__init__.py

from __future__ import annotations

import math
import random
import time
from datetime import datetime, date, timedelta
from typing import Optional

import regex as re
import requests

from jobspy.exception import NaukriException
from jobspy.naukri.constant import headers as naukri_headers
from jobspy.naukri.util import (
    is_job_remote,
    parse_job_type,
    parse_company_industry,
)
from jobspy.model import (
    JobPost,
    Location,
    JobResponse,
    Country,
    Compensation,
    DescriptionFormat,
    Scraper,
    ScraperInput,
    Site,
)
from jobspy.util import (
    extract_emails_from_text,
    currency_parser,
    markdown_converter,
    create_session,
    create_logger,
)

log = create_logger("Naukri")

class Naukri(Scraper):
    base_url = "https://www.naukri.com/jobapi/v3/search"
    delay = 3
    band_delay = 4
    jobs_per_page = 20  

    def __init__(
        self, proxies: list[str] | str | None = None, ca_cert: str | None = None, user_agent: str | None = None, rate_delay_min: int | float | None = None, rate_delay_max: int | float | None = None
    ):
        """
        Initializes NaukriScraper with the Naukri API URL
        """
        super().__init__(Site.NAUKRI, proxies=proxies, ca_cert=ca_cert)
        self.session = create_session(
            proxies=self.proxies,
            ca_cert=ca_cert,
            is_tls=False,
            has_retry=True,
            backoff_factor=5,
            clear_cookies=True,
            rate_delay_min=rate_delay_min,
            rate_delay_max=rate_delay_max,
        )
        self.session.headers.update(naukri_headers)
        self.scraper_input = None
        self.country = "India"  #naukri is india-focused by default
        log.info("Naukri scraper initialized")

    def scrape(self, scraper_input: ScraperInput) -> JobResponse:
        """
        Scrapes Naukri API for jobs with scraper_input criteria
        :param scraper_input:
        :return: job_response
        """
        self.scraper_input = scraper_input
        job_list: list[JobPost] = []
        seen_ids = set()
        start = scraper_input.offset or 0
        page = (start // self.jobs_per_page) + 1
        request_count = 0
        seconds_old = (
            scraper_input.hours_old * 3600 if scraper_input.hours_old else None
        )
        continue_search = (
            lambda: len(job_list) < scraper_input.results_wanted and page <= 50  # Arbitrary limit
        )

        while continue_search():
            request_count += 1
            log.info(
                f"Scraping page {request_count} / {math.ceil(scraper_input.results_wanted / self.jobs_per_page)} "
                f"for search term: {scraper_input.search_term}"
            )
            params = {
                "noOfResults": self.jobs_per_page,
                "urlType": "search_by_keyword",
                "searchType": "adv",
                "keyword": scraper_input.search_term,
                "pageNo": page,
                "k": scraper_input.search_term,
                "seoKey": f"{scraper_input.search_term.lower().replace(' ', '-')}-jobs",
                "src": "jobsearchDesk",
                "latLong": "",
                "location": scraper_input.location,
                "remote": "true" if scraper_input.is_remote else None,
            }
            if seconds_old:
                params["days"] = seconds_old // 86400  # Convert to days

            params = {k: v for k, v in params.items() if v is not None}
            try:
                log.debug(f"Sending request to {self.base_url} with params: {params}")
                response = self.session.get(self.base_url, params=params, timeout=10)
                if response.status_code not in range(200, 400):
                    err = f"Naukri API response status code {response.status_code} - {response.text}"
                    log.error(err)
                    return JobResponse(jobs=job_list)
                data = response.json()
                job_details = data.get("jobDetails", [])
                log.info(f"Received {len(job_details)} job entries from API")
                if not job_details:
                    log.warning("No job details found in API response")
                    break
            except Exception as e:
                log.error(f"Naukri API request failed: {str(e)}")
                return JobResponse(jobs=job_list)

            for job in job_details:
                job_id = job.get("jobId")
                if not job_id or job_id in seen_ids:
                    continue
                seen_ids.add(job_id)
                log.debug(f"Processing job ID: {job_id}")

                try:
                    fetch_desc = scraper_input.linkedin_fetch_description
                    job_post = self._process_job(job, job_id, fetch_desc)
                    if job_post:
                        job_list.append(job_post)
                        log.info(f"Added job: {job_post.title} (ID: {job_id})")
                    if not continue_search():
                        break
                except Exception as e:
                    log.error(f"Error processing job ID {job_id}: {str(e)}")
                    raise NaukriException(str(e))

            if continue_search():
                time.sleep(random.uniform(self.delay, self.delay + self.band_delay))
                page += 1

        job_list = job_list[:scraper_input.results_wanted]
        log.info(f"Scraping completed. Total jobs collected: {len(job_list)}")
        return JobResponse(jobs=job_list)

    def _process_job(
        self, job: dict, job_id: str, full_descr: bool
    ) -> Optional[JobPost]:
        """
        Processes a single job from API response into a JobPost object
        """
        title = job.get("title", "N/A")
        company = job.get("companyName", "N/A")
        company_url = f"https://www.naukri.com/{job.get('staticUrl', '')}" if job.get("staticUrl") else None

        location = self._get_location(job.get("placeholders", []))
        compensation = self._get_compensation(job.get("placeholders", []))
        date_posted = self._parse_date(job.get("footerPlaceholderLabel"), job.get("createdDate"))

        job_url = f"https://www.naukri.com{job.get('jdURL', f'/job/{job_id}')}"
        raw_description = job.get("jobDescription") if full_descr else None

        job_type = parse_job_type(raw_description) if raw_description else None
        company_industry = parse_company_industry(raw_description) if raw_description else None

        description = raw_description
        if description and self.scraper_input.description_format == DescriptionFormat.MARKDOWN:
            description = markdown_converter(description)

        is_remote = is_job_remote(title, description or "", location)
        company_logo = job.get("logoPathV3") or job.get("logoPath")

