"""5-dimension job scoring engine — adapted from JustHireMe.

Replaces the simple keyword counter with a proper weighted scorer:
  1. Stack overlap (40%) — exact + adjacent-skill credit
  2. Role alignment (25%) — domain match between resume and job
  3. Seniority fit (20%) — level compatibility, hard caps for severe mismatch
  4. Job quality (15%) — description richness, company presence

Returns a 0-100 integer match_score.
"""
from __future__ import annotations

import re
import logging
from typing import Dict, List, Optional, Set

logger = logging.getLogger(__name__)


# ── Tech category taxonomy (adapted from JustHireMe) ─────────────────────────
# Maps lowercase skill → category. Skills in the same category are "adjacent".
TECH_CATEGORY: Dict[str, str] = {
    # Languages
    "python": "lang", "javascript": "lang", "typescript": "lang", "java": "lang",
    "c++": "lang", "c#": "lang", "go": "lang", "golang": "lang", "rust": "lang",
    "ruby": "lang", "php": "lang", "swift": "lang", "kotlin": "lang", "scala": "lang",
    "r": "lang", "matlab": "lang", "dart": "lang", "elixir": "lang",
    "bash": "lang", "shell": "lang", "powershell": "lang",
    # Frontend frameworks
    "react": "frontend", "reactjs": "frontend", "react.js": "frontend",
    "vue": "frontend", "vue.js": "frontend", "angular": "frontend",
    "svelte": "frontend", "next.js": "frontend", "nextjs": "frontend",
    "nuxt": "frontend", "gatsby": "frontend", "jquery": "frontend",
    # Backend frameworks
    "node.js": "backend_fw", "nodejs": "backend_fw", "express": "backend_fw",
    "fastapi": "backend_fw", "django": "backend_fw", "flask": "backend_fw",
    "spring": "backend_fw", "spring boot": "backend_fw", "rails": "backend_fw",
    "laravel": "backend_fw", "fastify": "backend_fw", "nestjs": "backend_fw",
    "gin": "backend_fw", "fiber": "backend_fw", "echo": "backend_fw",
    # Cloud / infra
    "aws": "cloud", "azure": "cloud", "gcp": "cloud", "google cloud": "cloud",
    "digitalocean": "cloud", "heroku": "cloud", "vercel": "cloud", "netlify": "cloud",
    # DevOps / containers
    "docker": "devops", "kubernetes": "devops", "k8s": "devops",
    "terraform": "devops", "ansible": "devops", "helm": "devops",
    "jenkins": "devops", "github actions": "devops", "circleci": "devops",
    "gitlab ci": "devops", "argocd": "devops",
    # Databases (relational)
    "mysql": "db_rel", "postgresql": "db_rel", "postgres": "db_rel",
    "sqlite": "db_rel", "mariadb": "db_rel", "sql server": "db_rel",
    # Databases (NoSQL)
    "mongodb": "db_nosql", "dynamodb": "db_nosql", "cassandra": "db_nosql",
    "couchdb": "db_nosql", "firebase": "db_nosql", "firestore": "db_nosql",
    # Caching / messaging
    "redis": "cache_msg", "memcached": "cache_msg",
    "kafka": "cache_msg", "rabbitmq": "cache_msg", "celery": "cache_msg",
    # ML / AI
    "tensorflow": "ml", "pytorch": "ml", "keras": "ml",
    "scikit-learn": "ml", "sklearn": "ml", "pandas": "ml", "numpy": "ml",
    "huggingface": "ml", "transformers": "ml", "xgboost": "ml", "lightgbm": "ml",
    # Mobile
    "react native": "mobile", "flutter": "mobile", "ionic": "mobile",
    "swiftui": "mobile", "jetpack compose": "mobile", "android": "mobile",
    # CSS / styling
    "tailwind": "css", "tailwindcss": "css", "bootstrap": "css",
    "sass": "css", "scss": "css", "css": "css",
    # Testing
    "jest": "testing", "pytest": "testing", "junit": "testing",
    "selenium": "testing", "cypress": "testing", "playwright": "testing",
}

