"""
Company Matcher for NordaGPT
==============================

Pre-selects companies from the database BEFORE the AI sees them,
eliminating hallucinations by ensuring only real companies with
rich profiles are passed as context.

Multi-layer matching:
1. Category matching (query keywords -> category names)
2. Full-text search on search_vector (tsvector GIN index)
3. ILIKE on services_offered, description_full, description_short
4. AI Insights tags (industry_tags, services_list from company_ai_insights)
5. PKD codes matching

Author: Maciej Pienczyn, InPi sp. z o.o.
Created: 2026-03-28
"""

import re
import logging
from typing import Optional

from sqlalchemy import text
from sqlalchemy.orm import joinedload

from database import (
    SessionLocal, Company, Category, CompanyAIInsights,
    GBPAudit, CompanyService, CompanyCompetency,
    Service, Competency,
)

logger = logging.getLogger(__name__)


# ============================================================
# CATEGORY KEYWORD MAPPING
# ============================================================

CATEGORY_KEYWORDS = {
    'Budownictwo ogólne': [
        'budow', 'budynk', 'konstrukc', 'dekars', 'remontow',
        'stawianie obiektów',
    ],
    'Budownictwo': ['budow', 'budynk'],
    'IT i Technologie': [
        'it', 'informatyk', 'oprogramow', 'software', 'system',
        'serwer', 'technologi',
    ],
    'IT i Telekomunikacja': [
        'telekomunikac', 'internet', 'światłowod', 'sieć', 'łącznoś',
    ],
    'Energia i OZE': [
        'energ', 'elektryk', 'prąd', 'oze', 'fotowolt', 'zasilani',
    ],
    'HVAC i Instalacje': [
        'hvac', 'klimatyz', 'chłodz', 'wentylac', 'instalac', 'grzewcz',
    ],
    'Bezpieczeństwo i Ochrona': [
        'bezpiecz', 'ochron', 'monitoring', 'alarm',
    ],
    'Nieruchomości': [
        'nieruchomoś', 'dewelop', 'gruntów', 'działk',
    ],
    'Usługi prawne': ['prawn', 'adwokat', 'radca', 'kancelari'],
    'Usługi finansowe': [
        'finans', 'rachunk', 'księgow', 'doradztw',
    ],
    'Marketing i Reklama': [
        'marketing', 'reklam', 'promocj', 'media',
    ],
    'Handel i Hurtownie': [
        'handel', 'hurtowni', 'sprzedaż', 'dystrybuc',
    ],
    'Produkcja ogólna': [
        'produkcj', 'produku', 'wytwarz', 'fabryk',
    ],
    'Hotelarstwo i Turystyka': [
        'hotel', 'turyst', 'nocleg', 'gastrono',
    ],
    'Motoryzacja': ['motoryz', 'samochod', 'warsztat'],
}


# ============================================================
# PKD CODE MAPPING
# ============================================================

PKD_KEYWORDS = {
    'budow': ['41', '42', '43'],       # Construction
    'it': ['62', '63'],                 # IT / Software
    'energ': ['35'],                    # Energy
    'transport': ['49', '50', '51'],    # Transport / logistics
    'handel': ['45', '46', '47'],       # Trade
    'produkcj': ['10', '20', '22', '24', '25', '28'],  # Manufacturing
    'hotel': ['55', '56'],              # Hospitality
    'finans': ['64', '66'],             # Finance
    'prawn': ['69'],                    # Legal
    'marketing': ['73'],                # Advertising
    'nieruchomoś': ['68'],              # Real estate
}


# ============================================================
# SCORING WEIGHTS
# ============================================================

SCORE_CATEGORY = 10
SCORE_FTS = 8
SCORE_ILIKE = 5
SCORE_AI_INSIGHTS = 7
SCORE_PKD = 3


# ============================================================
# MAIN ENTRY POINT
# ============================================================

def match_companies(
    query: str,
    user_context: Optional[dict] = None,
    max_results: int = 15,
) -> list[dict]:
    """
    Search the database for companies matching the user's query.
    Returns rich company profiles ready for AI context injection.

