scoring/engine.py

"""
PhilVerify — Scoring Engine (Orchestrator)
Ties together all NLP modules, Layer 1, and Layer 2 into a final VerificationResponse.
Final Score = (ML Confidence × 0.40) + (Evidence Score × 0.60)
"""
import asyncio
import json
import logging
import uuid
from datetime import datetime, timezone
from pathlib import Path

from config import get_settings
from api.schemas import (
    VerificationResponse, Verdict, Language, DomainTier,
    Layer1Result, Layer2Result, EntitiesResult, EvidenceSource, Stance,
)

logger = logging.getLogger(__name__)
settings = get_settings()

# ── Module-level NLP singleton cache ─────────────────────────────────────────
# These are created once per process and reused across all requests.
# Creating fresh instances on every request causes unnecessary model reloads
# from disk (300–500 ms each) which compounds into multi-second latency.
_nlp_cache: dict = {}

def _get_nlp(key: str, factory):
    """Return cached NLP instance, creating via factory() on first call."""
    if key not in _nlp_cache:
        _nlp_cache[key] = factory()
    return _nlp_cache[key]

# ── Domain credibility lookup ─────────────────────────────────────────────────
_DOMAIN_DB_PATH = Path(__file__).parent.parent / "domain_credibility.json"
_DOMAIN_DB: dict = {}

def _load_domain_db() -> dict:
    global _DOMAIN_DB
    if not _DOMAIN_DB:
        try:
            _DOMAIN_DB = json.loads(_DOMAIN_DB_PATH.read_text())
        except Exception as e:
            logger.warning("Could not load domain_credibility.json: %s", e)
    return _DOMAIN_DB

def get_domain_tier(domain: str) -> DomainTier | None:
    if not domain:
        return None
    db = _load_domain_db()
    domain = domain.lower().replace("www.", "")
    for tier_key, tier_data in db.items():
        if domain in tier_data.get("domains", []):
            return DomainTier(int(tier_key[-1]))
    return DomainTier.SUSPICIOUS  # Unknown domains default to Tier 3


def _map_verdict(final_score: float) -> Verdict:
    if final_score >= settings.credible_threshold:
        return Verdict.CREDIBLE
    elif final_score >= settings.fake_threshold:
        return Verdict.UNVERIFIED
    else:
        return Verdict.LIKELY_FAKE


async def run_verification(
    text: str,
    input_type: str = "text",
    source_domain: str | None = None,
) -> VerificationResponse:
    """
    Full verification pipeline orchestrator.
    Runs NLP analysis and ML classifier synchronously, evidence retrieval async.
    """
    # ── Lazy imports so app starts without heavy deps ─────────────────────────
    from nlp.preprocessor import TextPreprocessor
    from nlp.language_detector import LanguageDetector
    from nlp.ner import EntityExtractor
    from nlp.sentiment import SentimentAnalyzer
    from nlp.clickbait import ClickbaitDetector
    from nlp.claim_extractor import ClaimExtractor
    from evidence.news_fetcher import fetch_evidence, compute_similarity

    # ── Step 1: Preprocess ────────────────────────────────────────────────────
    preprocessor = _get_nlp("preprocessor", TextPreprocessor)
    proc = preprocessor.preprocess(text)

    # ── Step 2: Language detection ────────────────────────────────────────────
    lang_detector = _get_nlp("lang_detector", LanguageDetector)
    lang_result = lang_detector.detect(text)
    language = Language(lang_result.language) if lang_result.language in Language._value2member_map_ else Language.TAGLISH

    # ── Steps 3–6: NLP analysis (run concurrently) ───────────────────────────
    ner_extractor    = _get_nlp("ner_extractor",    EntityExtractor)
    sentiment_analyzer = _get_nlp("sentiment",      SentimentAnalyzer)
    clickbait_detector = _get_nlp("clickbait",      ClickbaitDetector)
    claim_extractor  = _get_nlp("claim_extractor",  ClaimExtractor)

    ner_result = ner_extractor.extract(text)
    sentiment_result = sentiment_analyzer.analyze(proc.cleaned)
    clickbait_result = clickbait_detector.detect(text)
    claim_result = claim_extractor.extract(proc.cleaned)