# Domain → role keyword signals in job title/description
DOMAIN_ROLE_SIGNALS: Dict[str, List[str]] = {
    "Software / IT": [
        "software engineer", "developer", "backend", "frontend", "full stack",
        "fullstack", "web developer", "platform engineer", "sre", "devops",
    ],
    "Data / AI": [
        "data scientist", "data engineer", "ml engineer", "machine learning",
        "ai engineer", "nlp", "data analyst", "analytics engineer",
    ],
    "DevOps / Cloud": [
        "devops", "sre", "cloud engineer", "platform engineer", "infrastructure",
        "reliability", "kubernetes", "terraform",
    ],
    "Mobile": [
        "ios", "android", "mobile developer", "react native", "flutter",
    ],
    "Design / UX": [
        "designer", "ux", "ui", "product design", "figma",
    ],
    "Management": [
        "manager", "director", "head of", "vp ", "chief", "lead",
    ],
}

# Seniority signals
_SENIOR_SIGNALS = (
    "senior", "staff", "principal", "lead", "architect",
    "5+ years", "7+ years", "10+ years", "15+ years",
)
_JUNIOR_SIGNALS = (
    "junior", "entry level", "entry-level", "new grad", "graduate",
    "fresher", "intern", "0-2 years", "1-2 years",
)
_MID_SIGNALS = ("mid-level", "mid level", "3+ years", "3-5 years", "2-4 years")

# Skills to extract from job descriptions
_JD_SKILL_PATTERNS = re.compile(
    r'\b(' + '|'.join(re.escape(s) for s in sorted(TECH_CATEGORY.keys(), key=len, reverse=True)) + r')\b',
    re.IGNORECASE,
)


def _extract_jd_skills(text: str) -> List[str]:
    """Extract recognized tech skills from a job description."""
    if not text:
        return []
    found = []
    seen = set()
    for m in _JD_SKILL_PATTERNS.finditer(text):
        s = m.group(0).lower()
        if s not in seen:
            seen.add(s)
            found.append(s)
    return found


def _flatten_skills(skills_dict) -> Set[str]:
    """Flatten candidate skills dict to a set of lowercase skill names."""
    if isinstance(skills_dict, dict):
        flat = set()
        for v in skills_dict.values():
            if isinstance(v, list):
                flat.update(s.lower() for s in v)
        return flat
    if isinstance(skills_dict, list):
        return set(s.lower() for s in skills_dict)
    return set()


def _stack_overlap(candidate_skills: Set[str], jd_skills: List[str]) -> int:
    """
    Score 0-100 based on how many required skills the candidate covers.
    Exact match = 1.0 credit, adjacent category match = 0.30 credit.
    """
    if not jd_skills:
        return 50  # no skill requirements extracted → neutral

    total_credit = 0.0
    for req in jd_skills:
        req_lower = req.lower()
        if req_lower in candidate_skills:
            total_credit += 1.0
        else:
            # Adjacent credit: same tech category, different skill
            req_cat = TECH_CATEGORY.get(req_lower)
            if req_cat:
                for cand in candidate_skills:
                    if TECH_CATEGORY.get(cand) == req_cat:
                        total_credit += 0.30
                        break

    coverage = total_credit / len(jd_skills)
    return min(100, int(coverage * 130))  # 130 factor: 77% coverage → 100


def _role_alignment(job: dict, domain: str) -> int:
    """Score 0-100: how well the job role matches the resume domain."""
    if not domain:
        return 60  # neutral

    text = (str(job.get("title") or "") + " " + str(job.get("description") or "")).lower()
    signals = DOMAIN_ROLE_SIGNALS.get(domain, [])

    # Direct domain hit
    matches = sum(1 for s in signals if s in text)
    if matches >= 3:
        return 95
    if matches == 2:
        return 80
    if matches == 1:
        return 65

