191 lines
9.6 KiB
Python
191 lines
9.6 KiB
Python
from scrapers.proxy_manager import get_privado_proxy_list, get_random_privado_proxy
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import os
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import time
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import pandas as pd
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from typing import List, Dict, Any
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from scrapers.ats_ingestion import determine_experience_level, determine_department
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def get_jobspy_proxies():
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proxies = get_privado_proxy_list()
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if proxies:
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return proxies
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return None
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def run_jobspy_scrapes() -> List[Dict[Any, Any]]:
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"""
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Executes JobSpy searches across nationwide US hubs and remote roles
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covering major industries: Tech, Finance, Healthcare, Retail, Trades, etc.
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Gracefully handles Cloudflare/datacenter blocks and proxy rotation.
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"""
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collected_jobs = []
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try:
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from jobspy import scrape_jobs
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except ImportError:
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print("[JobSpy] python-jobspy is not installed. Skipping JobSpy runner.")
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return []
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proxies = get_jobspy_proxies()
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if proxies:
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print(f"[JobSpy] Using configured proxy for JobSpy requests: {proxies[0].split('@')[-1]}")
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# Prioritize Indeed (reliable without Cloudflare Captcha compared to ZipRecruiter on server IPs)
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# If proxies are configured, enable zip_recruiter and glassdoor
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sites = ["indeed"]
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if proxies:
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sites.extend(["zip_recruiter", "glassdoor"])
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# Target key employment hubs across US states covering all industries
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us_regions = [
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("New York, NY", ["Finance", "Accountant", "Marketing", "Data Analyst", "Nurse", "Paralegal", "Sales"]),
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("Austin, TX", ["Software Engineer", "Project Manager", "Customer Support", "Operations", "Electrician"]),
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("San Francisco, CA", ["AI Engineer", "Product Manager", "Graphic Designer", "Recruiter"]),
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("Chicago, IL", ["Operations", "Healthcare", "Logistics", "Accountant", "Warehouse", "HR Specialist"]),
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("Atlanta, GA", ["IT Support", "Sales", "Supply Chain", "Administrative", "Medical Assistant", "Customer Service"]),
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("Seattle, WA", ["Software Developer", "Data Scientist", "Procurement", "Compliance Officer"]),
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("Boston, MA", ["Biotech", "Clinical Research", "Healthcare", "Financial Analyst", "Teacher"]),
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("Denver, CO", ["Customer Success", "Cybersecurity", "Construction Manager", "Account Executive"]),
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("Connecticut", ["Healthcare", "Nurse", "Finance", "Insurance Underwriter", "Manufacturing", "Electrician", "Administrative"])
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]
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# Nationwide Remote queries across ALL professional disciplines and seniority levels
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remote_queries = [
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# IT & Systems Administration (Entry Level & Support Focus)
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"Entry Level IT Support", "Remote Help Desk Tier 1", "Junior IT Specialist", "Technical Support Representative",
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"Junior Systems Administrator", "Remote Desktop Support Technician", "Service Desk Analyst", "IT Support Specialist",
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"Systems Administrator", "Network Support Technician", "Cloud Support Associate", "Senior Systems Administrator",
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# Healthcare & Medical (Entry through Senior)
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"Medical Biller", "Telehealth Care Coordinator", "Remote Medical Records Clerk", "Telehealth Nurse",
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"Clinical Research Coordinator", "Healthcare Recruiter", "Healthcare Data Analyst",
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# Finance, Accounting & Legal
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"Junior Staff Accountant", "Remote Bookkeeper", "Accounts Payable Specialist", "Staff Accountant",
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"Financial Analyst", "Junior Legal Assistant", "Paralegal", "Compliance Specialist", "Underwriter",
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"Senior Financial Analyst",
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# Sales, Marketing & Customer Support (Entry through Senior)
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"Remote Customer Support", "Customer Support Representative", "Customer Success Specialist",
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"Sales Development Representative", "Account Executive", "Digital Marketing Specialist", "Content Writer",
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"Senior Account Executive",
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# Human Resources & Operations
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"HR Assistant", "People Operations Coordinator", "Junior Recruiter", "HR Generalist",
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"Technical Recruiter", "Executive Assistant", "Operations Coordinator", "Senior HR Manager",
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# Art, Design & Creative
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"Junior Graphic Designer", "UI UX Designer", "Graphic Designer", "Product Designer",
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"Video Editor", "Motion Graphics Animator", "Instructional Designer",
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# Logistics, Supply Chain & Purchasing
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"Logistics Coordinator", "Supply Chain Analyst", "Procurement Specialist", "Freight Broker Associate",
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# Software & Engineering (Entry through Senior)
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"Junior Software Engineer", "Entry Level Developer", "Software Engineer", "Frontend Developer",
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"Backend Developer", "Full Stack Developer", "Data Analyst", "Data Engineer", "DevOps Engineer",
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"Senior Software Engineer"
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]
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def _execute_scrape(site_list, term, loc, is_rem):