    # ── Step 7: Layer 1 — ML Classifier ──────────────────────────────────────
    # Try fine-tuned XLM-RoBERTa first; fall back to TF-IDF baseline if the
    # checkpoint hasn't been generated yet (ml/train_xlmr.py not yet run).
    model_tier = "xlmr"  # for observability in logs
    try:
        from ml.xlm_roberta_classifier import XLMRobertaClassifier, ModelNotFoundError
        classifier = _get_nlp("xlmr_classifier", XLMRobertaClassifier)
    except ModelNotFoundError:
        logger.info("XLM-RoBERTa checkpoint not found — falling back to TF-IDF baseline")
        from ml.tfidf_classifier import TFIDFClassifier
        def _make_tfidf():
            c = TFIDFClassifier(); c.train(); return c
        classifier = _get_nlp("tfidf_classifier", _make_tfidf)
        model_tier = "tfidf"
    except Exception as exc:
        logger.warning("XLM-RoBERTa load failed (%s) — falling back to TF-IDF", exc)
        from ml.tfidf_classifier import TFIDFClassifier
        def _make_tfidf():  # noqa: F811
            c = TFIDFClassifier(); c.train(); return c
        classifier = _get_nlp("tfidf_classifier", _make_tfidf)
        model_tier = "tfidf"

    l1 = classifier.predict(proc.cleaned)
    logger.debug("Layer-1 (%s): %s %.1f%%", model_tier, l1.verdict, l1.confidence)

    # Enrich triggered features with NLP signals
    if clickbait_result.is_clickbait:
        l1.triggered_features.extend(clickbait_result.triggered_patterns[:3])
    if sentiment_result.sentiment in ("high negative",):
        l1.triggered_features.append("high emotional language")

    layer1 = Layer1Result(
        verdict=Verdict(l1.verdict),
        confidence=l1.confidence,
        triggered_features=l1.triggered_features,
    )

    # ── Step 8: Layer 2 — Evidence Retrieval ──────────────────────────────────
    # Default evidence score depends on source domain tier when no API key is set:
    #   Tier 1 (Inquirer, GMA, Rappler…) → 65  – known credible, not neutral
    #   Tier 2 (satire/opinion)           → 45  – slight skepticism
    #   Tier 3 (unknown)                  → 50  – neutral
    #   Tier 4 (blacklisted)              → 25  – heavy prior against
    _src_tier_pre = get_domain_tier(source_domain) if source_domain else None
    _EVIDENCE_DEFAULTS: dict = {
        DomainTier.CREDIBLE:       65.0,
        DomainTier.SATIRE_OPINION: 45.0,
        DomainTier.SUSPICIOUS:     50.0,
        DomainTier.KNOWN_FAKE:     25.0,
    }
    evidence_score = _EVIDENCE_DEFAULTS.get(_src_tier_pre, 50.0) if _src_tier_pre else 50.0
    evidence_sources: list[EvidenceSource] = []
    l2_verdict = Verdict.UNVERIFIED

    if settings.news_api_key:
        try:
            query_entities = ner_result.persons + ner_result.organizations + ner_result.locations
            articles = await fetch_evidence(
                claim_result.claim, 
                settings.news_api_key, 
                entities=query_entities
            )
            for art in articles[:5]:
                article_text = f"{art.get('title', '')} {art.get('description', '')}"
                sim = compute_similarity(claim_result.claim, article_text)
                domain = (art.get("source", {}) or {}).get("name", "unknown").lower()
                tier = get_domain_tier(domain)

                # Simple stance heuristic — negative title keywords → Refutes
                title_lower = (art.get("title") or "").lower()
                stance = Stance.NOT_ENOUGH_INFO
                if any(w in title_lower for w in ["false", "fake", "hoax", "wrong", "debunked", "fact check"]):
                    stance = Stance.REFUTES
                elif sim > 0.6:
                    stance = Stance.SUPPORTS

                evidence_sources.append(EvidenceSource(
                    title=art.get("title", ""),
                    url=art.get("url", ""),
                    similarity=sim,
                    stance=stance,
                    domain_tier=tier or DomainTier.SUSPICIOUS,
                    published_at=art.get("publishedAt"),
                    source_name=art.get("source", {}).get("name"),
                ))

            # Evidence score: average similarity × 100, penalized for refuting sources
            if evidence_sources:
                supporting = [s for s in evidence_sources if s.stance == Stance.SUPPORTS]
                refuting = [s for s in evidence_sources if s.stance == Stance.REFUTES]
                avg_sim = sum(s.similarity for s in evidence_sources) / len(evidence_sources)
                refute_penalty = len(refuting) * 15
                evidence_score = max(0.0, min(100.0, avg_sim * 100 - refute_penalty))

                if len(refuting) > len(supporting):
                    l2_verdict = Verdict.LIKELY_FAKE
                elif len(supporting) >= 2:
                    l2_verdict = Verdict.CREDIBLE
        except Exception as e:
            logger.warning("Evidence retrieval failed: %s — using neutral score", e)

    layer2 = Layer2Result(
        verdict=l2_verdict,
        evidence_score=round(evidence_score, 1),
        sources=evidence_sources,
        claim_used=claim_result.claim,
    )

