reformat code

src/gpt.py

@@ -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
@@ -16,18 +16,19 @@ 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
@@ -39,6 +40,7 @@ class OpenAIModel(AIModel):
         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
@@ -49,6 +51,7 @@ class ClaudeModel(AIModel):
         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)
@@ -80,8 +84,8 @@ class AIAdapter:
     def invoke(self, prompt: str) -> str:
         return self.model.invoke(prompt)
 
-class LLMLogger:
 
+class LLMLogger:
 
     def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
 
@@ -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)
 
@@ -454,7 +456,9 @@ 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.")
 

src/linkedIn_easy_applier.py

@@ -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}")
 

src/linkedIn_job_manager.py

@@ -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
-

src/linkedin-api.py

@@ -1,13 +1,14 @@
-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] = []
 
@@ -182,13 +183,11 @@ class LinkedInEvolvedAPI(Linkedin):
 
         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}",
@@ -357,15 +356,11 @@ class LinkedInEvolvedAPI(Linkedin):
         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)
+    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
-
-        
-
-