    Args:
        query: User's search query in Polish or English.
        user_context: Optional dict with caller info (unused for now).
        max_results: Maximum number of companies to return.

    Returns:
        List of dicts with rich company profiles, sorted by relevance_score desc.
    """
    if not query or not query.strip():
        return []

    query = query.strip()
    query_lower = query.lower()

    db = SessionLocal()
    try:
        # Accumulator: company_id -> {score, reasons}
        scores: dict[int, dict] = {}

        def _add(company_id: int, points: int, reason: str):
            if company_id not in scores:
                scores[company_id] = {'score': 0, 'reasons': []}
            scores[company_id]['score'] += points
            scores[company_id]['reasons'].append(reason)

        # ----------------------------------------------------------
        # Layer 1: Category keyword matching
        # ----------------------------------------------------------
        matched_categories = _match_categories(query_lower)
        if matched_categories:
            cat_rows = (
                db.query(Company.id)
                .join(Category, Company.category_id == Category.id)
                .filter(
                    Category.name.in_(matched_categories),
                    Company.status == 'active',
                )
                .all()
            )
            for (cid,) in cat_rows:
                _add(cid, SCORE_CATEGORY, f"category: {', '.join(matched_categories)}")
            logger.debug(
                "Layer 1 (category): matched %d companies for categories %s",
                len(cat_rows), matched_categories,
            )

        # ----------------------------------------------------------
        # Layer 2: PostgreSQL Full-Text Search (search_vector)
        # ----------------------------------------------------------
        fts_ids = _fts_search(db, query)
        for cid in fts_ids:
            _add(cid, SCORE_FTS, f"fts: {query}")
        logger.debug("Layer 2 (FTS): matched %d companies", len(fts_ids))

        # ----------------------------------------------------------
        # Layer 3: ILIKE on text fields
        # ----------------------------------------------------------
        ilike_ids = _ilike_search(db, query_lower)
        for cid in ilike_ids:
            _add(cid, SCORE_ILIKE, f"ilike: {query}")
        logger.debug("Layer 3 (ILIKE): matched %d companies", len(ilike_ids))

        # ----------------------------------------------------------
        # Layer 4: AI Insights (services_list, industry_tags, USPs)
        # ----------------------------------------------------------
        ai_ids = _ai_insights_search(db, query_lower)
        for cid in ai_ids:
            _add(cid, SCORE_AI_INSIGHTS, f"ai_insights: {query}")
        logger.debug("Layer 4 (AI Insights): matched %d companies", len(ai_ids))

        # ----------------------------------------------------------
        # Layer 5: PKD codes
        # ----------------------------------------------------------
        pkd_ids = _pkd_search(db, query_lower)
        for cid in pkd_ids:
            _add(cid, SCORE_PKD, f"pkd: {query}")
        logger.debug("Layer 5 (PKD): matched %d companies", len(pkd_ids))

        # ----------------------------------------------------------
        # Sort by score and pick top N
        # ----------------------------------------------------------
        if not scores:
            logger.info("company_matcher: no matches for query=%r", query)
            return []

        ranked = sorted(scores.items(), key=lambda x: x[1]['score'], reverse=True)
        top_ids = [cid for cid, _ in ranked[:max_results]]

        # ----------------------------------------------------------
        # Build rich profiles
        # ----------------------------------------------------------
        companies = (
            db.query(Company)
            .options(
                joinedload(Company.category),
                joinedload(Company.services).joinedload(CompanyService.service),
                joinedload(Company.competencies).joinedload(CompanyCompetency.competency),
                joinedload(Company.ai_insights),
            )
            .filter(Company.id.in_(top_ids), Company.status == 'active')
            .all()
        )
        company_map = {c.id: c for c in companies}

