afiqzudinhadi/linkedIn_auto_jobs_applier_with_AI
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Revert "fixed some issues"

Browse source 
This reverts commit 6540bbbb40.
This commit is contained in:
queukat 2024-09-09 23:39:12 +03:00
parent 6540bbbb40
commit b554357be9
9 changed files with 77 additions and 127 deletions
Download patch file Download diff file Expand all files Collapse all files

7
data_folder/config.yaml
View file

@ -1,6 +1,6 @@
remote: [true/false] remote: [true/false]
experience_level: experienceLevel:
internship: [true/false] internship: [true/false]
entry: [true/false] entry: [true/false]
associate: [true/false] associate: [true/false]
@ -31,7 +31,7 @@ locations:
- Country1 - Country1
- Country2 - Country2
apply_once_at_company: [ true/false] applyOnceAtCompany: [true/false]
distance: 100 distance: 100
@ -39,8 +39,7 @@ company_blacklist:
- Company1 - Company1
- Company2 - Company2
titleBlacklist:
title_blacklist:
- word1 - word1
- word2 - word2

8
data_folder_example/config.yaml
View file

@ -1,6 +1,6 @@
remote: true remote: true
experience_level: experienceLevel:
internship: true internship: true
entry: true entry: true
associate: true associate: true
@ -29,15 +29,15 @@ positions:
locations: locations:
- USA - USA
apply_once_at_company: [true/false] applyOnceAtCompany: [true/false]
distance: 100 distance: 100
company_blacklist: companyBlacklist:
- Noir - Noir
- Crossover - Crossover
title_blacklist: titleBlacklist:
llm_model_type: openai llm_model_type: openai
llm_model: 'gpt-4o' llm_model: 'gpt-4o'

