"""Per-board normalizers: raw Apify item → the frontend `ScoutJob` shape.

Slice-1a: no engine, so matchScore/note/qx are placeholders (matchScore is assigned
in search.py after merge). Each board's output differs — one mapper per board.
"""
from __future__ import annotations

import html as _html
import json
import re

_PALETTE = ["#4F46E5", "#EA580C", "#0F6E56", "#DB2777", "#0EA5E9", "#7C3AED", "#D97706", "#059669"]


def _plain(s, limit: int = 1600) -> str:
    """HTML/Markdown → readable plain text for the detail modal (keeps paragraph breaks).

    We surface ONLY what the board/employer provided — and strip the scraper's own
    aggregator disclaimer boilerplate (we add nothing of our own).
    """
    if not s:
        return ""
    # HTML → text
    s = re.sub(r"<\s*(br|/p|/li|/div|/h\d|/tr)\s*[^>]*>", "\n", str(s), flags=re.I)  # breaks → newlines
    s = re.sub(r"<li[^>]*>", "• ", s, flags=re.I)
    s = re.sub(r"<[^>]+>", "", s)                                                     # drop remaining tags
    s = _html.unescape(s)
    # Markdown leftovers (when the source is markdown, not HTML)
    s = re.sub(r"(?m)^\s{0,3}#{1,6}\s*", "", s)          # ATX headers → plain line
    s = re.sub(r"(?m)^[ \t]*[-*+][ \t]+", "• ", s)       # list bullets → •
    s = re.sub(r"\*\*([^*]+)\*\*", r"\1", s)             # **bold** → text
    s = re.sub(r"`([^`]+)`", r"\1", s)                   # `code` → text
    # Broken-tag remnants (e.g. a leaked  ">  from malformed HTML)
    s = re.sub(r'"\s*>', " ", s)
    s = s.lstrip(' "\'>')
    # Drop the scraper's appended disclaimer boilerplate (NOT employer content)
    s = re.split(r"\n?\s*Disclaimer\s*[:\-]", s, maxsplit=1, flags=re.I)[0]
    # whitespace
    s = re.sub(r"[ \t]+", " ", s)
    s = re.sub(r"\n{3,}", "\n\n", s).strip()
    return (s[:limit].rstrip() + "…") if len(s) > limit else s


def _compact(d: dict) -> dict:
    """Drop empty values so the details payload stays lean."""
    return {k: v for k, v in d.items() if v not in (None, "", [], {})}


def _logo(org: str, url: str | None = None) -> dict:
    org = (org or "").strip()
    label = org[0].upper() if org else "?"
    bg = _PALETTE[sum(map(ord, org)) % len(_PALETTE)] if org else "#9CA3AF"
    d = {"label": label, "bg": bg}
    # Real company logo from the actor (used by the UI; the label/bg stay as the fallback).
    if url and isinstance(url, str) and url.startswith("http"):
        d["url"] = url
    return d


# actor field names for the company logo, across boards + nesting (Naukri=logoPath,
# LinkedIn=company.logo, Indeed=companyLogo, …). First http(s) value wins; None → letter badge.
_LOGO_KEYS = ("logoPath", "logoPathV3", "logo", "companyLogo", "company_logo", "logoUrl", "image", "companyImage")


def _logo_url(it: dict) -> str | None:
    scopes = [it, it.get("company") or {}, it.get("jobDetails") or {},
              (it.get("jobDetails") or {}).get("companyDetail") or {}]
    for sc in scopes:
        if not isinstance(sc, dict):
            continue
        for k in _LOGO_KEYS:
            v = sc.get(k)
            if isinstance(v, str) and v.startswith("http"):
                return v
    return None


