Create o1_job_recommender.py
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Eric Ciarla
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# %%
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# %%
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import os
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import requests
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import json
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from dotenv import load_dotenv
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from openai import OpenAI
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# ANSI color codes
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class Colors:
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CYAN = '\033[96m'
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YELLOW = '\033[93m'
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GREEN = '\033[92m'
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RED = '\033[91m'
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MAGENTA = '\033[95m'
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BLUE = '\033[94m'
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RESET = '\033[0m'
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# Load environment variables
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load_dotenv()
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# Initialize the FirecrawlApp with your API key
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firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY")
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
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# Set the jobs page URL
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jobs_page_url = "https://openai.com/careers/search"
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# Resume
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resume_paste = """"
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Eric Ciarla
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Co-Founder @ Firecrawl
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San Francisco, California, United States
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Summary
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Building…
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Experience
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Firecrawl
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Co-Founder
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April 2024 - Present (6 months)
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San Francisco, California, United States
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Firecrawl by Mendable. Building data extraction infrastructure for AI. Used by
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Amazon, Zapier, and Nvidia (YC S22)
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Mendable
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2 years 7 months
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Co-Founder @ Mendable.ai
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March 2022 - Present (2 years 7 months)
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San Francisco, California, United States
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- Built an AI powered search platform that that served millions of queries for
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hundreds of customers (YC S22)
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- We were one of the first LLM powered apps adopted by industry leaders like
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Coinbase, Snap, DoorDash, and MongoDB
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Co-Founder @ SideGuide
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March 2022 - Present (2 years 7 months)
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San Francisco, California, United States
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- Built and scaled an online course platform with a community of over 50,000
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developers
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- Selected for Y Combinator S22 batch, 2% acceptance rate
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Fracta
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Data Engineer
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2022 - 2022 (less than a year)
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Palo Alto, California, United States
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- Demoed tool during sales calls and provided technical support during the
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entire customer lifecycle
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Page 1 of 2
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- Mined, wrangled, & visualized geospatial and water utility data for predictive
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analytics & ML workflows (Python, QGIS)
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Ford Motor Company
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Data Scientist
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2021 - 2021 (less than a year)
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Dearborn, Michigan, United States
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- Extracted, cleaned, and joined data from multiple sources using SQL,
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Hadoop, and Alteryx
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- Used Bayesian Network Structure Learning (BNLearn, R) to uncover the
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relationships between survey free response verbatim topics (derived from
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natural language processing models) and numerical customer experience
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scores
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MDRemindME
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Co-Founder
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2018 - 2020 (2 years)
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Durham, New Hampshire, United States
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- Founded and led a healthtech startup aimed at improving patient adherence
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to treatment plans through an innovative engagement and retention tool
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- Piloted the product with healthcare providers and patients, gathering critical
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insights to refine functionality and enhance user experience
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- Secured funding through National Science Foundation I-CORPS Grant and
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UNH Entrepreneurship Center Seed Grant
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Education
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Y Combinator
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S22
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University of New Hampshire
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Economics and Philosophy
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"""
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# First, scrape the jobs page using Firecrawl
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try:
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response = requests.post(
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"https://api.firecrawl.dev/v1/scrape",
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {firecrawl_api_key}"
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},
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json={
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"url": jobs_page_url,
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"formats": ["markdown"]
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}
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)
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if response.status_code == 200:
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result = response.json()
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if result.get('success'):
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html_content = result['data']['markdown']
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# Define the O1 prompt for extracting apply links
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prompt = f"""
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Extract up to 30 job application links from the given markdown content.
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Return the result as a JSON object with a single key 'apply_links' containing an array of strings (the links).
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The output should be a valid JSON object, with no additional text.
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Do not include any JSON markdown formatting or code block indicators.
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Provide only the raw JSON object as the response.