        # Fetch latest GBP audit per company (one query)
        gbp_map = _fetch_gbp_audits(db, top_ids)

        results = []
        for cid in top_ids:
            company = company_map.get(cid)
            if not company:
                continue
            info = scores[cid]
            profile = _build_rich_profile(company, info, gbp_map.get(cid))
            results.append(profile)

        logger.info(
            "company_matcher: query=%r -> %d results (top score=%d)",
            query, len(results), results[0]['relevance_score'] if results else 0,
        )
        return results

    except Exception:
        logger.exception("company_matcher error for query=%r", query)
        return []
    finally:
        db.close()


# ============================================================
# LAYER IMPLEMENTATIONS
# ============================================================

def _match_categories(query_lower: str) -> list[str]:
    """Return category names whose keywords appear in the query."""
    matched = []
    for cat_name, keywords in CATEGORY_KEYWORDS.items():
        for kw in keywords:
            if kw in query_lower:
                matched.append(cat_name)
                break
    return matched


def _fts_search(db, query: str) -> list[int]:
    """Full-text search using the search_vector tsvector column."""
    # Sanitize: keep word characters (including Polish diacritics)
    words = re.findall(r'[\w]+', query, flags=re.UNICODE)
    words = [w for w in words if len(w) > 1]
    if not words:
        return []

    tsquery = ' | '.join(f"{w}:*" for w in words)

    sql = text("""
        SELECT c.id
        FROM companies c
        WHERE c.status = 'active'
          AND c.search_vector @@ to_tsquery('simple', :tsquery)
        ORDER BY ts_rank(c.search_vector, to_tsquery('simple', :tsquery)) DESC
        LIMIT 50
    """)
    try:
        rows = db.execute(sql, {'tsquery': tsquery}).fetchall()
        return [r[0] for r in rows]
    except Exception:
        logger.debug("FTS search failed (search_vector may not exist), skipping")
        db.rollback()
        return []


def _ilike_search(db, query_lower: str) -> list[int]:
    """ILIKE search across text columns."""
    words = re.findall(r'[\w]+', query_lower, flags=re.UNICODE)
    words = [w for w in words if len(w) > 2]
    if not words:
        return []

    patterns = [f'%{w}%' for w in words]

    sql = text("""
        SELECT DISTINCT c.id
        FROM companies c
        WHERE c.status = 'active'
          AND (
            c.services_offered ILIKE ANY(:patterns)
            OR c.description_full ILIKE ANY(:patterns)
            OR c.description_short ILIKE ANY(:patterns)
            OR c.founding_history ILIKE ANY(:patterns)
            OR c.technologies_used ILIKE ANY(:patterns)
          )
        LIMIT 50
    """)
    try:
        rows = db.execute(sql, {'patterns': patterns}).fetchall()
        return [r[0] for r in rows]
    except Exception:
        logger.debug("ILIKE search failed, skipping")
        db.rollback()
        return []


def _ai_insights_search(db, query_lower: str) -> list[int]:
    """Search AI Insights arrays: services_list, industry_tags, unique_selling_points."""
    words = re.findall(r'[\w]+', query_lower, flags=re.UNICODE)
    words = [w for w in words if len(w) > 2]
    if not words:
        return []

    # Build an OR of array element ILIKE checks
    # Using unnest to search inside PostgreSQL ARRAY columns
    patterns = [f'%{w}%' for w in words]

    sql = text("""
        SELECT DISTINCT ai.company_id
        FROM company_ai_insights ai
        JOIN companies c ON c.id = ai.company_id AND c.status = 'active'
        WHERE EXISTS (
            SELECT 1 FROM unnest(ai.services_list) s WHERE s ILIKE ANY(:patterns)
        )
        OR EXISTS (
            SELECT 1 FROM unnest(ai.industry_tags) t WHERE t ILIKE ANY(:patterns)
        )
        OR EXISTS (
            SELECT 1 FROM unnest(ai.unique_selling_points) u WHERE u ILIKE ANY(:patterns)
        )
        LIMIT 50
    """)
    try:
        rows = db.execute(sql, {'patterns': patterns}).fetchall()
        return [r[0] for r in rows]
    except Exception:
        logger.debug("AI Insights search failed, skipping")
        db.rollback()
        return []


def _pkd_search(db, query_lower: str) -> list[int]:
    """Match companies by PKD codes derived from query keywords."""
    pkd_prefixes = set()
    for keyword_stem, codes in PKD_KEYWORDS.items():
        if keyword_stem in query_lower:
            pkd_prefixes.update(codes)

    if not pkd_prefixes:
        return []

