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