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Example of the expected format:
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{{"apply_links": ["https://example.com/job1", "https://example.com/job2", ...]}}
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Markdown content:
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{html_content[:100000]}
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"""
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print(f"{Colors.GREEN}Successfully scraped the jobs page{Colors.RESET}")
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else:
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print(f"{Colors.RED}Failed to scrape the jobs page: {result.get('message', 'Unknown error')}{Colors.RESET}")
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html_content = ""
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else:
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print(f"{Colors.RED}Error {response.status_code}: {response.text}{Colors.RESET}")
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html_content = ""
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except requests.RequestException as e:
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print(f"{Colors.RED}An error occurred while scraping: {str(e)}{Colors.RESET}")
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html_content = ""
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except json.JSONDecodeError as e:
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print(f"{Colors.RED}Error decoding JSON response: {str(e)}{Colors.RESET}")
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html_content = ""
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except Exception as e:
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print(f"{Colors.RED}An unexpected error occurred while scraping: {str(e)}{Colors.RESET}")
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html_content = ""
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# Extract apply links from the scraped HTML using O1
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apply_links = []
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if html_content:
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try:
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completion = client.chat.completions.create(
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model="gpt-4o",
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messages=[
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{
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"role": "user",
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"content": prompt
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}
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]
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)
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if completion.choices:
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print(completion.choices[0].message.content)
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result = json.loads(completion.choices[0].message.content.strip())
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apply_links = result['apply_links']
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print(f"{Colors.GREEN}Successfully extracted {len(apply_links)} apply links{Colors.RESET}")
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else:
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print(f"{Colors.RED}No apply links extracted{Colors.RESET}")
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except json.JSONDecodeError as e:
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print(f"{Colors.RED}Error decoding JSON from OpenAI response: {str(e)}{Colors.RESET}")
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except KeyError as e:
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print(f"{Colors.RED}Expected key not found in OpenAI response: {str(e)}{Colors.RESET}")
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except Exception as e:
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print(f"{Colors.RED}An unexpected error occurred during extraction: {str(e)}{Colors.RESET}")
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else:
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print(f"{Colors.RED}No HTML content to process{Colors.RESET}")
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# Initialize a list to store the extracted data
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extracted_data = []
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# %%
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print(f"{Colors.CYAN}Apply links:{Colors.RESET}")
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for link in apply_links:
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print(f"{Colors.YELLOW}{link}{Colors.RESET}")
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# %%
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# Process each apply link
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for index, link in enumerate(apply_links):
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try:
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response = requests.post(
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"https://api.firecrawl.dev/v1/scrape",
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {firecrawl_api_key}"
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},
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json={
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"url": link,
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"formats": ["extract"],
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"actions": [{
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"type": "click",
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"selector": "#job-overview"
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}],
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"extract": {
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"schema": {
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"type": "object",
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"properties": {
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"job_title": {"type": "string"},
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"sub_division_of_organization": {"type": "string"},
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"key_skills": {"type": "array", "items": {"type": "string"}},
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"compensation": {"type": "string"},
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"location": {"type": "string"},
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"apply_link": {"type": "string"}
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},
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"required": ["job_title", "sub_division_of_organization", "key_skills", "compensation", "location", "apply_link"]
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}
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}
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}
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)
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if response.status_code == 200:
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result = response.json()
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if result.get('success'):
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extracted_data.append(result['data']['extract'])
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print(f"{Colors.GREEN}Data extracted for job {index}{Colors.RESET}")
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else:
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print(f"")
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else:
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print(f"")
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except Exception as e:
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print(f"")
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# %%
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# %%
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# Print the extracted data
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print(f"{Colors.CYAN}Extracted data:{Colors.RESET}")
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for job in extracted_data:
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print(json.dumps(job, indent=2))
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print(f"{Colors.MAGENTA}{'-' * 50}{Colors.RESET}")
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# %%
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# Use o1-preview to choose which jobs should be applied to based on the resume
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prompt = f"""
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Please analyze the resume and job listings, and return a JSON list of the top 3 roles that best fit the candidate's experience and skills. Include only the job title, compensation, and apply link for each recommended role. The output should be a valid JSON array of objects in the following format, with no additional text:
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[
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{{
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"job_title": "Job Title",
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"compensation": "Compensation (if available, otherwise empty string)",
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"apply_link": "Application URL"
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}},
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...
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]
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Based on the following resume:
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{resume_paste}
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And the following job listings:
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{json.dumps(extracted_data, indent=2)}
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"""
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completion = client.chat.completions.create(
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model="o1-preview",
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": prompt
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}
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]
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}
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]
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)
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recommended_jobs = json.loads(completion.choices[0].message.content.strip())
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print(f"{Colors.CYAN}Recommended jobs:{Colors.RESET}")
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print(json.dumps(recommended_jobs, indent=2))
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