    # ── Step 9: Final Score ───────────────────────────────────────────────────
    # ML confidence is 0-100 where high = more credible for the predicted class.
    # Adjust: if ML says Fake, its confidence works against credibility.
    ml_credibility = l1.confidence if l1.verdict == "Credible" else (100 - l1.confidence)
    base_score = (ml_credibility * settings.ml_weight) + (evidence_score * settings.evidence_weight)

    # Domain credibility adjustment — applied when we know the source URL.
    # The adjustment scales with how much ML disagrees with the domain tier:
    #   - Tier 1 source but ML says Fake at high confidence → bigger boost needed
    #   - Tier 4 source but ML says Credible at high confidence → bigger penalty
    # Base adjustments are scaled up by a "disagreement multiplier" (1.0–2.0)
    # so that a 95%-confident ML prediction on a Tier 1 source still respects
    # the fact that the article came from a verified outlet.
    domain_tier = get_domain_tier(source_domain) if source_domain else None
    domain_adjustment = 0.0
    if domain_tier is not None:
        _BASE_ADJ = {
            DomainTier.CREDIBLE:       +20.0,   # Tier 1 — established PH news orgs
            DomainTier.SATIRE_OPINION:  -5.0,   # Tier 2 — satire / opinion blogs
            DomainTier.SUSPICIOUS:     -10.0,   # Tier 3 — unknown / unverified
            DomainTier.KNOWN_FAKE:     -35.0,   # Tier 4 — blacklisted
        }
        base_adj = _BASE_ADJ.get(domain_tier, 0.0)

        # Disagreement multiplier: how much does ML diverge from what the domain implies?
        # Tier 1 implies credible (75), Tier 4 implies fake (25); others neutral (50)
        _TIER_IMPLIED_SCORE = {
            DomainTier.CREDIBLE: 75.0,
            DomainTier.SATIRE_OPINION: 50.0,
            DomainTier.SUSPICIOUS: 50.0,
            DomainTier.KNOWN_FAKE: 25.0,
        }
        implied = _TIER_IMPLIED_SCORE.get(domain_tier, 50.0)
        disagreement = abs(ml_credibility - implied) / 50.0   # 0.0 – 1.0+, capped below
        multiplier = min(1.5, 1.0 + disagreement * 0.5)      # 1.0 (agree) → 1.5 (hard disagree)

        domain_adjustment = base_adj * multiplier
        logger.info(
            "Domain credibility: %s (Tier %s) base=%+.0f × multiplier=%.2f → %+.1f pts  "
            "(ml_credibility=%.1f, implied=%.0f)",
            source_domain, domain_tier.value, base_adj, multiplier, domain_adjustment,
            ml_credibility, implied,
        )

    final_score = round(min(100.0, max(0.0, base_score + domain_adjustment)), 1)
    verdict = _map_verdict(final_score)

    # ── Step 10: Assemble response ────────────────────────────────────────────
    result = VerificationResponse(
        verdict=verdict,
        confidence=round(max(l1.confidence, evidence_score / 100 * 100), 1),
        final_score=final_score,
        layer1=layer1,
        layer2=layer2,
        entities=EntitiesResult(
            persons=ner_result.persons,
            organizations=ner_result.organizations,
            locations=ner_result.locations,
            dates=ner_result.dates,
        ),
        sentiment=sentiment_result.sentiment,
        emotion=sentiment_result.emotion,
        language=language,
        domain_credibility=get_domain_tier(source_domain) if source_domain else None,
        input_type=input_type,
    )

    # ── Record to Firestore (falls back to in-memory if Firebase not configured) ─
    history_entry = {
        "id": str(uuid.uuid4()),
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "input_type": input_type,
        "text_preview": text[:120],
        "verdict": verdict.value,
        "confidence": result.confidence,
        "final_score": final_score,
        "entities": ner_result.to_dict(),
        "claim_used": claim_result.claim,
        "layer1": {
            "verdict": layer1.verdict.value,
            "confidence": layer1.confidence,
            "triggered_features": layer1.triggered_features,
        },
        "layer2": {
            "verdict": layer2.verdict.value,
            "evidence_score": layer2.evidence_score,
            "claim_used": layer2.claim_used,
        },
        "sentiment": sentiment_result.sentiment,
        "emotion": sentiment_result.emotion,
        "language": language.value,
    }
    try:
        from firebase_client import save_verification
        saved = await save_verification(history_entry)
        if not saved:
            # Firestore unavailable — fall back to in-memory store
            from api.routes.history import record_verification
            record_verification(history_entry)
    except Exception as e:
        logger.warning("Failed to record history: %s", e)
        try:
            from api.routes.history import record_verification
            record_verification(history_entry)
        except Exception:
            pass

    return result