new func

src/gpt.py

@@ -3,89 +3,28 @@ import os
 import re
 import textwrap
 import time
-from abc import ABC, abstractmethod
-from typing import Dict, List, Union
 from datetime import datetime
-from functools import wraps
 from pathlib import Path
 from typing import Dict, List
 
 import httpx
 from Levenshtein import distance
 from dotenv import load_dotenv
-from httpx import HTTPStatusError
 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
-from Levenshtein import distance
 
 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)
-
-    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
-        self.model = ChatOllama(model=llm_model, base_url=llm_api_url)
-
-    def invoke(self, prompt: str) -> str:
-        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)
-
-    def _create_model(self, config: dict, api_key: str) -> AIModel:
-        llm_model_type = config['llm_model_type']
-        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":
-            return ClaudeModel(api_key, llm_model, llm_api_url)
-        elif llm_model_type == "ollama":
-            return OllamaModel(api_key, llm_model, llm_api_url)
-        else:
-            raise ValueError(f"Unsupported model type: {model_type}")
-
-    def invoke(self, prompt: str) -> str:
-        return self.model.invoke(prompt)
 
 class LLMLogger:
 
-    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
+    def __init__(self, llm: ChatOpenAI):
         logger.debug("Initializing LLMLogger with LLM: %s", llm)
         self.llm = llm
         logger.debug("LLMLogger successfully initialized with LLM: %s", llm)
@@ -108,11 +47,10 @@ class LLMLogger:
             prompts = prompts.text
             logger.debug("Prompts converted to text: %s", prompts)
         elif isinstance(prompts, Dict):
-            # Convert prompts to a dictionary if they are not in the expected format
             logger.debug("Prompts are of type Dict")
             try:
                 prompts = {
-                    f"prompt_{i+1}": prompt.content
+                    f"prompt_{i + 1}": prompt.content
                     for i, prompt in enumerate(prompts.messages)
                 }
                 logger.debug("Prompts converted to dictionary: %s", prompts)
@@ -123,7 +61,7 @@ class LLMLogger:
             logger.debug("Prompts are of unknown type, attempting default conversion")
             try:
                 prompts = {
-                    f"prompt_{i+1}": prompt.content
+                    f"prompt_{i + 1}": prompt.content
                     for i, prompt in enumerate(prompts.messages)
                 }
                 logger.debug("Prompts converted to dictionary using default method: %s", prompts)
@@ -137,7 +75,7 @@ class LLMLogger:
         except Exception as e:
             logger.error("Error obtaining current time: %s", str(e))
             raise
-        # Extract token usage details from the response
+
         try:
             token_usage = parsed_reply["usage_metadata"]
             output_tokens = token_usage["output_tokens"]
@@ -147,14 +85,14 @@ class LLMLogger:
         except KeyError as e:
             logger.error("KeyError in parsed_reply structure: %s", str(e))
             raise
-        # Extract model details from the response
+
         try:
             model_name = parsed_reply["response_metadata"]["model_name"]
             logger.debug("Model name: %s", model_name)
         except KeyError as e:
             logger.error("KeyError in response_metadata: %s", str(e))
             raise
-        # Calculate the total cost of the API call
+
         try:
             prompt_price_per_token = 0.00000015
             completion_price_per_token = 0.0000006
@@ -169,7 +107,7 @@ class LLMLogger:
                 "model": model_name,
                 "time": current_time,
                 "prompts": prompts,
-                "replies": parsed_reply["content"],  # Response content
+                "replies": parsed_reply["content"],
                 "total_tokens": total_tokens,
                 "input_tokens": input_tokens,
                 "output_tokens": output_tokens,
@@ -179,7 +117,7 @@ class LLMLogger:
         except KeyError as e:
             logger.error("Error creating log entry: missing key %s in parsed_reply", str(e))
             raise
-        # Write the log entry to the log file in JSON format
+
         try:
             with open(calls_log, "a", encoding="utf-8") as f:
                 json_string = json.dumps(log_entry, ensure_ascii=False, indent=4)
@@ -191,13 +129,12 @@ class LLMLogger:
 
 
 class LoggerChatModel:
-    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
+    def __init__(self, llm: ChatOpenAI):
         logger.debug("Initializing LoggerChatModel with LLM: %s", llm)
         self.llm = llm
         logger.debug("LoggerChatModel successfully initialized with LLM: %s", llm)
 