# posting-date field across boards (Naukri createdDate · LinkedIn postedDate · Foundit date_posted).
# Feeds the dashboard "Apply window" (freshness urgency) + honest "posted Nd ago" age signals.
_POSTED_KEYS = ("postedDate", "createdDate", "date_posted", "postedAt", "listedAt", "datePosted", "published")


def _posted_date(it: dict) -> str | None:
    scopes = [it, it.get("jobDetails") or {}]
    for sc in scopes:
        if not isinstance(sc, dict):
            continue
        for k in _POSTED_KEYS:
            v = sc.get(k)
            if isinstance(v, str) and v[:4].isdigit():   # ISO-ish "2026-06-18T…"
                return v
    return None


def _digits(s: str) -> int | None:
    nums = re.sub(r"[^\d]", "", s or "")
    return int(nums) if nums else None


def _salary_from_amount(amount: int | None, per_year: bool) -> tuple[float | None, str]:
    """amount in ₹ → (lpa, payLabel)."""
    if not amount:
        return None, "Not disclosed"
    annual = amount if per_year else amount * 12
    lpa = round(annual / 100_000, 1)
    return lpa, f"₹{lpa:g}L/yr"


_TITLE_SENIORITY = [
    (r"\b(intern|trainee|fresher|graduate|entry[- ]?level|jr|junior)\b", "junior"),
    (r"\b(associate)\b", "mid"),
    (r"\b(senior|sr|staff|manager|specialist)\b", "senior"),
    (r"\b(lead|principal|head|director|vp|chief|cxo|founding)\b", "lead"),
]


def _seniority_from_title(title: str | None):
    """Infer seniority from a job title when the board omits it — so the experience factor + Opus brief
    aren't blind. Conservative: only clear signal words; ambiguous titles stay None."""
    t = (title or "").lower()
    hit = None
    for pat, sen in _TITLE_SENIORITY:   # last match wins → "Lead"/"Director" beats "Senior"
        if re.search(pat, t):
            hit = sen
    return hit


def _seniority_from_years(min_yrs: int | None):
    if min_yrs is None:
        return None
    return ("junior" if min_yrs <= 1 else "junior" if min_yrs <= 4 else
            "mid" if min_yrs <= 8 else "senior" if min_yrs <= 12 else "lead")


# free-text seniority (LinkedIn experienceLevel / ATS seniority) → our Seniority enum
_SENIORITY_TEXT = {
    "internship": "junior", "entry level": "junior", "entry": "junior", "junior": "junior",
    "associate": "mid", "mid": "mid", "mid-senior level": "senior", "senior": "senior",
    "director": "lead", "executive": "lead", "lead": "lead",
}


def _seniority_from_text(s: str | None):
    if not s:
        return None
    return _SENIORITY_TEXT.get(s.strip().lower())


_WORKMODE = {"remote": "remote", "hybrid": "hybrid", "on_site": "onsite", "onsite": "onsite"}


def naukri_to_scoutjob(it: dict, board: str = "Naukri") -> dict | None:
    # The naukri-job-scraper (fetchDetails=True) nests the real fields under `jobDetails`.
    jd = it.get("jobDetails") or {}
    title = (jd.get("title") or "").strip()
    org = ((jd.get("companyDetail") or {}).get("name") or "").strip()
    if not title or not org:
        return None
    locs = jd.get("locations") or []
    city = (locs[0].get("label") if locs and isinstance(locs[0], dict) else None) or None
    # keySkills is {other:[{label}], preferred:[{label}], ...} — flatten the labels.
    skills: list[str] = []
    ks = jd.get("keySkills") or {}
    if isinstance(ks, dict):
        for bucket in ks.values():
            for s in bucket or []:
                lbl = (s.get("label") if isinstance(s, dict) else str(s)).strip()
                if lbl and lbl not in skills:
                    skills.append(lbl)
    # salaryDetail is already a dict here (not a JSON string).
    lpa, pay = None, "Not disclosed"
    sd = jd.get("salaryDetail") or {}
    if isinstance(sd, dict) and not sd.get("hideSalary") and sd.get("maximumSalary"):
        lpa, pay = _salary_from_amount(int(sd["maximumSalary"]), per_year=True)
    # Offsite employer/ATS apply link first; the Naukri listing (staticUrl) is the fallback.
    redirect = (
        jd.get("applyRedirectUrl") or jd.get("companyApplyUrl")
        or it.get("applyRedirectUrl") or it.get("companyApplyUrl")
    )
    return {
        "id": f"naukri-{jd.get('jobId')}",
        "title": title,
        "organization": org,
        "logo": _logo(org, _logo_url(it)),
        "posted_date": _posted_date(it),
        "location_city": city,
        "location_country": "India",
        "matchScore": 0,                       # placeholder — set after merge
        "salary_lpa": lpa,
        "payLabel": pay,
        "seniority_level": _seniority_from_years(_digits(str(jd.get("minimumExperience", "")))),
        "required_skills": skills[:6],
        "tags": skills[:3],
        "applicants": jd.get("applyCount"),
        "note": f"Live from {board}",
        "apply_url": redirect or jd.get("staticUrl"),   # offsite employer link first, Naukri listing fallback
        "offsite_apply": bool(redirect),
        "qx": 10,
    }


