from __future__ import annotations

import os
import re
import time
import html
from typing import List, Optional
from urllib.parse import urlencode

import httpx
from pydantic import BaseModel, Field, HttpUrl

from fastmcp import FastMCP


mcp = FastMCP(
    name="linkedin-jobs",
    host="0.0.0.0",
    port=7860,
)


class JobPosting(BaseModel):
    title: str = Field(..., description="Job title")
    company: Optional[str] = Field(None, description="Company name if available")
    location: Optional[str] = Field(None, description="Job location if available")
    url: HttpUrl = Field(..., description="Direct link to the LinkedIn job page")
    job_id: Optional[str] = Field(None, description="LinkedIn job ID parsed from URL, if found")
    listed_text: Optional[str] = Field(None, description="Human-readable posted time text, e.g., '3 days ago'")


def _default_headers(cookie: Optional[str]) -> dict:
    headers = {
        "User-Agent": (
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
            "AppleWebKit/537.36 (KHTML, like Gecko) "
            "Chrome/125.0.0.0 Safari/537.36"
        ),
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
        "Accept-Language": "en-US,en;q=0.9",
        "Cache-Control": "no-cache",
        "Pragma": "no-cache",
        "Connection": "keep-alive",
        "Referer": "https://www.linkedin.com/jobs/",
    }
    if cookie:
        headers["Cookie"] = cookie
    return headers


def _ensure_absolute_url(href: str) -> str:
    if href.startswith("http://") or href.startswith("https://"):
        return href
    if href.startswith("/"):
        return f"https://www.linkedin.com{href}"
    return f"https://www.linkedin.com/{href}"


def _parse_jobs_from_html(html_text: str) -> list[JobPosting]:
    try:
        from selectolax.parser import HTMLParser
    except Exception:
        raise RuntimeError(
            "selectolax is required. Ensure it is listed in requirements.txt and installed."
        )

    tree = HTMLParser(html_text)

    jobs: list[JobPosting] = []

    # Prefer list items with data-occludable-job-id when available
    cards = tree.css("li[data-occludable-job-id], .base-search-card, .job-search-card")
    for card in cards:
        job_id = card.attributes.get("data-occludable-job-id")

        # Link: any anchor pointing to /jobs/view/
        link_el = card.css_first("a[href*='/jobs/view/']") or card.css_first(
            "a.base-card__full-link, a.hidden-nested-link, a"
        )
        url = (link_el.attributes.get("href") if link_el else None) or ""
        if url:
            url = _ensure_absolute_url(url)
            if not job_id:
                job_id_match = re.search(r"/jobs/view/(\d+)", url)
                if job_id_match:
                    job_id = job_id_match.group(1)

        # Title
        title_el = (
            card.css_first("h3.base-search-card__title")
            or card.css_first(".base-search-card__title")
            or card.css_first(".job-card-list__title")
            or card.css_first(".sr-only")
            or card.css_first("a[href*='/jobs/view/']")
        )
        title = (title_el.text(strip=True) if title_el else "").strip()

        # Company
        company_el = (
            card.css_first("h4.base-search-card__subtitle")
            or card.css_first(".base-search-card__subtitle")
            or card.css_first(".job-search-card__subtitle")
            or card.css_first(".hidden-nested-link+div")
            or card.css_first(".job-card-container__company-name")
            or card.css_first(".job-card-container__primary-description")
        )
        company = (company_el.text(strip=True) if company_el else None)

        # Location
        location_el = (
            card.css_first(".job-search-card__location")
            or card.css_first(".base-search-card__metadata > .job-search-card__location")
            or card.css_first(".job-card-container__metadata-item")
        )
        location = (location_el.text(strip=True) if location_el else None)

        # Time listed
        time_el = card.css_first("time, .job-search-card__listdate, .job-search-card__listdate--new")
        listed_text = (time_el.text(strip=True) if time_el else None)

        if not url or not title:
            continue

        # Clean up HTML entities and whitespace
        title = html.unescape(re.sub(r"\s+", " ", title))
        if company:
            company = html.unescape(re.sub(r"\s+", " ", company))
        if location:
            location = html.unescape(re.sub(r"\s+", " ", location))
        if listed_text:
            listed_text = html.unescape(re.sub(r"\s+", " ", listed_text))

        try:
            jobs.append(
                JobPosting(
                    title=title,
                    company=company,
                    location=location,
                    url=url,  # type: ignore[arg-type]
                    job_id=job_id,
                    listed_text=listed_text,
                )
            )
        except Exception:
            continue

    # Fallback: grab anchors if no structured cards were detected
    if not jobs:
        anchors = tree.css("a[href*='/jobs/view/']")
        seen_ids: set[str] = set()
        for a in anchors:
            href = a.attributes.get("href") or ""
            if not href:
                continue
            url = _ensure_absolute_url(href)
            job_id_match = re.search(r"/jobs/view/(\d+)", url)
            job_id = job_id_match.group(1) if job_id_match else None
            if job_id and job_id in seen_ids:
                continue
            title = a.text(strip=True)
            if not title:
                title = "LinkedIn Job"
            try:
                jobs.append(
                    JobPosting(
                        title=title,
                        company=None,
                        location=None,
                        url=url,  # type: ignore[arg-type]
                        job_id=job_id,
                        listed_text=None,
                    )
                )
                if job_id:
                    seen_ids.add(job_id)
            except Exception:
                continue

    return jobs


# Mapping helpers to align with common notebook tutorials/filters
_DATE_POSTED_TO_TPR = {
    # keys accepted by our API → LinkedIn f_TPR values
    "past_24_hours": "r86400",
    "past_week": "r604800",
    "past_month": "r2592000",
}

