afiqzudinhadi/linkedIn_auto_jobs_applier_with_AI
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reformat code

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This commit is contained in:
queukat 2024-09-08 17:46:00 +03:00
parent ad85b9587f
commit 5efe6c3048
4 changed files with 82 additions and 98 deletions
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26
src/gpt.py
View file

@ -3,11 +3,11 @@ import os
import re
import textwrap
import time
from datetime import datetime
from abc import ABC, abstractmethod
from typing import Dict, List, Union
from datetime import datetime
from pathlib import Path
from typing import Dict, List
from typing import Union
import httpx
from Levenshtein import distance
@ -16,39 +16,42 @@ from langchain_core.messages.ai import AIMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompt_values import StringPromptValue
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
import src.strings as strings
from src.utils import logger
load_dotenv()
class AIModel(ABC):
@abstractmethod
def invoke(self, prompt: str) -> str:
pass
class OpenAIModel(AIModel):
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
from langchain_openai import ChatOpenAI
self.model = ChatOpenAI(model_name=llm_model, openai_api_key=api_key,
temperature=0.4, base_url=llm_api_url)
def invoke(self, prompt: str) -> str:
print("invoke in openai")
response = self.model.invoke(prompt)
return response
class ClaudeModel(AIModel):
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
from langchain_anthropic import ChatAnthropic
self.model = ChatAnthropic(model=llm_model, api_key=api_key,
temperature=0.4, base_url=llm_api_url)
temperature=0.4, base_url=llm_api_url)
def invoke(self, prompt: str) -> str:
response = self.model.invoke(prompt)
return response
class OllamaModel(AIModel):
def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
from langchain_ollama import ChatOllama
@ -58,6 +61,7 @@ class OllamaModel(AIModel):
response = self.model.invoke(prompt)
return response
class AIAdapter:
def __init__(self, config: dict, api_key: str):
self.model = self._create_model(config, api_key)
@ -67,7 +71,7 @@ class AIAdapter:
llm_model = config['llm_model']
llm_api_url = config['llm_api_url']
print('Using {0} with {1} from {2}'.format(llm_model_type, llm_model, llm_api_url))
if llm_model_type == "openai":
return OpenAIModel(api_key, llm_model, llm_api_url)
elif llm_model_type == "claude":
@ -80,9 +84,9 @@ class AIAdapter:
def invoke(self, prompt: str) -> str:
return self.model.invoke(prompt)
class LLMLogger:
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
self.llm = llm
@ -189,7 +193,6 @@ class LLMLogger:
class LoggerChatModel:
def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
self.llm = llm
@ -247,7 +250,6 @@ class LoggerChatModel:
time.sleep(30)
continue
def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
logger.debug("Parsing LLM result: %s", llmresult)
@ -454,12 +456,14 @@ class GPTAnswerer:
chain = prompt | self.llm_cheap | StrOutputParser()
output = chain.invoke({"question": question})
match = re.search(r"(Personal information|Self Identification|Legal Authorization|Work Preferences|Education Details|Experience Details|Projects|Availability|Salary Expectations|Certifications|Languages|Interests|Cover letter)", output, re.IGNORECASE)
match = re.search(
r"(Personal information|Self Identification|Legal Authorization|Work Preferences|Education Details|Experience Details|Projects|Availability|Salary Expectations|Certifications|Languages|Interests|Cover letter)",
output, re.IGNORECASE)
if not match:
raise ValueError("Could not extract section name from the response.")
section_name = match.group(1).lower().replace(" ", "_")
if section_name == "cover_letter":
chain = chains.get(section_name)
output = chain.invoke({"resume": self.resume, "job_description": self.job_description})