61
main.py
View file

@ -7,9 +7,9 @@ import click
from selenium import webdriver from selenium import webdriver
from selenium.webdriver.chrome.service import Service as ChromeService from selenium.webdriver.chrome.service import Service as ChromeService
from webdriver_manager.chrome import ChromeDriverManager from webdriver_manager.chrome import ChromeDriverManager
from selenium.common.exceptions import WebDriverException from selenium.common.exceptions import WebDriverException, TimeoutException
from lib_resume_builder_AIHawk import Resume,StyleManager,FacadeManager,ResumeGenerator from lib_resume_builder_AIHawk import Resume,StyleManager,FacadeManager,ResumeGenerator
from src.utils import chrome_browser_options from src.utils import chromeBrowserOptions
from src.gpt import GPTAnswerer from src.gpt import GPTAnswerer
from src.linkedIn_authenticator import LinkedInAuthenticator from src.linkedIn_authenticator import LinkedInAuthenticator
from src.linkedIn_bot_facade import LinkedInBotFacade from src.linkedIn_bot_facade import LinkedInBotFacade
@ -19,11 +19,9 @@ from src.job_application_profile import JobApplicationProfile
# Suppress stderr # Suppress stderr
sys.stderr = open(os.devnull, 'w') sys.stderr = open(os.devnull, 'w')
class ConfigError(Exception): class ConfigError(Exception):
pass pass
class ConfigValidator: class ConfigValidator:
@staticmethod @staticmethod
def validate_email(email: str) -> bool: def validate_email(email: str) -> bool:
@ -39,36 +37,36 @@ class ConfigValidator:
except FileNotFoundError: except FileNotFoundError:
raise ConfigError(f"File not found: {yaml_path}") raise ConfigError(f"File not found: {yaml_path}")
def validate_config(config_yaml_path: Path) -> dict: def validate_config(config_yaml_path: Path) -> dict:
parameters = ConfigValidator.validate_yaml_file(config_yaml_path) parameters = ConfigValidator.validate_yaml_file(config_yaml_path)
required_keys = { required_keys = {
'remote': bool, 'remote': bool,
'experience_level': dict, 'experienceLevel': dict,
'jobTypes': dict, 'jobTypes': dict,
'date': dict, 'date': dict,
'positions': list, 'positions': list,
'locations': list, 'locations': list,
'distance': int, 'distance': int,
'company_blacklist': list, 'companyBlacklist': list,
'title_blacklist': list 'titleBlacklist': list
} }
for key, expected_type in required_keys.items(): for key, expected_type in required_keys.items():
if key not in parameters: if key not in parameters:
if key in ['company_blacklist', 'title_blacklist']: if key in ['companyBlacklist', 'titleBlacklist']:
parameters[key] = [] parameters[key] = []
else: else:
raise ConfigError(f"Missing or invalid key '{key}' in config file {config_yaml_path}") raise ConfigError(f"Missing or invalid key '{key}' in config file {config_yaml_path}")
elif not isinstance(parameters[key], expected_type): elif not isinstance(parameters[key], expected_type):
if key in ['company_blacklist', 'title_blacklist'] and parameters[key] is None: if key in ['companyBlacklist', 'titleBlacklist'] and parameters[key] is None:
parameters[key] = [] parameters[key] = []
else: else:
raise ConfigError( raise ConfigError(f"Invalid type for key '{key}' in config file {config_yaml_path}. Expected {expected_type}.")
f"Invalid type for key '{key}' in config file {config_yaml_path}. Expected {expected_type}.")
experience_levels = ['internship', 'entry', 'associate', 'mid-senior level', 'director', 'executive'] experience_levels = ['internship', 'entry', 'associate', 'mid-senior level', 'director', 'executive']
for level in experience_levels: for level in experience_levels:
if not isinstance(parameters['experience_level'].get(level), bool): if not isinstance(parameters['experienceLevel'].get(level), bool):