_EXPERIENCE_TO_E = {
    "internship": "1",
    "entry": "2",
    "associate": "3",
    "mid-senior": "4",
    "director": "5",
    "executive": "6",
}

_JOBTYPE_TO_JT = {
    "full-time": "F",
    "part-time": "P",
    "contract": "C",
    "temporary": "T",
    "internship": "I",
    "volunteer": "V",
    "other": "O",
}

_REMOTE_TO_WRA = {
    "on-site": "1",
    "remote": "2",
    "hybrid": "3",
}


def _build_search_params(
    *,
    keywords: str,
    location: Optional[str],
    start: int,
    sort_by: str = "relevance",
    date_posted: Optional[str] = None,
    experience_levels: Optional[List[str]] = None,
    job_types: Optional[List[str]] = None,
    remote: Optional[str] = None,
    geo_id: Optional[int] = None,
) -> dict:
    params: dict = {
        "keywords": keywords,
        "start": start,
    }
    if location:
        params["location"] = location
    if geo_id is not None:
        params["geoId"] = str(geo_id)

    # Sort: relevance (R) or date (DD)
    if sort_by and sort_by.lower() in {"relevance", "date"}:
        params["sortBy"] = "R" if sort_by.lower() == "relevance" else "DD"

    # Time posted
    if date_posted:
        tpr = _DATE_POSTED_TO_TPR.get(date_posted)
        if tpr:
            params["f_TPR"] = tpr

    # Experience levels
    if experience_levels:
        codes = [code for key in experience_levels if (code := _EXPERIENCE_TO_E.get(key))]
        if codes:
            params["f_E"] = ",".join(codes)

    # Job types
    if job_types:
        codes = [code for key in job_types if (code := _JOBTYPE_TO_JT.get(key))]
        if codes:
            params["f_JT"] = ",".join(codes)

    # Workplace type (on-site / remote / hybrid)
    if remote:
        code = _REMOTE_TO_WRA.get(remote)
        if code:
            params["f_WRA"] = code

    return params


def _search_page(
    client: httpx.Client,
    *,
    params: dict,
) -> list[JobPosting]:
    base_url = "https://www.linkedin.com/jobs/search/?" + urlencode(params)
    resp = client.get(base_url, follow_redirects=True, timeout=20.0)
    resp.raise_for_status()
    jobs = _parse_jobs_from_html(resp.text)

    # If nothing parsed, try the fragment endpoint as a fallback regardless of page
    if len(jobs) == 0:
        fragment_url = (
            "https://www.linkedin.com/jobs-guest/jobs/api/seeMoreJobPostings/search?" + urlencode(params)
        )
        frag_resp = client.get(fragment_url, follow_redirects=True, timeout=20.0)
        if frag_resp.status_code == 200:
            jobs = _parse_jobs_from_html(frag_resp.text)

    return jobs


@mcp.tool(description="Search LinkedIn job listings and return structured job postings.")
def search_linkedin_jobs(
    query: str,
    location: Optional[str] = None,
    limit: int = 25,
    pages: int = 1,
    *,
    sort_by: str = "relevance",
    date_posted: Optional[str] = None,
    experience_levels: Optional[List[str]] = None,
    job_types: Optional[List[str]] = None,
    remote: Optional[str] = None,
    geo_id: Optional[int] = None,
) -> List[JobPosting]:
    """
    - query: Search keywords, e.g. "machine learning engineer"
    - location: Optional location filter, e.g. "Paris, Île-de-France, France"
    - limit: Maximum number of jobs to return (<= 200)
    - pages: Number of pages to fetch (each page is ~25 results)
    - sort_by: "relevance" or "date" (maps to LinkedIn sortBy R/DD)
    - date_posted: one of {"past_24_hours","past_week","past_month"}
    - experience_levels: list of {"internship","entry","associate","mid-senior","director","executive"}
    - job_types: list of {"full-time","part-time","contract","temporary","internship","volunteer","other"}
    - remote: one of {"on-site","remote","hybrid"}
    - geo_id: Optional numeric LinkedIn geoId for precise location targeting

    Note: LinkedIn may throttle or require authentication. You can set the environment
    variable LINKEDIN_COOKIE to a valid cookie string (e.g., including li_at) for better results.
    """
    cookie = os.environ.get("LINKEDIN_COOKIE")

    max_items = max(1, min(limit, 200))
    pages = max(1, min(pages, 8))

    headers = _default_headers(cookie)
    all_jobs: list[JobPosting] = []

    with httpx.Client(headers=headers) as client:
        start = 0
        for _page in range(pages):
            active_params = _build_search_params(
                keywords=query,
                location=location,
                start=start,
                sort_by=sort_by,
                date_posted=date_posted,
                experience_levels=experience_levels,
                job_types=job_types,
                remote=remote,
                geo_id=geo_id,
            )

            try:
                jobs = _search_page(client, params=active_params)
            except httpx.HTTPStatusError as e:
                status = e.response.status_code
                if status in (401, 403, 429):
                    break
                raise
            except Exception:
                jobs = []

            if not jobs:
                break

            all_jobs.extend(jobs)
            if len(all_jobs) >= max_items:
                break

            start += 25
            time.sleep(0.8)

    return all_jobs[:max_items]


if __name__ == "__main__":
    mcp.run(transport="http")