# Indeed (valig actor) leaves location.city empty — only lat/long. Map to the nearest Indian metro so
# the card + the location factor have a city (the board already filtered to the searched city anyway).
_INDIA_METROS = {
    "Mumbai": (19.076, 72.877), "Delhi": (28.61, 77.21), "Bengaluru": (12.97, 77.59),
    "Hyderabad": (17.385, 78.486), "Chennai": (13.083, 80.27), "Kolkata": (22.57, 88.36),
    "Pune": (18.52, 73.856), "Ahmedabad": (23.03, 72.58), "Gurugram": (28.46, 77.03),
    "Noida": (28.535, 77.39), "Jaipur": (26.91, 75.79), "Chandigarh": (30.73, 76.78),
    "Kochi": (9.93, 76.27), "Indore": (22.72, 75.86), "Coimbatore": (11.02, 76.96),
}


def _city_from_latlon(lat, lon) -> str | None:
    try:
        lat, lon = float(lat), float(lon)
    except (TypeError, ValueError):
        return None
    best, bestd = None, 9e9
    for name, (clat, clon) in _INDIA_METROS.items():
        d = (lat - clat) ** 2 + (lon - clon) ** 2
        if d < bestd:
            bestd, best = d, name
    return best if bestd < 1.5 else None   # ~130km radius; farther → leave unknown, don't mislabel


def indeed_to_scoutjob(it: dict, board: str = "Indeed") -> dict | None:
    # valig~indeed-jobs-scraper schema (7s vs misceres's 52s): title, employer.name, description.text,
    # location{city/lat/long}, jobUrl (offsite employer site), url (Indeed listing), key, baseSalary.
    if it.get("expired"):
        return None
    title = (it.get("title") or "").strip()
    org = ((it.get("employer") or {}).get("name") or "").strip()
    if not title or not org:
        return None
    loc = it.get("location") or {}
    city = (loc.get("city") or "").strip() or _city_from_latlon(loc.get("latitude"), loc.get("longitude"))
    country = loc.get("countryName") or "India"
    job_url = it.get("jobUrl") or ""
    offsite = bool(job_url and "indeed.com" not in job_url)   # jobUrl = real offsite employer link
    apply_url = job_url if offsite else it.get("url")          # offsite first, Indeed listing fallback
    bs = it.get("baseSalary") or {}
    lpa, pay = None, "Not disclosed"
    amt = bs.get("max") or bs.get("min")
    if amt and bs.get("currencyCode") in (None, "INR"):
        mult = {"YEAR": 1, "MONTH": 12, "WEEK": 52, "DAY": 260, "HOUR": 2080}.get(bs.get("unitOfWork"), 1)
        lpa = round(amt * mult / 100000, 1)
        pay = f"₹{lpa:g} LPA"
    etypes = [str(v) for v in (it.get("jobTypes") or {}).values()]
    occ = [str(v) for v in (it.get("occupations") or {}).values()]
    mode = "remote" if any("remote" in t.lower() for t in etypes) else None
    details = _compact({
        "description": _plain((it.get("description") or {}).get("text")),
        "employment_type": ", ".join(etypes) or None,
        "role_category": occ[0] if occ else None,
    })
    return {
        "id": f"indeed-{it.get('key')}",
        "title": title,
        "organization": org,
        "logo": _logo(org),
        "posted_date": (it.get("datePublished") or it.get("dateOnIndeed") or None),
        "location_city": city,
        "location_country": country,
        "location_mode": mode,
        "matchScore": 0,
        "salary_lpa": lpa,
        "payLabel": pay,
        "required_skills": [],          # Indeed exposes no skill list; description carries the signal
        "tags": etypes[:3],
        "applicants": None,
        "note": f"Live from {board}",
        "apply_url": apply_url,
        "offsite_apply": offsite,
        "details": details,
        "qx": 10,
    }