raise ConfigError(f"Experience level '{level}' must be a boolean in config file {config_yaml_path}") raise ConfigError(f"Experience level '{level}' must be a boolean in config file {config_yaml_path}")
job_types = ['full-time', 'contract', 'part-time', 'temporary', 'internship', 'other', 'volunteer'] job_types = ['full-time', 'contract', 'part-time', 'temporary', 'internship', 'other', 'volunteer']
@ -88,10 +86,9 @@ class ConfigValidator:
approved_distances = {0, 5, 10, 25, 50, 100} approved_distances = {0, 5, 10, 25, 50, 100}
if parameters['distance'] not in approved_distances: if parameters['distance'] not in approved_distances:
raise ConfigError( raise ConfigError(f"Invalid distance value in config file {config_yaml_path}. Must be one of: {approved_distances}")
f"Invalid distance value in config file {config_yaml_path}. Must be one of: {approved_distances}")
for blacklist in ['company_blacklist', 'title_blacklist']: for blacklist in ['companyBlacklist', 'titleBlacklist']:
if not isinstance(parameters.get(blacklist), list): if not isinstance(parameters.get(blacklist), list):
raise ConfigError(f"'{blacklist}' must be a list in config file {config_yaml_path}") raise ConfigError(f"'{blacklist}' must be a list in config file {config_yaml_path}")
if parameters[blacklist] is None: if parameters[blacklist] is None:
@ -99,6 +96,8 @@ class ConfigValidator:
return parameters return parameters
@staticmethod @staticmethod
def validate_secrets(secrets_yaml_path: Path) -> tuple: def validate_secrets(secrets_yaml_path: Path) -> tuple:
secrets = ConfigValidator.validate_yaml_file(secrets_yaml_path) secrets = ConfigValidator.validate_yaml_file(secrets_yaml_path)
@ -114,13 +113,10 @@ class ConfigValidator:
raise ConfigError(f"Password cannot be empty in secrets file {secrets_yaml_path}.") raise ConfigError(f"Password cannot be empty in secrets file {secrets_yaml_path}.")
return secrets['email'], str(secrets['password']), secrets['llm_api_key'] return secrets['email'], str(secrets['password']), secrets['llm_api_key']
class FileManager: class FileManager:
@staticmethod @staticmethod
def find_file(name_containing: str, with_extension: str, at_path: Path) -> Path: def find_file(name_containing: str, with_extension: str, at_path: Path) -> Path:
return next((file for file in at_path.iterdir() if return next((file for file in at_path.iterdir() if name_containing.lower() in file.name.lower() and file.suffix.lower() == with_extension.lower()), None)
name_containing.lower() in file.name.lower() and file.suffix.lower() == with_extension.lower()),
None)
@staticmethod @staticmethod
def validate_data_folder(app_data_folder: Path) -> tuple: def validate_data_folder(app_data_folder: Path) -> tuple:
@ -135,9 +131,7 @@ class FileManager:
output_folder = app_data_folder / 'output' output_folder = app_data_folder / 'output'
output_folder.mkdir(exist_ok=True) output_folder.mkdir(exist_ok=True)
return ( return (app_data_folder / 'secrets.yaml', app_data_folder / 'config.yaml', app_data_folder / 'plain_text_resume.yaml', output_folder)
app_data_folder / 'secrets.yaml', app_data_folder / 'config.yaml', app_data_folder / 'plain_text_resume.yaml',
output_folder)
@staticmethod @staticmethod
def file_paths_to_dict(resume_file: Path | None, plain_text_resume_file: Path) -> dict: def file_paths_to_dict(resume_file: Path | None, plain_text_resume_file: Path) -> dict:
@ -153,16 +147,14 @@ class FileManager:
return result return result
def init_browser() -> webdriver.Chrome: def init_browser() -> webdriver.Chrome:
try: try:
options = chrome_browser_options() options = chromeBrowserOptions()
service = ChromeService(ChromeDriverManager().install()) service = ChromeService(ChromeDriverManager().install())