11
src/linkedIn_easy_applier.py
View file

@ -37,7 +37,6 @@ class LinkedInEasyApplier:
logger.debug("LinkedInEasyApplier initialized successfully")
def _load_questions_from_json(self) -> List[dict]:
output_file = 'answers.json'
logger.debug("Loading questions from JSON file: %s", output_file)
@ -60,7 +59,6 @@ class LinkedInEasyApplier:
logger.error("Error loading questions data from JSON file: %s", tb_str)
raise Exception(f"Error loading questions data from JSON file: \nTraceback:\n{tb_str}")
def check_for_premium_redirect(self, job: Any, max_attempts=3):
current_url = self.driver.current_url
@ -79,7 +77,6 @@ class LinkedInEasyApplier:
raise Exception(
f"Redirected to LinkedIn Premium page and failed to return after {max_attempts} attempts. Job application aborted.")
def job_apply(self, job: Any):
logger.debug("Starting job application for job: %s", job)
@ -167,7 +164,6 @@ class LinkedInEasyApplier:
if method.get('find_elements'):
buttons = self.driver.find_elements(By.XPATH, method['xpath'])
if buttons:
for index, button in enumerate(buttons):
@ -209,7 +205,6 @@ class LinkedInEasyApplier:
logger.error("No clickable 'Easy Apply' button found after 2 attempts. Page source:\n%s", page_source)
raise Exception("No clickable 'Easy Apply' button found")
def _get_job_description(self) -> str:
logger.debug("Getting job description")
try:
@ -541,7 +536,6 @@ class LinkedInEasyApplier:
wrapped_lines.append(line)
return wrapped_lines
lines = split_text_by_width(cover_letter_text, "Helvetica", 12, max_width)
for line in lines:
@ -567,7 +561,6 @@ class LinkedInEasyApplier:
logger.error(f"Traceback: {tb_str}")
raise
file_size = os.path.getsize(file_path_pdf)
max_file_size = 2 * 1024 * 1024 # 2 MB
logger.debug(f"Cover letter file size: {file_size} bytes")
@ -670,7 +663,6 @@ class LinkedInEasyApplier:
for item in self.all_data:
logger.debug(
f"Comparing sanitized stored question: '{self._sanitize_text(item['question'])}' and type: '{item.get('type')}' with current question: '{self._sanitize_text(question_text)}' and type: '{question_type}'")
@ -697,7 +689,6 @@ class LinkedInEasyApplier:
answer = self.gpt_answerer.answer_question_textual_wide_range(question_text)
logger.debug(f"Generated textual answer: {answer}")
self._save_questions_to_json({'type': question_type, 'question': question_text, 'answer': answer})
self._enter_text(text_field, answer)
logger.debug("Entered new answer into the textbox and saved it to JSON.")
@ -730,7 +721,6 @@ class LinkedInEasyApplier:
logger.debug("Entered existing date answer")
return True
self._save_questions_to_json({'type': 'date', 'question': question_text, 'answer': answer_text})
self._enter_text(date_field, answer_text)
logger.debug("Entered new date answer")
@ -752,7 +742,6 @@ class LinkedInEasyApplier:
logger.debug(f"Dropdown options found: {options}")
question_text = question.find_element(By.TAG_NAME, 'label').text.lower()
logger.debug(f"Processing dropdown or combobox question: {question_text}")

4
src/linkedIn_job_manager.py
View file

@ -452,9 +452,9 @@ class LinkedInJobManager:
existing_data = json.load(f)
for applied_job in existing_data:
if applied_job['company'].strip().lower() == company.strip().lower():
utils.printyellow(f"Already applied at {company} (once per company policy), skipping...")
utils.printyellow(
f"Already applied at {company} (once per company policy), skipping...")
return True
except json.JSONDecodeError:
continue
return False

