Spaces:
Sleeping
Sleeping
| from multiprocessing import process | |
| import pandas as pd | |
| import datetime as dt | |
| import http.client | |
| import json | |
| import urllib.parse | |
| import os | |
| from pymongo import MongoClient | |
| from concurrent.futures import ThreadPoolExecutor, as_completed | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| mongodb_conn = os.getenv('MONGODB_CONNECTION_STRING') | |
| # Global variables to keep track of searched job titles and cities | |
| searched_jobs = set() | |
| searched_cities = set() | |
| def google_job_search(job_title, city_state, start=0): | |
| ''' | |
| job_title(str): "Data Scientist", "Data Analyst" | |
| city_state(str): "Denver, CO" | |
| ''' | |
| query = f"{job_title} {city_state}" | |
| params = { | |
| "api_key": os.getenv('WEBSCRAPING_API_KEY'), | |
| "engine": "google_jobs", | |
| "q": query, | |
| "hl": "en", | |
| "google_domain": "google.com", | |
| # "start": start, | |
| # "chips": f"date_posted:{post_age}", | |
| } | |
| query_string = urllib.parse.urlencode(params, quote_via=urllib.parse.quote) | |
| conn = http.client.HTTPSConnection("serpapi.webscrapingapi.com") | |
| try: | |
| conn.request("GET", f"/v1?{query_string}") | |
| print(f"GET /v1?{query_string}") | |
| res = conn.getresponse() | |
| try: | |
| data = res.read() | |
| finally: | |
| res.close() | |
| finally: | |
| conn.close() | |
| try: | |
| json_data = json.loads(data.decode("utf-8")) | |
| jobs_results = json_data['google_jobs_results'] | |
| return jobs_results | |
| except (KeyError, json.JSONDecodeError) as e: | |
| print(f"Error occurred for search: {job_title} in {city_state}") | |
| print(f"Error message: {str(e)}") | |
| print(f"Data: {data}") | |
| return None | |
| def mongo_dump(jobs_results, collection_name): | |
| client = MongoClient(mongodb_conn) | |
| db = client.job_search_db | |
| collection = db[collection_name] | |
| for job in jobs_results: | |
| job['retrieve_date'] = dt.datetime.today().strftime('%Y-%m-%d') | |
| collection.insert_one(job) | |
| print(f"Dumped {len(jobs_results)} documents to MongoDB collection {collection_name}") | |
| def process_batch(job, city_state, start=0): | |
| global searched_jobs, searched_cities | |
| # Check if the job title and city have already been searched | |
| if (job, city_state) in searched_jobs: | |
| print(f'Skipping already searched job: {job} in {city_state}') | |
| return | |
| jobs_results = google_job_search(job, city_state, start) | |
| if jobs_results is not None: | |
| print(f'City: {city_state} Job: {job} Start: {start}') | |
| mongo_dump(jobs_results, 'sf_bay_test_jobs') | |
| # Add the job title and city to the searched sets | |
| searched_jobs.add((job, city_state)) | |
| searched_cities.add(city_state) | |
| def main(job_list, city_state_list): | |
| for job in job_list: | |
| for city_state in city_state_list: | |
| output = process_batch(job, city_state) | |
| if __name__ == "__main__": | |
| job_list = ["Data Scientist", "Machine Learning Engineer", "AI Gen Engineer", "ML Ops"] | |
| city_state_list = ["Atlanta, GA", "Austin, TX", "Boston, MA", "Chicago, IL", | |
| "Denver CO", "Dallas-Ft. Worth, TX", "Los Angeles, CA", | |
| "New York City NY", "San Francisco, CA", "Seattle, WA", | |
| "Palo Alto CA", "Mountain View CA", "San Jose, CA"] | |
| simple_city_state_list: list[str] = ["Palo Alto CA", "San Francisco CA", "Mountain View CA"] | |
| main(job_list, simple_city_state_list) |