        # Naukri-specific fields
        skills = job.get("tagsAndSkills", "").split(",") if job.get("tagsAndSkills") else None
        experience_range = job.get("experienceText")
        ambition_box = job.get("ambitionBoxData", {})
        company_rating = float(ambition_box.get("AggregateRating")) if ambition_box.get("AggregateRating") else None
        company_reviews_count = ambition_box.get("ReviewsCount")
        vacancy_count = job.get("vacancy")
        work_from_home_type = self._infer_work_from_home_type(job.get("placeholders", []), title, description or "")

        job_post = JobPost(
            id=f"nk-{job_id}",
            title=title,
            company_name=company,
            company_url=company_url,
            location=location,
            is_remote=is_remote,
            date_posted=date_posted,
            job_url=job_url,
            compensation=compensation,
            job_type=job_type,
            company_industry=company_industry,
            description=description,
            emails=extract_emails_from_text(description or ""),
            company_logo=company_logo,
            skills=skills,
            experience_range=experience_range,
            company_rating=company_rating,
            company_reviews_count=company_reviews_count,
            vacancy_count=vacancy_count,
            work_from_home_type=work_from_home_type,
        )
        log.debug(f"Processed job: {title} at {company}")
        return job_post

    def _get_location(self, placeholders: list[dict]) -> Location:
        """
        Extracts location data from placeholders
        """
        location = Location(country=Country.INDIA)
        for placeholder in placeholders:
            if placeholder.get("type") == "location":
                location_str = placeholder.get("label", "")
                parts = location_str.split(", ")
                city = parts[0] if parts else None
                state = parts[1] if len(parts) > 1 else None
                location = Location(city=city, state=state, country=Country.INDIA)
                log.debug(f"Parsed location: {location.display_location()}")
                break
        return location

    def _get_compensation(self, placeholders: list[dict]) -> Optional[Compensation]:
        """
        Extracts compensation data from placeholders, handling Indian salary formats (Lakhs, Crores)
        """
        for placeholder in placeholders:
            if placeholder.get("type") == "salary":
                salary_text = placeholder.get("label", "").strip()
                if salary_text == "Not disclosed":
                    log.debug("Salary not disclosed")
                    return None

                # Handle Indian salary formats (e.g., "12-16 Lacs P.A.", "1-5 Cr")
                salary_match = re.match(r"(\d+(?:\.\d+)?)\s*-\s*(\d+(?:\.\d+)?)\s*(Lacs|Lakh|Cr)\s*(P\.A\.)?", salary_text, re.IGNORECASE)
                if salary_match:
                    min_salary, max_salary, unit = salary_match.groups()[:3]
                    min_salary, max_salary = float(min_salary), float(max_salary)
                    currency = "INR"

                    # Convert to base units (INR)
                    if unit.lower() in ("lacs", "lakh"):
                        min_salary *= 100000  # 1 Lakh = 100,000 INR
                        max_salary *= 100000
                    elif unit.lower() == "cr":
                        min_salary *= 10000000  # 1 Crore = 10,000,000 INR
                        max_salary *= 10000000

                    log.debug(f"Parsed salary: {min_salary} - {max_salary} INR")
                    return Compensation(
                        min_amount=int(min_salary),
                        max_amount=int(max_salary),
                        currency=currency,
                    )
                else:
                    log.debug(f"Could not parse salary: {salary_text}")
                    return None
        return None

    def _parse_date(self, label: str, created_date: int) -> Optional[date]:
        """
        Parses date from footerPlaceholderLabel or createdDate, returning a date object
        """
        today = datetime.now()
        if not label:
            if created_date:
                return datetime.fromtimestamp(created_date / 1000).date()  # Convert to date
            return None
        label = label.lower()
        if "today" in label or "just now" in label or "few hours" in label:
            log.debug("Date parsed as today")
            return today.date()
        elif "ago" in label:
            match = re.search(r"(\d+)\s*day", label)
            if match:
                days = int(match.group(1))
                parsed_date = (today - timedelta(days = days)).date()
                log.debug(f"Date parsed: {days} days ago -> {parsed_date}")
                return parsed_date
        elif created_date:
            parsed_date = datetime.fromtimestamp(created_date / 1000).date()
            log.debug(f"Date parsed from timestamp: {parsed_date}")
            return parsed_date
        log.debug("No date parsed")
        return None

    def _infer_work_from_home_type(self, placeholders: list[dict], title: str, description: str) -> Optional[str]:
        """
        Infers work-from-home type from job data (e.g., 'Hybrid', 'Remote', 'Work from office')
        """
        location_str = next((p["label"] for p in placeholders if p["type"] == "location"), "").lower()
        if "hybrid" in location_str or "hybrid" in title.lower() or "hybrid" in description.lower():
            return "Hybrid"
        elif "remote" in location_str or "remote" in title.lower() or "remote" in description.lower():
            return "Remote"
        elif "work from office" in description.lower() or not ("remote" in description.lower() or "hybrid" in description.lower()):
            return "Work from office"
        return None