160 lines
4.7 KiB
Python
160 lines
4.7 KiB
Python
"""
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Goal: Searches for job listings, evaluates relevance based on a CV, and applies
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@dev You need to add OPENAI_API_KEY to your environment variables.
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Also you have to install PyPDF2 to read pdf files: pip install PyPDF2
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"""
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import asyncio
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import csv
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import logging
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import os
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import sys
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from pathlib import Path
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
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from dotenv import load_dotenv
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load_dotenv()
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from langchain_openai import AzureChatOpenAI
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from pydantic import BaseModel, SecretStr
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from PyPDF2 import PdfReader
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from browser_use import ActionResult, Agent, Controller
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from browser_use.browser.browser import Browser, BrowserConfig
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from browser_use.browser.context import BrowserContext
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required_env_vars = ['AZURE_OPENAI_KEY', 'AZURE_OPENAI_ENDPOINT']
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for var in required_env_vars:
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if not os.getenv(var):
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raise ValueError(f'{var} is not set. Please add it to your environment variables.')
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logger = logging.getLogger(__name__)
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# full screen mode
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controller = Controller()
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# NOTE: This is the path to your cv file
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CV = Path.cwd() / 'cv_04_24.pdf'
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if not CV.exists():
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raise FileNotFoundError(f'You need to set the path to your cv file in the CV variable. CV file not found at {CV}')
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class Job(BaseModel):
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title: str
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link: str
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company: str
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fit_score: float
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location: str | None = None
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salary: str | None = None
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@controller.action('Save jobs to file - with a score how well it fits to my profile', param_model=Job)
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def save_jobs(job: Job):
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with open('jobs.csv', 'a', newline='') as f:
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writer = csv.writer(f)
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writer.writerow([job.title, job.company, job.link, job.salary, job.location])
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return 'Saved job to file'
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@controller.action('Read jobs from file')
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def read_jobs():
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with open('jobs.csv') as f:
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return f.read()
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@controller.action('Read my cv for context to fill forms')
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def read_cv():
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pdf = PdfReader(CV)
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text = ''
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for page in pdf.pages:
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text += page.extract_text() or ''
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logger.info(f'Read cv with {len(text)} characters')
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return ActionResult(extracted_content=text, include_in_memory=True)
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@controller.action(
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'Upload cv to element - call this function to upload if element is not found, try with different index of the same upload element',
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)
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async def upload_cv(index: int, browser: BrowserContext):
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path = str(CV.absolute())
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dom_el = await browser.get_dom_element_by_index(index)
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if dom_el is None:
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return ActionResult(error=f'No element found at index {index}')
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file_upload_dom_el = dom_el.get_file_upload_element()
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if file_upload_dom_el is None:
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logger.info(f'No file upload element found at index {index}')
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return ActionResult(error=f'No file upload element found at index {index}')
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file_upload_el = await browser.get_locate_element(file_upload_dom_el)
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if file_upload_el is None:
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logger.info(f'No file upload element found at index {index}')
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return ActionResult(error=f'No file upload element found at index {index}')
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try:
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await file_upload_el.set_input_files(path)
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msg = f'Successfully uploaded file "{path}" to index {index}'
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logger.info(msg)
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return ActionResult(extracted_content=msg)
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except Exception as e:
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logger.debug(f'Error in set_input_files: {str(e)}')
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return ActionResult(error=f'Failed to upload file to index {index}')
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browser = Browser(
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config=BrowserConfig(
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browser_binary_path='/Applications/Google Chrome.app/Contents/MacOS/Google Chrome',
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disable_security=True,
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)
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)
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async def main():
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# ground_task = (
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# 'You are a professional job finder. '
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# '1. Read my cv with read_cv'
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# '2. Read the saved jobs file '
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# '3. start applying to the first link of Amazon '
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# 'You can navigate through pages e.g. by scrolling '
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# 'Make sure to be on the english version of the page'
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# )
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ground_task = (
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'You are a professional job finder. '
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'1. Read my cv with read_cv'
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'find ml internships in and save them to a file'
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'search at company:'
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)
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tasks = [
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ground_task + '\n' + 'Google',
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# ground_task + '\n' + 'Amazon',
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# ground_task + '\n' + 'Apple',
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# ground_task + '\n' + 'Microsoft',
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# ground_task
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# + '\n'
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# + 'go to https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/Taiwan%2C-Remote/Fulfillment-Analyst---New-College-Graduate-2025_JR1988949/apply/autofillWithResume?workerSubType=0c40f6bd1d8f10adf6dae42e46d44a17&workerSubType=ab40a98049581037a3ada55b087049b7 NVIDIA',
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# ground_task + '\n' + 'Meta',
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]
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model = AzureChatOpenAI(
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model='gpt-4o',
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api_version='2024-10-21',
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azure_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT', ''),
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api_key=SecretStr(os.getenv('AZURE_OPENAI_KEY', '')),
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)
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agents = []
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for task in tasks:
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agent = Agent(task=task, llm=model, controller=controller, browser=browser)
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agents.append(agent)
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await asyncio.gather(*[agent.run() for agent in agents])
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if __name__ == '__main__':
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asyncio.run(main())
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