     def __call__(self, messages: List[Dict[str, str]]) -> str:
-        # Call the LLM with the provided messages and log the response.
         logger.debug("Entering __call__ method with messages: %s", messages)
         while True:
             try:
@@ -221,18 +158,25 @@ class LoggerChatModel:
 
                     if retry_after:
                         wait_time = int(retry_after)
-                        logger.warning("Rate limit exceeded. Waiting for %d seconds before retrying (extracted from 'retry-after' header)...", wait_time)
+                        logger.warning(
+                            "Rate limit exceeded. Waiting for %d seconds before retrying (extracted from 'retry-after' header)...",
+                            wait_time)
                         time.sleep(wait_time)
                     elif retry_after_ms:
                         wait_time = int(retry_after_ms) / 1000.0
-                        logger.warning("Rate limit exceeded. Waiting for %f seconds before retrying (extracted from 'retry-after-ms' header)...", wait_time)
+                        logger.warning(
+                            "Rate limit exceeded. Waiting for %f seconds before retrying (extracted from 'retry-after-ms' header)...",
+                            wait_time)
                         time.sleep(wait_time)
                     else:
-                        wait_time = 30  # Время ожидания по умолчанию
-                        logger.warning("'retry-after' header not found. Waiting for %d seconds before retrying (default)...", wait_time)
+                        wait_time = 30
+                        logger.warning(
+                            "'retry-after' header not found. Waiting for %d seconds before retrying (default)...",
+                            wait_time)
                         time.sleep(wait_time)
                 else:
-                    logger.error("HTTP error occurred with status code: %d, waiting 30 seconds before retrying", e.response.status_code)
+                    logger.error("HTTP error occurred with status code: %d, waiting 30 seconds before retrying",
+                                 e.response.status_code)
                     time.sleep(30)
 
             except Exception as e:
@@ -242,8 +186,6 @@ class LoggerChatModel:
                 continue
 
     def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
-            # Parse the LLM result into a structured format.
-
         logger.debug("Parsing LLM result: %s", llmresult)
 
         try:
@@ -280,11 +222,11 @@ class LoggerChatModel:
             raise
 
 
-
 class GPTAnswerer:
-    def __init__(self, config, llm_api_key):
-        self.ai_adapter = AIAdapter(config, llm_api_key)
-        self.llm_cheap = LoggerChatModel(self.ai_adapter)
+    def __init__(self, openai_api_key):
+        self.llm_cheap = LoggerChatModel(
+            ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4)
+        )
         logger.debug("GPTAnswerer initialized with API key")
 
     @property
@@ -309,7 +251,6 @@ class GPTAnswerer:
 
     @staticmethod
     def _preprocess_template_string(template: str) -> str:
-        # Preprocess a template string to remove unnecessary indentation.
         logger.debug("Preprocessing template string")
         return textwrap.dedent(template)
 
@@ -343,7 +284,6 @@ class GPTAnswerer:
         return prompt | self.llm_cheap | StrOutputParser()
 
     def answer_question_textual_wide_range(self, question: str) -> str:
-        # Define chains for each section of the resume
         logger.debug("Answering textual question: %s", question)
         chains = {
             "personal_information": self._create_chain(strings.personal_information_template),
@@ -452,17 +392,14 @@ class GPTAnswerer:
         chain = prompt | self.llm_cheap | StrOutputParser()
         output = chain.invoke({"question": question})
         logger.debug("Section determined from question: %s", output)
-        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(" ", "_")
+        section_name = output.lower().replace(" ", "_")
         if section_name == "cover_letter":
             chain = chains.get(section_name)
             output = chain.invoke({"resume": self.resume, "job_description": self.job_description})
             logger.debug("Cover letter generated: %s", output)
             return output
-        resume_section = getattr(self.resume, section_name, None) or getattr(self.job_application_profile, section_name, None)
+        resume_section = getattr(self.resume, section_name, None) or getattr(self.job_application_profile, section_name,
+                                                                             None)
         if resume_section is None:
             logger.error("Section '%s' not found in either resume or job_application_profile.", section_name)
             raise ValueError(f"Section '{section_name}' not found in either resume or job_application_profile.")
@@ -479,7 +416,9 @@ class GPTAnswerer:
         func_template = self._preprocess_template_string(strings.numeric_question_template)
         prompt = ChatPromptTemplate.from_template(func_template)
         chain = prompt | self.llm_cheap | StrOutputParser()
-        output_str = chain.invoke({"resume_educations": self.resume.education_details,"resume_jobs": self.resume.experience_details,"resume_projects": self.resume.projects , "question": question})
+        output_str = chain.invoke(
+            {"resume_educations": self.resume.education_details, "resume_jobs": self.resume.experience_details,
+             "resume_projects": self.resume.projects, "question": question})
         logger.debug("Raw output for numeric question: %s", output_str)
         try:
             output = self.extract_number_from_string(output_str)
@@ -511,10 +450,12 @@ class GPTAnswerer:
         return best_option
 
