "confidence": 0.0, "score": 0.0, "method": "error", "n_candidates": 0},
            "evidence": None, "correction": None, "suggestion": None, "notes": [],
        }

    @staticmethod
    def _finalize(report: dict, status: str, reason: dict) -> None:
        info = STATUS_INFO[status]
        report.update(status=status, status_ar=info["ar"], group=info["group"], reason=reason)

    @staticmethod
    def source_label(source: dict) -> str:
        """Plain reference used in reasons, e.g. ``سورة البقرة 153`` or ``حديث رقم 5``."""
        if source["type"] == "Quran":
            start, end = source["ayah_start"], source["ayah_end"]
            return f"سورة {source['surah_name']} {start}" + (f"–{end}" if end != start else "")
        return f"حديث رقم {source['hadithID']}"

    def _decide(self, index: int, span: DetectedSpan) -> dict:
        report = self._empty_report(index, span)
        verification = self.verifier.verify(span.text, span.label)
        report["verification"] = {
            "verdict": verification.verdict, "confidence": verification.confidence, "score": verification.best_score,
            "method": verification.method, "n_candidates": verification.n_candidates,
        }
        if span.label == "Ayah":
            self._decide_quran(report, span, verification)
        else:
            self._decide_hadith(report, span, verification)
        if span.source == "rules" and len(normalize_for_matching(span.text).split()) < SHORT_QUOTE_WORDS:
            # two words cannot identify a source: never claim "verified" or "wrong", and never correct
            self._finalize(report, "HUMAN_REVIEW", {"code": "too_short"})
            report["correction"] = None
            report["suggestion"] = None
        return report

    @staticmethod
    def _proposal(span: DetectedSpan, match: Optional[CorrectionMatch]) -> Optional[dict]:
        if match is None:
            return None
        return {"text": match.text, "display_text": match.display, "source": match.source,
                "match_strength": round(match.strength, 4), "full_ratio": round(match.full_ratio, 4)}

    def _decide_quran(self, report: dict, span: DetectedSpan, verification: Verification) -> None:
        cfg, corr_cfg = self.cfg, self.cfg.corrector
        match = self.corrector.match_quran(span.text)
        if match is not None:
            source_text, source_ref = match.display, match.source
        elif verification.source:
            candidate = verification.source
            source_text = candidate["text"]
            source_ref = {"type": "Quran", "surah_id": candidate["surah_id"], "surah_name": candidate["surah_name"],
                          "ayah_start": candidate["ayah_id"], "ayah_end": candidate["ayah_id"]}
        else:
            source_text, source_ref = "", None
        alignment = align(span.text, source_text, self.retriever.quran_vocabulary) if source_text else None
        if source_ref:
            report["evidence"] = {"source": source_ref, "signals": compute_signals(span.text, source_text, "Ayah"),
                                  "comparison": alignment}
        proposal = self._proposal(span, match)
        n_tokens = len(content_words(normalize_for_matching(span.text).split())) if span.text else 0
        has_tokens = alignment is not None and alignment["exact"] and len(alignment["word_diff"]) >= 1

        if has_tokens and sum(len(op["span"].split()) for op in alignment["word_diff"]) >= MIN_EXACT_TOKENS:
            self._finalize(report, "VERIFIED", {"code": "exact_match", "source": self.source_label(source_ref)})
            if alignment["diacritic_notes"]:
                report["notes"].append({"code": "diacritic_conflict", "n": len(alignment["diacritic_notes"])})
            if alignment["orthographic_variants"]:
                report["notes"].append({"code": "orthographic_variant", "n": alignment["orthographic_variants"]})
            return

        strong = match is not None and match.strength >= corr_cfg.quran_strong and match.full_ratio >= corr_cfg.min_full_ratio
        if strong:
            self._finalize(report, "CORRECTED", {"code": "altered_passage", "source": self.source_label(source_ref),
                                                 "n": alignment["mismatches"] if alignment else 0,
                                                 "reordered": bool(alignment and alignment["reordered"])})
            # replace only the passage the author quoted, not the whole ayah that contains it
            excerpt = alignment["source_excerpt"] if alignment and alignment["source_excerpt"] else proposal["display_text"]
            report["correction"] = {**proposal, "display_text": excerpt, "applied": True}
            return

        if verification.verdict == "Correct":
            self._finalize(report, "HUMAN_REVIEW", {"code": "weak_match"})
            report["suggestion"] = proposal
        elif match is None or match.strength < corr_cfg.quran_low:
            confident = verification.confidence >= cfg.unsupported_min_conf and not verification.method.startswith("borderline")
            if match is None or match.strength < cfg.unsupported_strength or confident:
                self._finalize(report, "UNSUPPORTED", {"code": "no_source"})
            else:
                self._finalize(report, "HUMAN_REVIEW", {"code": "insufficient_evidence"})
                report["suggestion"] = proposal
        else:
            self._finalize(report, "HUMAN_REVIEW", {"code": "candidate_not_strong", "source": self.source_label(source_ref),
                                                    "strength": round(match.strength, 2)})
            report["suggestion"] = proposal

    def _decide_hadith(self, report: dict, span: DetectedSpan, verification: Verification) -> None:
        cfg, corr_cfg = self.cfg, self.cfg.corrector
        best = verification.source
        alignment = None
        if best:
            alignment = align(span.text, best["text"])
            report["evidence"] = {
                "source": {"type": "Hadith", "hadithID": best["hadithID"], "book": best["book"], "title": best["title"]},
                "signals": best.get("signals"), "comparison": alignment,
            }
        exact_tokens = alignment is not None and alignment["exact"] and sum(len(op["span"].split()) for op in alignment["word_diff"]) >= 4

        if verification.verdict == "Correct" or exact_tokens:
            if exact_tokens or (not verification.method.startswith("borderline") and verification.confidence >= cfg.verified_min_conf):
                self._finalize(report, "VERIFIED", {"code": "exact_match" if exact_tokens else "close_match",
                                                    "source": f"حديث رقم {best['hadithID']}"})
                if alignment and not alignment["exact"]:
                    report["notes"].append({"code": "hadith_minor_diffs", "n": alignment["mismatches"]})
                if alignment and alignment["diacritic_notes"]:
                    report["notes"].append({"code": "diacritic_conflict", "n": len(alignment["diacritic_notes"])})
            else:
                self._finalize(report, "HUMAN_REVIEW", {"code": "weak_match"})
            return

        match = self.corrector.match_hadith(span.text)
        proposal = self._proposal(span, match)
        if match is None or match.strength < corr_cfg.hadith_low:
            confident = verification.confidence >= cfg.unsupported_min_conf and not verification.method.startswith("borderline")
            if match is None or match.strength < cfg.unsupported_strength or confident:
                self._finalize(report, "UNSUPPORTED", {"code": "no_source"})
            else:
                self._finalize(report, "HUMAN_REVIEW", {"code": "insufficient_evidence"})
                report["suggestion"] = proposal
        else:   # a similar narration exists, but Hadith is never corrected automatically
            self._finalize(report, "HUMAN_REVIEW", {"code": "hadith_candidate", "source": self.source_label(match.source),
                                                    "strength": round(match.strength, 2)})
            report["suggestion"] = proposal

    @staticmethod
    def _apply_corrections(text: str, reports: List[dict]) -> str:
        out = text
        for report in sorted(reports, key=lambda r: r["start"], reverse=True):
            if report["status"] == "CORRECTED" and report["correction"] and report["correction"].get("applied"):
                out = out[: report["start"]] + report["correction"]["display_text"] + out[report["end"]:]
        return out