def linkedin_to_scoutjob(it: dict, board: str = "LinkedIn") -> dict | None:
    title = (it.get("title") or "").strip()
    org = ((it.get("company") or {}).get("name") or "").strip()
    if not title or not org:
        return None
    loc = it.get("location") or {}
    parsed = loc.get("parsed") or {} if isinstance(loc, dict) else {}
    city = parsed.get("city") or None
    country = parsed.get("country") or parsed.get("countryFull") or None
    mode = _WORKMODE.get((it.get("workplaceType") or "").lower())
    industries = [s for s in (it.get("industries") or []) if isinstance(s, str)]
    sal = it.get("salary") or {}
    pay = sal.get("text") if isinstance(sal, dict) and sal.get("text") else "Not disclosed"
    co = it.get("company") or {}
    details = _compact({
        "description": _plain(it.get("descriptionText") or it.get("descriptionHtml")),
        "experience": it.get("experienceLevel"),
        "employment_type": (it.get("employmentType") or "").replace("_", " ").title() or None,
        "industry": ", ".join(industries[:3]) or None,
        "skills": industries,
        "company_about": _plain(co.get("description"), 700) if isinstance(co, dict) else "",
    })
    # Prefer the OFFSITE employer/ATS apply link over the LinkedIn listing — the
    # board URL can go stale, the employer's own apply page is the durable target.
    _apply_method = it.get("applyMethod") or {}
    offsite = (
        it.get("companyApplyUrl")
        or (_apply_method.get("companyApplyUrl") if isinstance(_apply_method, dict) else None)
        or it.get("applyRedirectUrl")
    )
    return {
        "id": f"linkedin-{it.get('id')}",
        "title": title,
        "organization": org,
        "logo": _logo(org, _logo_url(it)),
        "posted_date": _posted_date(it),
        "location_city": city,
        "location_country": country,
        "location_mode": mode,
        "matchScore": 0,
        "salary_lpa": None,
        "payLabel": pay,
        "seniority_level": _seniority_from_text(it.get("experienceLevel")),
        "required_skills": industries[:6],
        "tags": industries[:3],
        "applicants": it.get("applicants"),
        "note": f"Live from {board}",
        "apply_url": offsite or it.get("linkedinUrl"),   # offsite employer link first, board link fallback
        "offsite_apply": bool(offsite),
        "details": details,
        "qx": 10,
    }


def ats_to_scoutjob(it: dict, board: str = "Direct") -> dict | None:
    title = (it.get("title") or "").strip()
    org = (it.get("company") or "").strip()
    if not title or not org:
        return None
    org = org.replace("-", " ").replace("_", " ").title()  # ats slugs ("stripe") → "Stripe"
    parts = [p.strip() for p in (it.get("location") or "").split(",") if p.strip()]
    city = parts[0] if parts else None
    mode = _WORKMODE.get((it.get("remote_type") or "").lower()) or ("remote" if it.get("remote") else None)
    dept = [d for d in [it.get("department"), it.get("team")] if d]
    return {
        "id": f"ats-{it.get('global_id') or it.get('job_id')}",
        "title": title,
        "organization": org,
        "logo": _logo(org, _logo_url(it)),
        "posted_date": _posted_date(it),
        "location_city": city,
        "location_country": None,
        "location_mode": mode,
        "matchScore": 0,
        "salary_lpa": None,
        "payLabel": "Not disclosed",
        "seniority_level": _seniority_from_text(it.get("seniority")),
        "required_skills": dept[:6],
        "tags": dept[:3],
        "applicants": None,
        "note": f"Live from {board}",  # direct employer posting → real offsite apply link
        "apply_url": it.get("apply_url") or it.get("url"),
        "offsite_apply": bool(it.get("apply_url") or it.get("url")),   # ATS direct posting = employer's own apply page
        "qx": 10,
    }


