mirror of https://github.com/Bunsly/JobSpy
117 lines
5.0 KiB
Python
117 lines
5.0 KiB
Python
import csv
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import datetime
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from jobspy.google import Google
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from jobspy.linkedin import LinkedIn
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from jobspy.indeed import Indeed
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from jobspy.ziprecruiter import ZipRecruiter
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from jobspy.model import ScraperInput
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# Define job sources
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sources = {
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"google": Google,
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"linkedin": LinkedIn,
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"indeed": Indeed,
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"zip_recruiter": ZipRecruiter,
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}
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# Define search preferences
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search_terms = ["Automation Engineer", "CRM Manager", "Implementation Specialist"]
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results_wanted = 200 # Fetch more jobs
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max_days_old = 2 # Fetch jobs posted in last 48 hours
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target_state = "NY" # Only keep jobs from New York
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def scrape_jobs(search_terms, results_wanted, max_days_old, target_state):
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"""Scrape jobs from multiple sources and filter by state."""
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all_jobs = []
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today = datetime.date.today()
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print("\n🔎 DEBUG: Fetching jobs for search terms:", search_terms)
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for search_term in search_terms:
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for source_name, source_class in sources.items():
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print(f"\n🚀 Scraping {search_term} from {source_name}...")
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scraper = source_class()
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search_criteria = ScraperInput(
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site_type=[source_name],
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search_term=search_term,
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results_wanted=results_wanted,
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)
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job_response = scraper.scrape(search_criteria)
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for job in job_response.jobs:
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# Normalize location fields
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location_city = job.location.city.strip() if job.location.city else "Unknown"
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location_state = job.location.state.strip().upper() if job.location.state else "Unknown"
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location_country = str(job.location.country) if job.location.country else "Unknown"
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# Debug: Show all jobs being fetched
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print(f"📍 Fetched Job: {job.title} - {location_city}, {location_state}, {location_country}")
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# Ensure the job is recent
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if job.date_posted and (today - job.date_posted).days <= max_days_old:
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if location_state == target_state or job.is_remote:
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print(f"✅ MATCH (In NY or Remote): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
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all_jobs.append({
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"Job ID": job.id,
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"Job Title (Primary)": job.title,
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"Company Name": job.company_name if job.company_name else "Unknown",
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"Industry": job.company_industry if job.company_industry else "Not Provided",
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"Experience Level": job.job_level if job.job_level else "Not Provided",
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"Job Type": job.job_type[0].name if job.job_type else "Not Provided",
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"Is Remote": job.is_remote,
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"Currency": job.compensation.currency if job.compensation else "",
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"Salary Min": job.compensation.min_amount if job.compensation else "",
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"Salary Max": job.compensation.max_amount if job.compensation else "",
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"Date Posted": job.date_posted.strftime("%Y-%m-%d") if job.date_posted else "Not Provided",
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"Location City": location_city,
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"Location State": location_state,
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"Location Country": location_country,
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"Job URL": job.job_url,
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"Job Description": job.description[:500] if job.description else "No description available",
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"Job Source": source_name
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})
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else:
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print(f"❌ Ignored (Wrong State): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
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else:
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print(f"⏳ Ignored (Too Old): {job.title} - {location_city}, {location_state} (Posted {job.date_posted})")
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print(f"\n✅ {len(all_jobs)} jobs retrieved in NY")
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return all_jobs
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def save_jobs_to_csv(jobs, filename="jobspy_output.csv"):
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"""Save job data to a CSV file."""
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if not jobs:
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print("⚠️ No jobs found matching criteria.")
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return
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fieldnames = [
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"Job ID", "Job Title (Primary)", "Company Name", "Industry",
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"Experience Level", "Job Type", "Is Remote", "Currency",
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"Salary Min", "Salary Max", "Date Posted", "Location City",
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"Location State", "Location Country", "Job URL", "Job Description",
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"Job Source"
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]
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with open(filename, mode="w", newline="", encoding="utf-8") as file:
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writer = csv.DictWriter(file, fieldnames=fieldnames)
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writer.writeheader()
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writer.writerows(jobs)
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print(f"✅ Jobs saved to {filename} ({len(jobs)} entries)")
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# Run the scraper with multiple job searches
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job_data = scrape_jobs(
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search_terms=search_terms,
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results_wanted=results_wanted,
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max_days_old=max_days_old,
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target_state=target_state
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)
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# Save results to CSV
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save_jobs_to_csv(job_data)
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