from __future__ import annotations

import re

from crawler_platform.app.config.loader import ProjectConfig
from crawler_platform.app.core.extractor.base import ExtractedClaim, ExtractedEntity, ExtractionBundle
from crawler_platform.app.core.extractor.rule_based import GenericRuleBasedExtractor, find_price, first_non_empty_line
from crawler_platform.app.core.ontology.mapper import normalize_predicate


NOTE_LABELS = {
    "top_notes": ["top notes", "top note", "opening notes", "탑 노트", "탑노트", "상단 노트"],
    "middle_notes": ["middle notes", "heart notes", "미들 노트", "하트 노트", "미들노트"],
    "base_notes": ["base notes", "base note", "베이스 노트", "베이스노트", "잔향"],
}

FIELD_TO_PREDICATE = {
    "top_notes": "hasTopNote",
    "middle_notes": "hasMiddleNote",
    "base_notes": "hasBaseNote",
    "accords": "hasAccord",
    "mood_tags": "evokesMood",
    "season_tags": "suitableForSeason",
    "occasion_tags": "suitableForOccasion",
    "review_keywords": "hasReviewKeyword",
}

MOOD_KEYWORDS = {
    "Fresh": ["fresh", "clean", "상쾌", "깨끗", "청량"],
    "Romantic": ["romantic", "soft", "로맨틱", "부드러운"],
    "Elegant": ["elegant", "luxury", "우아", "고급"],
    "Cozy": ["cozy", "warm", "포근", "따뜻"],
    "Energetic": ["bright", "sparkling", "활기", "발랄"],
}

SEASON_KEYWORDS = {
    "Spring": ["spring", "봄"],
    "Summer": ["summer", "여름"],
    "Autumn": ["autumn", "fall", "가을"],
    "Winter": ["winter", "겨울"],
}

OCCASION_KEYWORDS = {
    "Daily": ["daily", "everyday", "데일리", "매일"],
    "Date": ["date", "데이트"],
    "Office": ["office", "work", "오피스", "출근"],
    "Evening": ["evening", "night", "저녁", "밤"],
}

ACCORD_KEYWORDS = [
    "citrus",
    "floral",
    "woody",
    "musky",
    "amber",
    "powdery",
    "green",
    "spicy",
    "sweet",
    "fresh",
    "시트러스",
    "플로럴",
    "우디",
    "머스크",
    "앰버",
    "파우더리",
]

REVIEW_KEYWORDS = [
    "long lasting",
    "compliment",
    "too strong",
    "soft",
    "fresh",
    "powdery",
    "지속력",
    "잔향",
    "호불호",
    "칭찬",
    "은은",
    "강한",
]


class PerfumeRuleBasedExtractor(GenericRuleBasedExtractor):
    name = "perfume_rule_based"

    def extract_entities(self, page_text: str, project_config: ProjectConfig) -> list[ExtractedEntity]:
        product_name = extract_product_name(page_text)
        attrs = {"name": product_name}
        brand = extract_brand(page_text, product_name)
        if brand:
            attrs["brand"] = brand
        price = find_price(page_text)
        if price:
            attrs["price"] = {k: v for k, v in price.items() if k != "evidence"}
        entities = [ExtractedEntity("Perfume", product_name, attrs, confidence=0.68)]
        if brand:
            entities.append(ExtractedEntity("Brand", brand, confidence=0.62))
        for field, entity_type in [
            ("top_notes", "Note"),
            ("middle_notes", "Note"),
            ("base_notes", "Note"),
            ("accords", "Accord"),
            ("mood_tags", "Mood"),
            ("season_tags", "Season"),
            ("occasion_tags", "Occasion"),
            ("review_keywords", "Review"),
        ]:
            for value, evidence in extract_field_values(field, page_text):
                entities.append(ExtractedEntity(entity_type, value, evidence_text=evidence, confidence=0.6))
        return dedupe_entities(entities)