def naukri_feed_to_scoutjob(it: dict, board: str = "Naukri") -> dict | None:
    """blackfalcondata/naukri-jobs-feed (fetchDetails=True) — flat shape with a real
    offsite `applyRedirectUrl` when the employer uses an external ATS."""
    title = (it.get("title") or "").strip()
    org = (it.get("companyName") or it.get("staticCompanyName") or "").strip()
    if not title or not org:
        return None
    loc = it.get("location") or ""
    city = (loc.split("/")[0].split(",")[0].strip() or None) if isinstance(loc, str) else None
    # keySkills = {preferred:[{label}], other:[{label}]} — flatten labels.
    skills: list[str] = []
    ks = it.get("keySkills") or {}
    if isinstance(ks, dict):
        for bucket in ks.values():
            for s in bucket or []:
                lbl = (s.get("label") if isinstance(s, dict) else str(s)).strip()
                if lbl and lbl not in skills:
                    skills.append(lbl)
    elif isinstance(ks, list):
        skills = [str(s) for s in ks if s]
    lpa, pay = None, "Not disclosed"
    if not it.get("salaryHidden") and it.get("salaryMax"):
        lpa, pay = _salary_from_amount(int(it["salaryMax"]), per_year=True)
    redirect = it.get("applyRedirectUrl")
    education = "; ".join(it.get("educationUG") or []) or it.get("degreeCombination") or None
    details = _compact({
        "description": _plain(it.get("descriptionMarkdown") or it.get("description") or it.get("shortDescription")),
        "experience": it.get("experienceText"),
        "employment_type": it.get("employmentType"),
        "education": education,
        "industry": it.get("industry"),
        "role_category": it.get("roleCategory"),
        "skills": skills,
        "company_about": _plain(it.get("companyDescription"), 700),
        "vacancies": it.get("vacancy"),
    })
    return {
        "id": f"naukri-{it.get('jobId')}",
        "title": title.title() if title.isupper() else title,
        "organization": org,
        "logo": _logo(org, _logo_url(it)),
        "posted_date": _posted_date(it),
        "location_city": city,
        "location_country": "India",
        "location_mode": _WORKMODE.get((it.get("wfhType") or "").lower()),
        "matchScore": 0,
        "salary_lpa": lpa,
        "payLabel": pay,
        "seniority_level": _seniority_from_years(it.get("minimumExperience")),
        "required_skills": skills[:6],
        "tags": skills[:3],
        "applicants": it.get("applyCount"),
        "note": f"Live from {board}",
        "apply_url": redirect or it.get("staticUrl") or it.get("portalUrl"),
        "offsite_apply": bool(redirect),   # True = real 3rd-party employer/ATS link (auto-applyable)
        "details": details,
        "qx": 10,
    }