return webdriver.Chrome(service=service, options=options) return webdriver.Chrome(service=service, options=options)
except Exception as e: except Exception as e:
raise RuntimeError(f"Failed to initialize browser: {str(e)}") raise RuntimeError(f"Failed to initialize browser: {str(e)}")
def create_and_run_bot(email, password, parameters, llm_api_key): def create_and_run_bot(email, password, parameters, llm_api_key):
try: try:
style_manager = StyleManager() style_manager = StyleManager()
@ -170,8 +162,7 @@ def create_and_run_bot(email, password, parameters, llm_api_key):
with open(parameters['uploads']['plainTextResume'], "r", encoding='utf-8') as file: with open(parameters['uploads']['plainTextResume'], "r", encoding='utf-8') as file:
plain_text_resume = file.read() plain_text_resume = file.read()
resume_object = Resume(plain_text_resume) resume_object = Resume(plain_text_resume)
resume_generator_manager = FacadeManager(llm_api_key, style_manager, resume_generator, resume_object, resume_generator_manager = FacadeManager(llm_api_key, style_manager, resume_generator, resume_object, Path("data_folder/output"))
Path("data_folder/output"))
os.system('cls' if os.name == 'nt' else 'clear') os.system('cls' if os.name == 'nt' else 'clear')
resume_generator_manager.choose_style() resume_generator_manager.choose_style()
os.system('cls' if os.name == 'nt' else 'clear') os.system('cls' if os.name == 'nt' else 'clear')
@ -196,8 +187,7 @@ def create_and_run_bot(email, password, parameters, llm_api_key):
@click.command() @click.command()
@click.option('--resume', type=click.Path(exists=True, file_okay=True, dir_okay=False, path_type=Path), @click.option('--resume', type=click.Path(exists=True, file_okay=True, dir_okay=False, path_type=Path), help="Path to the resume PDF file")
help="Path to the resume PDF file")
def main(resume: Path = None): def main(resume: Path = None):
try: try:
data_folder = Path("data_folder") data_folder = Path("data_folder")
@ -212,24 +202,19 @@ def main(resume: Path = None):
create_and_run_bot(email, password, parameters, llm_api_key) create_and_run_bot(email, password, parameters, llm_api_key)
except ConfigError as ce: except ConfigError as ce:
print(f"Configuration error: {str(ce)}") print(f"Configuration error: {str(ce)}")
print( print("Refer to the configuration guide for troubleshooting: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
"Refer to the configuration guide for troubleshooting: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
except FileNotFoundError as fnf: except FileNotFoundError as fnf:
print(f"File not found: {str(fnf)}") print(f"File not found: {str(fnf)}")
print("Ensure all required files are present in the data folder.") print("Ensure all required files are present in the data folder.")
print( print("Refer to the file setup guide: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
"Refer to the file setup guide: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
except RuntimeError as re: except RuntimeError as re:
print(f"Runtime error: {str(re)}") print(f"Runtime error: {str(re)}")
print( print("Refer to the configuration and troubleshooting guide: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
"Refer to the configuration and troubleshooting guide: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
except Exception as e: except Exception as e:
print(f"An unexpected error occurred: {str(e)}") print(f"An unexpected error occurred: {str(e)}")
print( print("Refer to the general troubleshooting guide: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
"Refer to the general troubleshooting guide: https://github.com/feder-cr/LinkedIn_AIHawk_automatic_job_application/blob/main/readme.md#configuration")
if __name__ == "__main__": if __name__ == "__main__":
main() main()