139
src/linkedin-api.py
View file

@ -1,58 +1,59 @@
from typing import Dict, List
from linkedin_api import Linkedin
from typing import Optional, Union, Literal
from urllib.parse import quote, urlencode
import logging
import json
from typing import Dict, List
from typing import Optional, Union, Literal
from urllib.parse import urlencode
from linkedin_api import Linkedin
# set log to all debug
logging.basicConfig(level=logging.INFO)
class LinkedInEvolvedAPI(Linkedin):
already_applied_jobs: List[str] = []
def __init__(self, username, password):
super().__init__(username, password)
def search_jobs(
self,
keywords: Optional[str] = None,
companies: Optional[List[str]] = None,
experience: Optional[
List[
Union[
Literal["1"],
Literal["2"],
Literal["3"],
Literal["4"],
Literal["5"],
Literal["6"],
self,
keywords: Optional[str] = None,
companies: Optional[List[str]] = None,
experience: Optional[
List[
Union[
Literal["1"],
Literal["2"],
Literal["3"],
Literal["4"],
Literal["5"],
Literal["6"],
]
]
]
] = None,
job_type: Optional[
List[
Union[
Literal["F"],
Literal["C"],
Literal["P"],
Literal["T"],
Literal["I"],
Literal["V"],
Literal["O"],
] = None,
job_type: Optional[
List[
Union[
Literal["F"],
Literal["C"],
Literal["P"],
Literal["T"],
Literal["I"],
Literal["V"],
Literal["O"],
]
]
]
] = None,
job_title: Optional[List[str]] = None,
industries: Optional[List[str]] = None,
location_name: Optional[str] = None,
remote: Optional[List[Union[Literal["1"], Literal["2"], Literal["3"]]]] = None,
listed_at: None | int = None,
distance: Optional[int] = None,
easy_apply: Optional[bool] = True,
limit=-1,
offset=0,
**kwargs,
] = None,
job_title: Optional[List[str]] = None,
industries: Optional[List[str]] = None,
location_name: Optional[str] = None,
remote: Optional[List[Union[Literal["1"], Literal["2"], Literal["3"]]]] = None,
listed_at: None | int = None,
distance: Optional[int] = None,
easy_apply: Optional[bool] = True,
limit=-1,
offset=0,
**kwargs,
) -> List[Dict]:
"""Perform a LinkedIn search for jobs.
@ -154,21 +155,21 @@ class LinkedInEvolvedAPI(Linkedin):
e["job_id"] = trackingUrn
if e.get("$type") == "com.linkedin.voyager.dash.jobs.JobPosting":
new_data.append(e)
if not new_data:
break
results.extend(new_data)
if (
(-1 < limit <= len(results))
or len(results) / count >= Linkedin._MAX_REPEATED_REQUESTS
(-1 < limit <= len(results))
or len(results) / count >= Linkedin._MAX_REPEATED_REQUESTS
) or len(elements) == 0:
break
self.logger.debug(f"results grew to {len(results)}")
return results
def get_fields_for_easy_apply(self,job_id: str) -> List[Dict]:
def get_fields_for_easy_apply(self, job_id: str) -> List[Dict]:
"""Get fields needed for easy apply jobs.
:param job_id: Job ID
@ -181,14 +182,12 @@ class LinkedInEvolvedAPI(Linkedin):
cookie_str = "; ".join([f"{k}={v}" for k, v in cookies.items()])
headers: Dict[str, str] = self._headers()
headers["Accept"] = "application/vnd.linkedin.normalized+json+2.1"
headers["csrf-token"] = cookies["JSESSIONID"].replace('"', "")
headers["Cookie"] = cookie_str
headers["Connection"] = "keep-alive"
default_params = {
"decorationId": "com.linkedin.voyager.dash.deco.jobs.OnsiteApplyApplication-67",
"jobPostingUrn": f"urn:li:fsd_jobPosting:{job_id}",
@ -217,26 +216,26 @@ class LinkedInEvolvedAPI(Linkedin):
except ValueError:
self.logger.error("Failed to parse JSON response")
return []
form_components = []
for item in data.get("included", []):
if 'formComponent' in item:
if 'formComponent' in item:
urn = item['urn']
try:
title = item['title']['text']
except TypeError:
title = urn
form_component_type = list(item['formComponent'].keys())[0]
form_component_details = item['formComponent'][form_component_type]
component_info = {
'title': title,
'urn': urn,
'formComponentType': form_component_type,
}
if 'textSelectableOptions' in form_component_details:
options = [
opt['optionText']['text'] for opt in form_component_details['textSelectableOptions']
@ -244,18 +243,18 @@ class LinkedInEvolvedAPI(Linkedin):
component_info['selectableOptions'] = options
elif 'selectableOptions' in form_component_details:
options = [
opt['textSelectableOption']['optionText']['text']
opt['textSelectableOption']['optionText']['text']
for opt in form_component_details['selectableOptions']
]
component_info['selectableOptions'] = options
form_components.append(component_info)
return form_components
def apply_to_job(self,job_id: str, fields: dict, followCompany: bool = True) -> bool:
def apply_to_job(self, job_id: str, fields: dict, followCompany: bool = True) -> bool:
return False
# ToDo: Implement apply to job parser first
# How need to be implemented:
# 1. Get fields for easy apply job from the previous method (get_fields_for_easy_apply)
@ -265,11 +264,11 @@ class LinkedInEvolvedAPI(Linkedin):
# {'title': 'Quanti anni di esperienza di lavoro hai con Router?', 'urn': 'urn:li:fsd_formElement:urn:li:jobs_applyformcommon_easyApplyFormElement:(4013860791,9478711764,numeric)', 'formComponentType': 'singleLineTextFormComponent', 'response': '5'}
# To fill, you can temporary use input() function to get the data from the user manually for testing purposes (for the further implementation, the question will be asked to AI implementation and automatically filled)
# Build a working payload.
# EXAMPLE OF WORKING PAYLOAD
# 4005350454 is job_id, so need to be replaced with the job_id
#{
# {
# "followCompany": true,
# "responses": [
# {
@ -349,23 +348,19 @@ class LinkedInEvolvedAPI(Linkedin):
# }
# ],
# "trackingId": ""
#}
# }
# Push the commit to the repository and create a pull request to the v3 branch.
def set_job_as_applied(self, job_id: str) -> None:
self.already_applied_jobs.append(job_id)
## EXAMPLE USAGE
if __name__ == "__main__":
api: LinkedInEvolvedAPI = LinkedInEvolvedAPI(username="", password="")
jobs = api.search_jobs(keywords="Frontend Developer", location_name="Italia", limit=100, easy_apply=True, offset=1, listed_at=None)
api: LinkedInEvolvedAPI = LinkedInEvolvedAPI(username="", password="")
jobs = api.search_jobs(keywords="Frontend Developer", location_name="Italia", limit=100, easy_apply=True, offset=1,
listed_at=None)
for job in jobs:
job_id: str = job["job_id"]
print(f"Job ID: {job_id}")
@ -379,7 +374,3 @@ if __name__ == "__main__":
for field in fields:
print(field)
break