     def resume_or_cover(self, phrase: str) -> str:
-        # Define the prompt template
         logger.debug("Determining if phrase refers to resume or cover letter: %s", phrase)
         prompt_template = """
-        Given the following phrase, respond with only 'resume' if the phrase is about a resume, or 'cover' if it's about a cover letter. If the phrase contains only the word 'upload', consider it as 'cover'. Do not provide any additional information or explanations.
+        Given the following phrase, respond with only 'resume' if the phrase is about a resume, or 'cover' if it's about a cover letter.
+        If the phrase contains only one word 'upload', consider it as 'cover'.
+        If the phrase contains 'upload resume', consider it as 'resume'.
+        Do not provide any additional information or explanations.
         
         phrase: {phrase}
         """

src/job_application_profile.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Dict, List
+
 import yaml
 
 from src.utils import logger
@@ -13,6 +13,7 @@ class SelfIdentification:
     disability: str
     ethnicity: str
 
+
 @dataclass
 class LegalAuthorization:
     eu_work_authorization: str
@@ -24,6 +25,7 @@ class LegalAuthorization:
     legally_allowed_to_work_in_eu: str
     requires_eu_sponsorship: str
 
+
 @dataclass
 class WorkPreferences:
     remote_work: str
@@ -33,14 +35,17 @@ class WorkPreferences:
     willing_to_undergo_drug_tests: str
     willing_to_undergo_background_checks: str
 
+
 @dataclass
 class Availability:
     notice_period: str
 
+
 @dataclass
 class SalaryExpectations:
     salary_range_usd: str
 
+
 @dataclass
 class JobApplicationProfile:
     self_identification: SelfIdentification
@@ -159,6 +164,7 @@ class JobApplicationProfile:
 
     def __str__(self):
         logger.debug("Generating string representation of JobApplicationProfile")
+
         def format_dataclass(obj):
             return "\n".join(f"{field.name}: {getattr(obj, field.name)}" for field in obj.__dataclass_fields__.values())
 

src/linkedIn_authenticator.py

@@ -1,9 +1,10 @@
 import random
 import time
+
 from selenium.common.exceptions import NoSuchElementException, TimeoutException
 from selenium.webdriver.common.by import By
-from selenium.webdriver.support.ui import WebDriverWait
 from selenium.webdriver.support import expected_conditions as EC
+from selenium.webdriver.support.ui import WebDriverWait
 
 from src.utils import logger
 
@@ -107,7 +108,6 @@ class LinkedInAuthenticator:
             buttons = self.driver.find_elements(By.CLASS_NAME, 'share-box-feed-entry__trigger')
             logger.debug("Found %d 'Start a post' buttons", len(buttons))
 
-            # Выведем текст всех найденных кнопок в лог для диагностики
             for i, button in enumerate(buttons):
                 logger.debug("Button %d text: %s", i + 1, button.text.strip())
 
@@ -115,7 +115,6 @@ class LinkedInAuthenticator:
                 logger.info("Found 'Start a post' button indicating user is logged in.")
                 return True
 
-            # Альтернативная проверка авторизации по наличию изображения профиля
             profile_img_elements = self.driver.find_elements(By.XPATH, "//img[contains(@alt, 'Photo of')]")
             if profile_img_elements:
                 logger.info("Profile image found. Assuming user is logged in.")

src/linkedIn_bot_facade.py

@@ -23,6 +23,7 @@ class LinkedInBotState:
                 raise ValueError(f"{key.replace('_', ' ').capitalize()} must be set before proceeding.")
         logger.debug("State validation passed")
 