    def extract_attributes(
        self,
        entity: ExtractedEntity,
        page_text: str,
        project_config: ProjectConfig,
    ) -> dict[str, object]:
        if entity.entity_type != "Perfume":
            return {}
        attrs: dict[str, object] = {}
        longevity = find_metric(page_text, ["longevity", "lasting", "지속력"])
        sillage = find_metric(page_text, ["sillage", "projection", "확산력", "발향"])
        if longevity:
            attrs["longevity"] = longevity
        if sillage:
            attrs["sillage"] = sillage
        gender_bias = find_gender_bias(page_text)
        if gender_bias:
            attrs["gender_bias"] = gender_bias
        return attrs

    def extract_relations(
        self,
        entities: list[ExtractedEntity],
        page_text: str,
        project_config: ProjectConfig,
    ) -> list[ExtractedClaim]:
        perfume = next((entity for entity in entities if entity.entity_type == "Perfume"), None)
        if perfume is None:
            return []
        claims: list[ExtractedClaim] = []
        brand = next((entity for entity in entities if entity.entity_type == "Brand"), None)
        if brand:
            claims.append(
                ExtractedClaim(
                    perfume.name,
                    "Perfume",
                    "hasBrand",
                    brand.name,
                    "Brand",
                    evidence_text=brand.evidence_text or brand.name,
                    confidence=0.78,
                    confidence_reason="brand pattern matched",
                )
            )
        for field, predicate in FIELD_TO_PREDICATE.items():
            entity_type = field_entity_type(field)
            for value, evidence in extract_field_values(field, page_text):
                claims.append(
                    ExtractedClaim(
                        perfume.name,
                        "Perfume",
                        predicate,
                        value,
                        entity_type,
                        evidence_text=evidence,
                        evidence_summary=f"{field} includes {value}",
                        confidence=field_confidence(field),
                        confidence_reason=f"{field} rule matched",
                    )
                )
        price = find_price(page_text)
        if price:
            claims.append(
                ExtractedClaim(
                    perfume.name,
                    "Perfume",
                    "hasPrice",
                    object_value={k: v for k, v in price.items() if k != "evidence"},
                    evidence_text=price["evidence"],
                    confidence=0.7,
                    confidence_reason="price pattern matched",
                )
            )
        return claims

    def normalize_to_ontology(self, bundle: ExtractionBundle, ontology: dict[str, object]) -> ExtractionBundle:
        for claim in bundle.claims:
            claim.predicate = normalize_predicate(claim.predicate, ontology)
        return bundle


def extract_product_name(page_text: str) -> str:
    for line in page_text.splitlines()[:8]:
        clean = line.strip()
        if clean and not looks_like_navigation(clean):
            return clean[:240]
    return first_non_empty_line(page_text) or "Unknown Perfume"


def extract_brand(page_text: str, product_name: str) -> str | None:
    patterns = [
        r"(?:brand|브랜드)\s*[:：]\s*(?P<brand>[A-Za-z0-9가-힣 '&.-]{2,80})",
        r"by\s+(?P<brand>[A-Z][A-Za-z0-9 '&.-]{2,80})",
    ]
    for pattern in patterns:
        match = re.search(pattern, page_text, flags=re.IGNORECASE)
        if match:
            return cleanup_value(match.group("brand"))
    lines = [line.strip() for line in page_text.splitlines() if line.strip()]
    if len(lines) >= 2 and lines[1].lower() not in product_name.lower():
        candidate = cleanup_value(lines[1])
        if len(candidate) <= 80 and not looks_like_navigation(candidate):
            return candidate
    return None


def extract_field_values(field: str, page_text: str) -> list[tuple[str, str]]:
    if field in NOTE_LABELS:
        return extract_labeled_values(page_text, NOTE_LABELS[field])
    if field == "accords":
        return keyword_values(page_text, ACCORD_KEYWORDS)
    if field == "mood_tags":
        return taxonomy_keyword_values(page_text, MOOD_KEYWORDS)
    if field == "season_tags":
        return taxonomy_keyword_values(page_text, SEASON_KEYWORDS)
    if field == "occasion_tags":
        return taxonomy_keyword_values(page_text, OCCASION_KEYWORDS)
    if field == "review_keywords":
        return keyword_values(page_text, REVIEW_KEYWORDS)
    return []