5
requirements.txt
View file

@ -14,8 +14,3 @@ click
git+https://github.com/feder-cr/lib_resume_builder_AIHawk.git git+https://github.com/feder-cr/lib_resume_builder_AIHawk.git
linkedin-api linkedin-api
pdfminer.six==20221105 pdfminer.six==20221105
inputimeout==1.0.4
langchain-ollama==0.1.3
langchain-anthropic==0.1.3
jsonschema==4.23.0
jsonschema-specifications==2023.12.1

16
resume_yaml_generator.py
View file

@ -7,17 +7,14 @@ import re
from jsonschema import validate, ValidationError from jsonschema import validate, ValidationError
from pdfminer.high_level import extract_text from pdfminer.high_level import extract_text
def load_yaml(file_path: str) -> Dict[str, Any]: def load_yaml(file_path: str) -> Dict[str, Any]:
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
return yaml.safe_load(file) return yaml.safe_load(file)
def load_resume_text(file_path: str) -> str: def load_resume_text(file_path: str) -> str:
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
return file.read() return file.read()
def get_api_key() -> str: def get_api_key() -> str:
secrets_path = os.path.join('data_folder', 'secrets.yaml') secrets_path = os.path.join('data_folder', 'secrets.yaml')
if not os.path.exists(secrets_path): if not os.path.exists(secrets_path):
@ -34,7 +31,6 @@ def get_api_key() -> str:
return api_key return api_key
def generate_yaml_from_resume(resume_text: str, schema: Dict[str, Any], api_key: str) -> str: def generate_yaml_from_resume(resume_text: str, schema: Dict[str, Any], api_key: str) -> str:
client = OpenAI(api_key=api_key) client = OpenAI(api_key=api_key)
@ -87,8 +83,7 @@ def generate_yaml_from_resume(resume_text: str, schema: Dict[str, Any], api_key:
response = client.chat.completions.create( response = client.chat.completions.create(
model="gpt-4o-mini", model="gpt-4o-mini",
messages=[ messages=[
{"role": "system", {"role": "system", "content": "You are a helpful assistant that generates structured YAML content from resume files, paying close attention to format requirements and schema structure."},
"content": "You are a helpful assistant that generates structured YAML content from resume files, paying close attention to format requirements and schema structure."},
{"role": "user", "content": prompt} {"role": "user", "content": prompt}
], ],
temperature=0.5, temperature=0.5,
@ -103,12 +98,10 @@ def generate_yaml_from_resume(resume_text: str, schema: Dict[str, Any], api_key:
else: else:
raise ValueError("YAML content not found in the expected format") raise ValueError("YAML content not found in the expected format")
def save_yaml(data: str, output_file: str): def save_yaml(data: str, output_file: str):
with open(output_file, 'w') as file: with open(output_file, 'w') as file:
file.write(data) file.write(data)
def validate_yaml(yaml_content: str, schema: Dict[str, Any]) -> Dict[str, Any]: def validate_yaml(yaml_content: str, schema: Dict[str, Any]) -> Dict[str, Any]:
try: try:
yaml_dict = yaml.safe_load(yaml_content) yaml_dict = yaml.safe_load(yaml_content)
@ -117,7 +110,6 @@ def validate_yaml(yaml_content: str, schema: Dict[str, Any]) -> Dict[str, Any]:
except ValidationError as e: except ValidationError as e:
return {"valid": False, "errors": str(e)} return {"valid": False, "errors": str(e)}
def generate_report(validation_result: Dict[str, Any], output_file: str): def generate_report(validation_result: Dict[str, Any], output_file: str):
report = f"Validation Report for {output_file}\n" report = f"Validation Report for {output_file}\n"
report += "=" * 40 + "\n" report += "=" * 40 + "\n"
@ -129,14 +121,11 @@ def generate_report(validation_result: Dict[str, Any], output_file: str):
print(report) print(report)
def pdf_to_text(pdf_path: str) -> str: def pdf_to_text(pdf_path: str) -> str:
return extract_text(pdf_path) return extract_text(pdf_path)
def main(): def main():
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(description="Generate a resume YAML file from a PDF or text resume using OpenAI API")
description="Generate a resume YAML file from a PDF or text resume using OpenAI API")
parser.add_argument("--input", required=True, help="Path to the input resume file (PDF or TXT)") parser.add_argument("--input", required=True, help="Path to the input resume file (PDF or TXT)")
parser.add_argument("--output", default="data_folder/plain_text_resume.yaml", help="Path to the output YAML file") parser.add_argument("--output", default="data_folder/plain_text_resume.yaml", help="Path to the output YAML file")
args = parser.parse_args() args = parser.parse_args()
@ -167,6 +156,5 @@ def main():
except Exception as e: except Exception as e:
print(f"An error occurred: {e}") print(f"An error occurred: {e}")
if __name__ == "__main__": if __name__ == "__main__":
main() main()