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# 1. Try with configured proxies
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if proxies:
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try:
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return scrape_jobs(
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site_name=site_list,
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search_term=term,
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location=loc if not is_rem else None,
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results_wanted=35,
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hours_old=72,
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country_indeed="USA",
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is_remote=is_rem,
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proxies=proxies
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)
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except Exception as e:
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# Proxy or site auth error, drop proxy and fall back
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pass
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# 2. Fall back to direct Indeed scrape (no proxy, highly reliable)
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try:
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return scrape_jobs(
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site_name=["indeed"],
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search_term=term,
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location=loc if not is_rem else None,
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results_wanted=35,
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hours_old=72,
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country_indeed="USA",
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is_remote=is_rem,
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proxies=None
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)
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except Exception as e:
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print(f"[JobSpy Warning] Scrape failed for '{term}' ({loc}): {e}")
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return None
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print(f"[JobSpy] Starting Nationwide Regional Scrapes using sites: {sites}...")
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for loc, queries in us_regions:
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for query in queries:
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try:
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print(f"[JobSpy] Searching {loc}: '{query}'")
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jobs_df = _execute_scrape(sites, query, loc, False)
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if isinstance(jobs_df, pd.DataFrame) and not jobs_df.empty:
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for idx, row in jobs_df.iterrows():
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job_url = str(row.get("job_url", "") or row.get("job_url_direct", ""))
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if not job_url or job_url == "nan":
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continue
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loc_val = str(row.get("location", loc) or loc)
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if loc_val == "nan":
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loc_val = loc
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title_str = str(row.get("title", "Untitled"))
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collected_jobs.append({
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"title": title_str,
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"company": str(row.get("company", "Unknown")),
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"location": loc_val,
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"is_remote": False,
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"department": determine_department(title_str, ""),
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"experience_level": determine_experience_level(title_str),
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"description": str(row.get("description", "") or "No description provided."),
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"salary_min": float(row.get("min_amount")) if pd.notnull(row.get("min_amount")) else None,
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"salary_max": float(row.get("max_amount")) if pd.notnull(row.get("max_amount")) else None,
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"job_url": job_url,
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"source": str(row.get("site", "jobspy")),
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"date_posted": str(row.get("date_posted")) if pd.notnull(row.get("date_posted")) else None
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})
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time.sleep(1.5)
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except Exception as e:
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print(f"[JobSpy Warning] Search '{query}' in '{loc}' failed: {e}")
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print("[JobSpy] Starting US Nationwide Remote Scrapes...")
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for query in remote_queries:
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try:
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print(f"[JobSpy] Searching US Remote: '{query}'")
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jobs_df = _execute_scrape(sites, query, "USA", True)
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if isinstance(jobs_df, pd.DataFrame) and not jobs_df.empty:
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for idx, row in jobs_df.iterrows():
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job_url = str(row.get("job_url", "") or row.get("job_url_direct", ""))
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if not job_url or job_url == "nan":
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continue
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title_str = str(row.get("title", "Untitled"))
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collected_jobs.append({
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"title": title_str,
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"company": str(row.get("company", "Unknown")),
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"location": "Remote, USA",
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"is_remote": True,
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"department": determine_department(title_str, ""),
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"experience_level": determine_experience_level(title_str),
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"description": str(row.get("description", "") or "No description provided."),
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"salary_min": float(row.get("min_amount")) if pd.notnull(row.get("min_amount")) else None,
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"salary_max": float(row.get("max_amount")) if pd.notnull(row.get("max_amount")) else None,
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"job_url": job_url,
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"source": str(row.get("site", "jobspy")),
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"date_posted": str(row.get("date_posted")) if pd.notnull(row.get("date_posted")) else None
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})
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time.sleep(1.5)
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except Exception as e:
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print(f"[JobSpy Warning] Remote search '{query}' failed: {e}")
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print(f"[JobSpy] Finished. Total jobs parsed: {len(collected_jobs)}")
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return collected_jobs
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