def foundit_to_scoutjob(it: dict, board: str = "Foundit") -> dict | None:
    title = (it.get("title") or "").strip()
    org = (it.get("company") or "").strip()
    if not title or not org:
        return None
    loc = it.get("location") or ""
    city = (loc.split("/")[0].split(",")[0].strip() or None) if isinstance(loc, str) else None
    skills = it.get("skills") or []
    if isinstance(skills, str):
        skills = [s.strip() for s in skills.split(",") if s.strip()]
    skills = [str(s) for s in skills if s]
    seniority = None
    m = re.search(r"\d+", str(it.get("experience") or ""))
    if m:
        seniority = _seniority_from_years(int(m.group()))
    redirect = it.get("apply_url")
    details = _compact({
        "description": _plain(it.get("description_text") or it.get("description_html")),
        "experience": it.get("experience"),
        "employment_type": it.get("employment_type"),
        "industry": it.get("industry"),
        "role_category": it.get("function"),
        "skills": skills,
    })
    return {
        "id": f"foundit-{it.get('job_id')}",
        "title": title,
        "organization": org,
        "logo": _logo(org, _logo_url(it)),
        "posted_date": _posted_date(it),
        "location_city": city,
        "location_country": "India",
        "matchScore": 0,
        "salary_lpa": None,
        "payLabel": "Not disclosed",
        "seniority_level": seniority,
        "required_skills": skills[:6],
        "tags": [it.get("function")][:1] if it.get("function") else skills[:3],
        "applicants": None,
        "note": f"Live from {board}",
        "apply_url": redirect or it.get("url"),
        "offsite_apply": bool(redirect),
        "details": details,
        "qx": 10,
    }


# LinkedIn seniorityLevel → our band (often "Not Applicable" for India → None, then title-inferred centrally).
_LI_SENIORITY = {"internship": "junior", "entry level": "junior", "associate": "mid",
                 "mid-senior level": "senior", "director": "lead", "executive": "lead"}


def linkedin_cc_to_scoutjob(it: dict, board: str = "LinkedIn") -> dict | None:
    """curious_coder~linkedin-jobs-scraper — structured INDUSTRY + jobFunction (role) + applicantsCount.
    (seniorityLevel is present but often 'Not Applicable' for India; we map it when real, else infer.)"""
    title = (it.get("title") or "").strip()
    org = (it.get("companyName") or "").strip()
    if not title or not org:
        return None
    # location is "City, State, Country" — first part is the city, last is the country.
    parts = [p.strip() for p in str(it.get("location") or "").split(",") if p.strip()]
    city = parts[0] if parts else None
    country = parts[-1] if len(parts) >= 2 else "India"
    sen = _LI_SENIORITY.get(str(it.get("seniorityLevel") or "").strip().lower())   # None if "Not Applicable"
    inds = [s.strip() for s in str(it.get("industries") or "").split(",") if s.strip()]
    fn = (it.get("jobFunction") or "").strip() or None
    etype = (it.get("employmentType") or "").strip()
    mode = "remote" if "remote" in etype.lower() else None
    ext = it.get("applyUrl")
    offsite = bool(ext and "linkedin.com" not in ext)        # applyUrl (when present) = offsite employer
    apply_url = ext if offsite else it.get("link")           # else the LinkedIn job page
    details = _compact({
        "description": _plain(it.get("descriptionText") or it.get("descriptionHtml")),
        "industry": inds[0] if inds else None,
        "role_category": fn,
        "employment_type": etype or None,
    })
    return {
        "id": f"linkedin-{it.get('id')}",
        "title": title,
        "organization": org,
        "logo": _logo(org, it.get("companyLogo")),
        "posted_date": it.get("postedAt"),
        "location_city": city,
        "location_country": country,
        "location_mode": mode,
        "matchScore": 0,
        "salary_lpa": None,
        "payLabel": "Not disclosed",
        "seniority_level": sen,
        "required_skills": [],          # LinkedIn exposes no skill tags; description + jobFunction carry it
        "industry": inds[0] if inds else None,
        "tags": [fn] if fn else [],
        "applicants": it.get("applicantsCount"),
        "note": f"Live from {board}",
        "apply_url": apply_url,
        "offsite_apply": offsite,
        "details": details,
        "qx": 10,
    }


