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import http.client
# from config import *
import json
import os
from datetime import datetime
api_key = os.getenv('RAPID_API_KEY')
def scrape_jobs():
conn = http.client.HTTPSConnection("linkedin-job-search-api.p.rapidapi.com")
headers = {
'x-rapidapi-key': api_key,
'x-rapidapi-host': "linkedin-job-search-api.p.rapidapi.com"
}
conn.request("GET", "/active-jb-7d?title_filter=machine%20learning&description_type=text", headers=headers)
res = conn.getresponse()
data = res.read()
jobs_str = data.decode("utf-8")
jobs = json.loads(jobs_str)
return jobs
def extract_job_descriptions(jobs):
# Get the current date in YYYY-MM-DD format and create folder
current_date = datetime.now().strftime('%d-%m-%Y')
folder_path = os.path.join("job-postings", current_date)
print(f"Creating folder at: {folder_path}")
os.makedirs(folder_path, exist_ok=True)
for idx, job in enumerate(jobs, start=1):
if 'description_text' in job.keys():
jd = job['description_text']
print(jd)
# Save the job description to a text file
file_path = os.path.join(folder_path, f"{idx}.txt")
with open(file_path, "w", encoding="utf-8") as file:
file.write(jd)
print("Job {} saved".format(str(idx)))
else:
print("Job description not available")
jobs = scrape_jobs()
extract_job_descriptions(jobs)
# current_date = datetime.now().strftime('%d-%m-%Y')
# folder_path = os.path.join("job-postings", current_date)
# print(f"Creating folder at: {folder_path}")
# os.makedirs(folder_path, exist_ok=True)
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