    # Match pkd_code starting with any of the 2-digit prefixes
    like_patterns = [f'{p}%' for p in pkd_prefixes]

    sql = text("""
        SELECT DISTINCT c.id
        FROM companies c
        WHERE c.status = 'active'
          AND c.pkd_code IS NOT NULL
          AND c.pkd_code LIKE ANY(:patterns)
        LIMIT 50
    """)
    try:
        rows = db.execute(sql, {'patterns': like_patterns}).fetchall()
        return [r[0] for r in rows]
    except Exception:
        logger.debug("PKD search failed, skipping")
        db.rollback()
        return []


# ============================================================
# GBP AUDIT HELPER
# ============================================================

def _fetch_gbp_audits(db, company_ids: list[int]) -> dict:
    """Fetch the latest GBP audit for each company. Returns {company_id: GBPAudit}."""
    if not company_ids:
        return {}

    # Use DISTINCT ON to get the latest audit per company
    sql = text("""
        SELECT DISTINCT ON (company_id) id, company_id
        FROM gbp_audits
        WHERE company_id = ANY(:ids)
        ORDER BY company_id, audit_date DESC
    """)
    try:
        rows = db.execute(sql, {'ids': company_ids}).fetchall()
        audit_ids = [r[0] for r in rows]
        if not audit_ids:
            return {}
        audits = db.query(GBPAudit).filter(GBPAudit.id.in_(audit_ids)).all()
        return {a.company_id: a for a in audits}
    except Exception:
        logger.debug("GBP audit fetch failed, skipping")
        db.rollback()
        return {}


# ============================================================
# RICH PROFILE BUILDER
# ============================================================

def _build_rich_profile(
    company: Company,
    score_info: dict,
    gbp_audit: Optional[GBPAudit],
) -> dict:
    """Build a rich company profile dict for AI context injection."""

    # Structured services from relationship
    structured_services = []
    if company.services:
        for cs in company.services:
            if cs.service and cs.service.name:
                structured_services.append(cs.service.name)

    # Competencies from relationship
    competency_list = []
    if company.competencies:
        for cc in company.competencies:
            if cc.competency and cc.competency.name:
                competency_list.append(cc.competency.name)

    # AI Insights
    ai_data = {}
    if company.ai_insights:
        ai = company.ai_insights
        ai_data = {
            'services_list': ai.services_list or [],
            'unique_selling_points': ai.unique_selling_points or [],
            'target_market': ai.target_market or '',
            'industry_tags': ai.industry_tags or [],
            'certifications': ai.certifications or [],
        }

    # GBP data
    google_rating = None
    google_reviews = None
    if gbp_audit:
        google_rating = float(gbp_audit.average_rating) if gbp_audit.average_rating else None
        google_reviews = gbp_audit.review_count

    # Category name
    category_name = company.category.name if company.category else None

    # Truncate founding_history
    founding = None
    if company.founding_history:
        founding = company.founding_history[:300]
        if len(company.founding_history) > 300:
            founding += '...'

    return {
        'name': company.name,
        'slug': company.slug,
        'link': f'/company/{company.slug}',
        'category': category_name,
        'description': company.description_full or company.description_short or '',
        'services': company.services_offered or '',
        'structured_services': structured_services,
        'competencies': competency_list,
        'ai_insights': ai_data,
        'founding_history': founding,
        'city': company.address_city,
        'website': company.website,
        'phone': company.phone,
        'email': company.email,
        'year_established': company.year_established,
        'employee_range': company.employee_count_range,
        'google_rating': google_rating,
        'google_reviews': google_reviews,
        'relevance_score': score_info['score'],
        'match_reasons': score_info['reasons'],
    }