def timesjobs_to_scoutjob(it: dict, board: str = "TimesJobs") -> dict | None:
    """shahidirfan/Timesjobs-Scraper — the richest India board: skills + salary + experience +
    description + an OFFSITE employer link (external_job_url). Clean structured fields."""
    title = (it.get("title") or "").strip()
    title = re.sub(r"^title:\s*", "", title, flags=re.I).strip()   # some rows carry a stray "Title:" prefix
    org = (it.get("company") or "").strip()
    if not title or not org:
        return None
    if org.islower():
        org = org.title()
    # location is "City" or "City, City2" — take the first concrete city.
    city = next((p.strip() for p in str(it.get("location") or "").split(",") if p.strip()), None)
    skills = it.get("skills") or []
    if isinstance(skills, str):
        skills = [s.strip() for s in skills.split(",") if s.strip()]
    skills = [str(s) for s in skills if s]
    # experience "6 - 10 Yrs" → seniority from the LOW end of the band.
    seniority = None
    m = re.search(r"\d+", str(it.get("experience") or ""))
    if m:
        seniority = _seniority_from_years(int(m.group()))
    # salary is usually the text "As per industry standard"; surface a ₹L band only if a real number is there.
    sal_raw = str(it.get("salary") or "")
    lpa, pay = (None, sal_raw or "Not disclosed")
    if re.search(r"\d", sal_raw):
        lpa, pay = _salary_from_amount(_digits(sal_raw), per_year=True)
    types = [t.strip() for t in str(it.get("job_type") or "").split(",") if t.strip()]
    mode = "remote" if any("remote" in t.lower() for t in types) else None
    ext = it.get("external_job_url")
    offsite = bool(ext and "timesjobs.com" not in (ext or ""))   # external_job_url = real employer site
    apply_url = ext if offsite else it.get("url")
    details = _compact({
        "description": _plain(it.get("description_text") or it.get("description_html")),
        "experience": it.get("experience"),
        "employment_type": ", ".join(types) or None,
        "company_about": _plain(it.get("company_description"), limit=400),
        "skills": skills,
    })
    return {
        "id": f"timesjobs-{it.get('job_id')}",
        "title": title,
        "organization": org,
        "logo": _logo(org),
        "posted_date": it.get("date_posted"),
        "location_city": city,
        "location_country": "India",
        "location_mode": mode,
        "matchScore": 0,
        "salary_lpa": lpa,
        "payLabel": pay,
        "seniority_level": seniority,
        "required_skills": skills[:8],
        "tags": skills[:3],
        "applicants": None,
        "note": f"Live from {board}",
        "apply_url": apply_url,
        "offsite_apply": offsite,
        "details": details,
        "qx": 10,
    }


def workindia_to_scoutjob(it: dict, board: str = "WorkIndia") -> dict | None:
    """shahidirfan/workindia-jobs-scraper (includeDetails=True) — India blue/grey-collar non-tech.
    Verified live (Phase 0): returns real `skills` + `profile_job_description`. Apply is on-platform."""
    title = (it.get("profile_job_title") or "").strip()
    org = (it.get("branch_company_name") or "").strip()
    if not title or not org:
        return None
    city = (str(it.get("branch_location_city_name") or "").strip() or None)
    skills = it.get("skills") or []
    if isinstance(skills, str):
        skills = [s.strip() for s in skills.split(",") if s.strip()]
    skills = [str(s) for s in skills if s]
    seniority = None
    m = re.search(r"\d+", str(it.get("job_experience") or it.get("experience") or ""))
    if m:
        seniority = _seniority_from_years(int(m.group()))
    # Blue/grey-collar pay is usually monthly ₹ and noisy — show the structure text, don't risk the ₹L band.
    details = _compact({
        "description": _plain(it.get("profile_job_description") or it.get("profile_html_description")
                              or it.get("profile_short_description")),
        "experience": it.get("job_experience") or it.get("experience"),
        "employment_type": it.get("employment_type"),
        "education": it.get("degree") or it.get("profile_qualification_required"),
        "industry": it.get("profile_industry_display_name"),
        "skills": skills,
    })
    return {
        "id": f"workindia-{it.get('job_id') or it.get('id')}",
        "title": title.title() if title.isupper() else title,
        "organization": org,
        "logo": _logo(org, _logo_url(it)),
        "posted_date": it.get("job_published_on_platform") or it.get("created_at"),
        "location_city": city,
        "location_country": "India",
        "matchScore": 0,
        "salary_lpa": None,
        "payLabel": it.get("profile_salary_structure") or "Not disclosed",
        "seniority_level": seniority,
        "required_skills": skills[:6],
        "tags": skills[:3],
        "applicants": None,
        "note": f"Live from {board}",
        # source_url is a GENERIC listing page (same for every job) → would dedup-collapse the deck.
        # Build a per-job URL from job_id so each is unique AND clickable.
        "apply_url": (f"https://www.workindia.in/jobs/{it.get('job_id')}/"
                      if it.get("job_id") else it.get("source_url")),
        "offsite_apply": False,   # WorkIndia is a platform — apply on WorkIndia
        "details": details,
        "qx": 10,
    }