32
src/gpt.py
View file

@ -6,7 +6,8 @@ import time
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from datetime import datetime from datetime import datetime
from pathlib import Path from pathlib import Path
from typing import Dict, List, Union from typing import Dict, List
from typing import Union
import httpx import httpx
from Levenshtein import distance from Levenshtein import distance
@ -37,7 +38,7 @@ class OpenAIModel(AIModel):
def invoke(self, prompt: str) -> str: def invoke(self, prompt: str) -> str:
print("invoke in openai") print("invoke in openai")
response = self.model.invoke(prompt) response = self.model.invoke(prompt)
return response.content return response
class ClaudeModel(AIModel): class ClaudeModel(AIModel):
@ -48,7 +49,7 @@ class ClaudeModel(AIModel):
def invoke(self, prompt: str) -> str: def invoke(self, prompt: str) -> str:
response = self.model.invoke(prompt) response = self.model.invoke(prompt)
return response.content return response
class OllamaModel(AIModel): class OllamaModel(AIModel):
@ -58,14 +59,14 @@ class OllamaModel(AIModel):
def invoke(self, prompt: str) -> str: def invoke(self, prompt: str) -> str:
response = self.model.invoke(prompt) response = self.model.invoke(prompt)
return response.content return response
class AIAdapter: class AIAdapter:
def __init__(self, config: dict, api_key: str): def __init__(self, config: dict, api_key: str):
self.model = self._create_model(config, api_key) self.model = self._create_model(config, api_key)
def _create_model(self, config: dict, api_key: str) -> Union[OpenAIModel, OllamaModel, ClaudeModel]: def _create_model(self, config: dict, api_key: str) -> AIModel:
llm_model_type = config['llm_model_type'] llm_model_type = config['llm_model_type']
llm_model = config['llm_model'] llm_model = config['llm_model']
llm_api_url = config['llm_api_url'] llm_api_url = config['llm_api_url']
@ -78,7 +79,7 @@ class AIAdapter:
elif llm_model_type == "ollama": elif llm_model_type == "ollama":
return OllamaModel(api_key, llm_model, llm_api_url) return OllamaModel(api_key, llm_model, llm_api_url)
else: else:
raise ValueError(f"Unsupported model type: {llm_model_type}") raise ValueError(f"Unsupported model type: {model_type}")
def invoke(self, prompt: str) -> str: def invoke(self, prompt: str) -> str:
return self.model.invoke(prompt) return self.model.invoke(prompt)
@ -108,34 +109,25 @@ class LLMLogger:
logger.debug("Prompts are of type StringPromptValue") logger.debug("Prompts are of type StringPromptValue")
prompts = prompts.text prompts = prompts.text
logger.debug("Prompts converted to text: %s", prompts) logger.debug("Prompts converted to text: %s", prompts)
elif isinstance(prompts, dict): elif isinstance(prompts, Dict):
logger.debug("Prompts are of type dict") logger.debug("Prompts are of type Dict")
try: try:
if "messages" in prompts:
logger.debug("Prompts contain 'messages' key")
prompts = { prompts = {
f"prompt_{i + 1}": prompt["content"] f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts["messages"]) for i, prompt in enumerate(prompts.messages)
} }
logger.debug("Prompts converted to dictionary: %s", prompts) logger.debug("Prompts converted to dictionary: %s", prompts)
else:
logger.debug("Prompts dictionary does not contain 'messages' key")
except Exception as e: except Exception as e:
logger.error("Error converting prompts to dictionary: %s", str(e)) logger.error("Error converting prompts to dictionary: %s", str(e))
raise raise
else: else:
logger.debug("Prompts are of unknown type, attempting default conversion") logger.debug("Prompts are of unknown type, attempting default conversion")
try: try:
if hasattr(prompts, "messages"):
logger.debug("Prompts have 'messages' attribute")
prompts = { prompts = {
f"prompt_{i + 1}": prompt.content f"prompt_{i + 1}": prompt.content
for i, prompt in enumerate(prompts.messages) for i, prompt in enumerate(prompts.messages)
} }
logger.debug("Prompts converted to dictionary using default method: %s", prompts) logger.debug("Prompts converted to dictionary using default method: %s", prompts)
else:
logger.error("Prompts do not have 'messages' attribute, and default conversion failed")
raise ValueError("Prompts structure is not supported.")
except Exception as e: except Exception as e:
logger.error("Error converting prompts using default method: %s", str(e)) logger.error("Error converting prompts using default method: %s", str(e))
raise raise
@ -299,7 +291,7 @@ class GPTAnswerer:
def __init__(self, config, llm_api_key): def __init__(self, config, llm_api_key):
self.ai_adapter = AIAdapter(config, llm_api_key) self.ai_adapter = AIAdapter(config, llm_api_key)
self.llm_cheap = LoggerChatModel(self.ai_adapter.model) self.llm_cheap = LoggerChatModel(self.ai_adapter)
@property @property
def job_description(self): def job_description(self):

15
src/linkedIn_easy_applier.py
View file

@ -5,8 +5,7 @@ import random
import re import re
import time import time
import traceback import traceback
from pathlib import Path from typing import List, Optional, Any, Tuple
from typing import List, Optional, Any, Tuple, Set
from httpx import HTTPStatusError from httpx import HTTPStatusError
from reportlab.lib.pagesizes import A4 from reportlab.lib.pagesizes import A4
@ -24,13 +23,11 @@ from src.utils import logger
class LinkedInEasyApplier: class LinkedInEasyApplier:
def __init__(self, driver: Any, resume_dir: Optional[str], set_old_answers: Set[Tuple[str, str, str]], def __init__(self, driver: Any, resume_dir: Optional[str], set_old_answers: List[Tuple[str, str, str]],
gpt_answerer: Any, resume_generator_manager): gpt_answerer: Any, resume_generator_manager):
logger.debug("Initializing LinkedInEasyApplier") logger.debug("Initializing LinkedInEasyApplier")
if resume_dir is None or not os.path.exists(resume_dir): if resume_dir is None or not os.path.exists(resume_dir):
resume_dir = None resume_dir = None
else:
resume_dir = Path(resume_dir)
self.driver = driver self.driver = driver
self.resume_path = resume_dir self.resume_path = resume_dir
self.set_old_answers = set_old_answers self.set_old_answers = set_old_answers
@ -541,18 +538,16 @@ class LinkedInEasyApplier:
lines = split_text_by_width(cover_letter_text, "Helvetica", 12, max_width) lines = split_text_by_width(cover_letter_text, "Helvetica", 12, max_width)
line_height = 14
max_lines_per_page = int(available_height // line_height)
for line in lines: for line in lines:
text_height = text_object.getY() text_height = text_object.getY()
if text_height > bottom_margin:
text_object.textLine(line)
else:
if text_height - line_height < bottom_margin:
c.drawText(text_object) c.drawText(text_object)
c.showPage() c.showPage()
text_object = c.beginText(50, page_height - 50) text_object = c.beginText(50, page_height - 50)
text_object.setFont("Helvetica", 12) text_object.setFont("Helvetica", 12)
text_object.textLine(line) text_object.textLine(line)
c.drawText(text_object) c.drawText(text_object)