    # Heavy penalty: tech resume → clearly non-tech job
    non_tech_patterns = [
        "sales manager", "marketing manager", "account executive", "recruiter",
        "human resources", "real estate", "insurance agent", "financial advisor",
        "business development manager", "operations coordinator",
    ]
    if domain in ("Software / IT", "Data / AI", "DevOps / Cloud", "Mobile"):
        if any(p in text for p in non_tech_patterns):
            return 5

    return 45  # no direct signal — possible but uncertain


def _seniority_fit(job: dict, resume_text: str) -> int:
    """
    Score 0-100 based on seniority alignment.
    Applies hard cap: fresher applying to senior-only role → max 25.
    """
    job_text = (str(job.get("title") or "") + " " + str(job.get("description") or "")).lower()
    resume_lower = (resume_text or "").lower()[:3000]  # first 3000 chars

    job_level = "unknown"
    if any(s in job_text for s in _SENIOR_SIGNALS):
        job_level = "senior"
    elif any(s in job_text for s in _MID_SIGNALS):
        job_level = "mid"
    elif any(s in job_text for s in _JUNIOR_SIGNALS):
        job_level = "junior"

    # Candidate level from resume text
    resume_years = 0
    m = re.search(r'(\d+)\+?\s*years?\s+(?:of\s+)?experience', resume_lower)
    if m:
        resume_years = int(m.group(1))

    has_senior_title = any(s in resume_lower for s in ("senior ", "lead ", "principal ", "staff "))
    has_junior_title = any(s in resume_lower for s in ("junior ", "entry level", "fresher", "intern"))

    if has_senior_title or resume_years >= 5:
        candidate_level = "senior"
    elif resume_years >= 2:
        candidate_level = "mid"
    elif has_junior_title or resume_years <= 1:
        candidate_level = "junior"
    else:
        candidate_level = "unknown"

    if job_level == "unknown":
        return 70  # no seniority specified → neutral

    if candidate_level == "unknown":
        return 65  # can't assess → slight neutral

    # Scoring matrix: (job_level, candidate_level) → score
    matrix = {
        ("senior", "senior"): 95,
        ("senior", "mid"):    75,
        ("senior", "junior"): 20,  # hard cap — underqualified
        ("mid",    "senior"): 65,  # overqualified — employer likely won't hire
        ("mid",    "mid"):    95,
        ("mid",    "junior"): 50,
        ("junior", "senior"): 40,  # overqualified — employer likely won't hire
        ("junior", "mid"):    70,
        ("junior", "junior"): 95,
    }
    return matrix.get((job_level, candidate_level), 65)


def _job_quality(job: dict) -> int:
    """Score 0-100 based on listing quality: description richness, company presence."""
    score = 50
    desc = str(job.get("description") or job.get("snippet") or "")
    words = len(desc.split())
    if words >= 200:
        score += 30
    elif words >= 80:
        score += 20
    elif words >= 30:
        score += 10

    if job.get("company"):
        score += 10
    if job.get("date_posted"):
        score += 5
    if job.get("location"):
        score += 5

    return min(100, score)


def score_job(
    job: dict,
    candidate_skills,  # dict from skill_extractor OR flat list
    resume_text: str = "",
    domain: str = "",
) -> int:
    """
    Compute a 0-100 match score using 4 weighted dimensions.

    Weights:
      stack_overlap   40%
      role_alignment  25%
      seniority_fit   20%
      job_quality     15%
    """
    jd_text = str(job.get("description") or job.get("snippet") or "")
    jd_skills = _extract_jd_skills(jd_text + " " + str(job.get("title") or ""))
    candidate_set = _flatten_skills(candidate_skills)

    stack   = _stack_overlap(candidate_set, jd_skills)
    role    = _role_alignment(job, domain)
    senior  = _seniority_fit(job, resume_text)
    quality = _job_quality(job)

    raw = stack * 0.40 + role * 0.25 + senior * 0.20 + quality * 0.15
    score = max(1, min(100, int(raw)))

    logger.debug(
        f"[scoring] '{job.get('title','')}' → {score} "
        f"(stack={stack} role={role} senior={senior} quality={quality})"
    )
    return score


def rescore_jobs(
    jobs: List[dict],
    candidate_skills,
    resume_text: str = "",
    domain: str = "",
) -> List[dict]:
    """Apply score_job to all jobs, replacing match_score. Returns sorted list."""
    for job in jobs:
        job["match_score"] = score_job(job, candidate_skills, resume_text, domain)
    jobs.sort(key=lambda j: j["match_score"], reverse=True)
    return jobs