def wellfound_to_scoutjob(it: dict, board: str = "Wellfound") -> dict | None:
    """blackfalcondata/wellfound-scraper (enrichDetail=True) — startup roles with skills, equity,
    funding stage. Apply is on-platform (Wellfound), so apply_url = the listing (offsite_apply=False)."""
    title = (it.get("title") or "").strip()
    org = (it.get("companyName") or "").strip()
    if not title or not org:
        return None
    locs = it.get("locationNames") or []
    city = (str(locs[0]).split(",")[0].strip() or None) if isinstance(locs, list) and locs else None
    skills = [str(s) for s in (it.get("skills") or []) if s]
    # Salary → LPA only when the listing is in INR (don't pollute the India band with USD); else the
    # raw compensation string is the honest label.
    lpa, pay = None, "Not disclosed"
    cur = (it.get("salaryCurrency") or "").upper()
    if it.get("salaryMax") and cur == "INR":
        lpa, pay = _salary_from_amount(int(it["salaryMax"]), per_year=True)
    elif it.get("compensation"):
        pay = str(it["compensation"])
    elif it.get("salaryMin") or it.get("salaryMax"):
        pay = f"{cur or '$'} {it.get('salaryMin') or ''}–{it.get('salaryMax') or ''}".strip()
    details = _compact({
        "description": _plain(it.get("description")),
        "employment_type": (it.get("jobType") or "").replace("_", " ").title() or None,
        "skills": skills,
        "funding_stage": it.get("companyFundingStage"),
        "equity": it.get("equityMin") or it.get("equity"),
        "visa": it.get("visaSponsorship"),
    })
    return {
        "id": f"wellfound-{it.get('id')}",
        "title": title,
        "organization": org,
        "logo": _logo(org, _logo_url(it)),
        "posted_date": _posted_date(it),
        "location_city": city,
        "location_country": None,
        "location_mode": "remote" if it.get("remote") else None,
        "matchScore": 0,
        "salary_lpa": lpa,
        "payLabel": pay,
        "required_skills": skills[:6],
        "tags": skills[:3],
        "applicants": None,
        "note": f"Live from {board}",
        "apply_url": it.get("portalUrl"),
        "offsite_apply": False,   # apply happens on Wellfound (on-platform), not an offsite employer redirect
        "details": details,
        "qx": 10,
    }


# board key → normalizer
NORMALIZERS = {
    "naukri": naukri_feed_to_scoutjob,   # blackfalcondata (primary)
    "foundit": foundit_to_scoutjob,
    "linkedin": linkedin_cc_to_scoutjob,   # curious_coder (structured industry+jobFunction+applicants)
    "linkedin_v1": linkedin_to_scoutjob,   # harvestapi (fallback)
    "naukri_v1": naukri_to_scoutjob,     # muhammetakkurtt (fallback)
    "indeed": indeed_to_scoutjob,
    "ats": ats_to_scoutjob,
    "wellfound": wellfound_to_scoutjob,    # blackfalcondata (startup roles)
    "workindia": workindia_to_scoutjob,    # shahidirfan (India blue/grey-collar non-tech)
    "timesjobs": timesjobs_to_scoutjob,    # shahidirfan (India — full signal: skills+salary+experience)
}