9
src/linkedIn_job_manager.py
View file

@ -47,10 +47,10 @@ class LinkedInJobManager:
def set_parameters(self, parameters): def set_parameters(self, parameters):
logger.debug("Setting parameters for LinkedInJobManager") logger.debug("Setting parameters for LinkedInJobManager")
self.company_blacklist = parameters.get('company_blacklist', []) or [] self.company_blacklist = parameters.get('company_blacklist', []) or []
self.title_blacklist = parameters.get('title_blacklist', []) or [] self.title_blacklist = parameters.get('titleBlacklist', []) or []
self.positions = parameters.get('positions', []) self.positions = parameters.get('positions', [])
self.locations = parameters.get('locations', []) self.locations = parameters.get('locations', [])
self.apply_once_at_company = parameters.get('apply_once_at_company', False) self.apply_once_at_company = parameters.get('applyOnceAtCompany', False)
self.base_search_url = self.get_base_search_url(parameters) self.base_search_url = self.get_base_search_url(parameters)
self.seen_jobs = [] self.seen_jobs = []
@ -272,7 +272,7 @@ class LinkedInJobManager:
logger.debug(f"Applicants text found: {applicants_text}") logger.debug(f"Applicants text found: {applicants_text}")
# Extract numeric digits from the text (e.g., "70 applicants" -> "70") # Extract numeric digits from the text (e.g., "70 applicants" -> "70")
applicants_count = ''.join([char for char in str(applicants_text) if char.isdigit()]) applicants_count = ''.join(filter(str.isdigit, applicants_text))
logger.debug(f"Extracted applicants count: {applicants_count}") logger.debug(f"Extracted applicants count: {applicants_count}")
if applicants_count: if applicants_count:
@ -370,7 +370,7 @@ class LinkedInJobManager:
url_parts = [] url_parts = []
if parameters['remote']: if parameters['remote']:
url_parts.append("f_CF=f_WRA") url_parts.append("f_CF=f_WRA")
experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experience_level', {}).items()) if experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experienceLevel', {}).items()) if
v] v]
if experience_levels: if experience_levels:
url_parts.append(f"f_E={','.join(experience_levels)}") url_parts.append(f"f_E={','.join(experience_levels)}")
@ -429,6 +429,7 @@ class LinkedInJobManager:
link_seen = link in self.seen_jobs link_seen = link in self.seen_jobs
is_blacklisted = title_blacklisted or company_blacklisted or link_seen is_blacklisted = title_blacklisted or company_blacklisted or link_seen
logger.debug("Job blacklisted status: %s", is_blacklisted) logger.debug("Job blacklisted status: %s", is_blacklisted)
return is_blacklisted
return title_blacklisted or company_blacklisted or link_seen return title_blacklisted or company_blacklisted or link_seen

5
src/utils.py
View file

@ -179,8 +179,3 @@ def printyellow(text):
reset = "\033[0m" reset = "\033[0m"
logger.debug("Printing text in yellow: %s", text) logger.debug("Printing text in yellow: %s", text)
print(f"{yellow}{text}{reset}") print(f"{yellow}{text}{reset}")
def stringWidth(text, font, font_size):
bbox = font.getbbox(text)
return bbox[2] - bbox[0]