def extract_labeled_values(page_text: str, labels: list[str]) -> list[tuple[str, str]]:
    values: list[tuple[str, str]] = []
    lines = page_text.splitlines()
    for idx, line in enumerate(lines):
        lower = line.lower()
        if any(label.lower() in lower for label in labels):
            evidence = line
            raw = line.split(":", 1)[-1] if ":" in line else ""
            if not raw and idx + 1 < len(lines):
                raw = lines[idx + 1]
                evidence = f"{line} {raw}"
            for value in split_values(raw):
                values.append((value, evidence[:1000]))
    return values


def split_values(raw: str) -> list[str]:
    raw = re.sub(r"\([^)]*\)", "", raw)
    parts = re.split(r"[,/|·ㆍ]+|\band\b| 및 | 그리고 ", raw, flags=re.IGNORECASE)
    return [cleanup_value(part) for part in parts if 1 < len(cleanup_value(part)) <= 80]


def keyword_values(page_text: str, keywords: list[str]) -> list[tuple[str, str]]:
    lower = page_text.lower()
    found: list[tuple[str, str]] = []
    for keyword in keywords:
        if keyword.lower() in lower:
            found.append((keyword.title() if keyword.isascii() else keyword, snippet_for(page_text, keyword)))
    return found


def taxonomy_keyword_values(page_text: str, taxonomy: dict[str, list[str]]) -> list[tuple[str, str]]:
    lower = page_text.lower()
    found: list[tuple[str, str]] = []
    for label, keywords in taxonomy.items():
        for keyword in keywords:
            if keyword.lower() in lower:
                found.append((label, snippet_for(page_text, keyword)))
                break
    return found


def snippet_for(text: str, keyword: str, window: int = 160) -> str:
    index = text.lower().find(keyword.lower())
    if index < 0:
        return keyword
    start = max(index - window // 2, 0)
    end = min(index + len(keyword) + window // 2, len(text))
    return text[start:end].replace("\n", " ")


def cleanup_value(value: str) -> str:
    return re.sub(r"\s+", " ", value.strip(" -:：[]()")).strip()


def looks_like_navigation(value: str) -> bool:
    return value.lower() in {"home", "shop", "menu", "cart", "login", "검색", "장바구니", "홈"}


def field_entity_type(field: str) -> str:
    return {
        "top_notes": "Note",
        "middle_notes": "Note",
        "base_notes": "Note",
        "accords": "Accord",
        "mood_tags": "Mood",
        "season_tags": "Season",
        "occasion_tags": "Occasion",
        "review_keywords": "Review",
    }[field]


def field_confidence(field: str) -> float:
    return {
        "top_notes": 0.82,
        "middle_notes": 0.82,
        "base_notes": 0.82,
        "accords": 0.66,
        "mood_tags": 0.62,
        "season_tags": 0.62,
        "occasion_tags": 0.6,
        "review_keywords": 0.58,
    }[field]


def find_metric(page_text: str, labels: list[str]) -> str | None:
    for label in labels:
        match = re.search(rf"{label}\s*[:：]?\s*(?P<value>\d(?:\.\d)?/5|moderate|strong|weak|long|short|좋음|강함|약함)", page_text, re.IGNORECASE)
        if match:
            return cleanup_value(match.group("value"))
    return None


def find_gender_bias(page_text: str) -> str | None:
    lower = page_text.lower()
    if "unisex" in lower or "공용" in lower:
        return "unisex"
    if "for women" in lower or "여성" in lower:
        return "feminine"
    if "for men" in lower or "남성" in lower:
        return "masculine"
    return None


def dedupe_entities(entities: list[ExtractedEntity]) -> list[ExtractedEntity]:
    seen: set[tuple[str, str]] = set()
    result: list[ExtractedEntity] = []
    for entity in entities:
        key = (entity.entity_type, entity.name.strip().lower())
        if key in seen:
            continue
        seen.add(key)
        result.append(entity)
    return result