+
 class LinkedInBotFacade:
     def __init__(self, login_component, apply_component):
         logger.debug("Initializing LinkedInBotFacade")

src/linkedIn_easy_applier.py

@@ -3,26 +3,28 @@ import json
 import os
 import random
 import re
-import tempfile
 import time
 import traceback
-from datetime import date
 from typing import List, Optional, Any, Tuple
+
 from httpx import HTTPStatusError
-from openai import RateLimitError
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
 from selenium.common.exceptions import NoSuchElementException, TimeoutException
+from selenium.webdriver import ActionChains
 from selenium.webdriver.common.by import By
 from selenium.webdriver.common.keys import Keys
 from selenium.webdriver.remote.webelement import WebElement
 from selenium.webdriver.support import expected_conditions as EC
 from selenium.webdriver.support.ui import Select, WebDriverWait
-from selenium.webdriver import ActionChains
+
 import src.utils as utils
 from src.utils import logger
+
+
 class LinkedInEasyApplier:
-    def __init__(self, driver: Any, resume_dir: Optional[str], set_old_answers: List[Tuple[str, str, str]], gpt_answerer: Any, resume_generator_manager):
+    def __init__(self, driver: Any, resume_dir: Optional[str], set_old_answers: List[Tuple[str, str, str]],
+                 gpt_answerer: Any, resume_generator_manager):
         logger.debug("Initializing LinkedInEasyApplier")
         if resume_dir is None or not os.path.exists(resume_dir):
             resume_dir = None
@@ -56,11 +58,28 @@ 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):
+        """Проверяет, был ли выполнен редирект на страницу LinkedIn Premium.
+        В случае редиректа возвращает пользователя на исходную страницу вакансии."""
+        current_url = self.driver.current_url
+        attempts = 0
+
+        while "linkedin.com/premium" in current_url and attempts < max_attempts:
+            logger.warning("Redirected to LinkedIn Premium page. Attempting to return to job page.")
+            attempts += 1
+
+            self.driver.get(job.link)
+            time.sleep(2)
+            current_url = self.driver.current_url
+
+        if "linkedin.com/premium" in current_url:
+            logger.error("Failed to return to job page after %d attempts. Cannot apply for the job.", max_attempts)
+            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)
 
-        # Открываем страницу с вакансией
         try:
             self.driver.get(job.link)
             logger.debug("Navigated to job link: %s", job.link)
@@ -68,62 +87,60 @@ class LinkedInEasyApplier:
             logger.error("Failed to navigate to job link: %s, error: %s", job.link, str(e))
             raise
 
-        # Добавляем небольшую паузу для загрузки страницы
         time.sleep(random.uniform(3, 5))
+        self.check_for_premium_redirect(job)
 
         try:
-            # Поиск кнопки 'Easy Apply'
-            logger.debug("Searching for 'Easy Apply' button on job page")
-            easy_apply_button = self._find_easy_apply_button()
 
-            # Получаем описание вакансии
+            self.driver.execute_script("document.activeElement.blur();")
+            logger.debug("Focus removed from the active element")
+
+            self.check_for_premium_redirect(job)
+
+            easy_apply_button = self._find_easy_apply_button(job)
+
+            self.check_for_premium_redirect(job)
+
             logger.debug("Retrieving job description")
             job_description = self._get_job_description()
             job.set_job_description(job_description)
-            logger.debug("Job description set: %s", job_description[:100])  # Логируем только первые 100 символов
+            logger.debug("Job description set: %s", job_description[:100])
 
-            # Получаем ссылку на рекрутера (если есть)
             logger.debug("Retrieving recruiter link")
             recruiter_link = self._get_job_recruiter()
             job.set_recruiter_link(recruiter_link)
             logger.debug("Recruiter link set: %s", recruiter_link)
 
-            # Действие: нажимаем на кнопку 'Easy Apply'
             logger.debug("Attempting to click 'Easy Apply' button")
             actions = ActionChains(self.driver)
             actions.move_to_element(easy_apply_button).click().perform()
             logger.debug("'Easy Apply' button clicked successfully")
 
-            # Передача информации о работе для дальнейшей обработки
             logger.debug("Passing job information to GPT Answerer")
             self.gpt_answerer.set_job(job)
 
-            # Заполнение формы подачи заявки
             logger.debug("Filling out application form")
             self._fill_application_form(job)
             logger.debug("Job application process completed successfully for job: %s", job)
 
         except Exception as e:
-            # Захват и логирование полного traceback в случае ошибки
+
             tb_str = traceback.format_exc()
             logger.error("Failed to apply to job: %s. Error traceback: %s", job, tb_str)
 
-            # Отмена заявки в случае ошибки
             logger.debug("Discarding application due to failure")
             self._discard_application()
 
-            # Поднятие исключения с оригинальной ошибкой
             raise Exception(f"Failed to apply to job! Original exception:\nTraceback:\n{tb_str}")
 
-    def _find_easy_apply_button(self) -> WebElement:
+    def _find_easy_apply_button(self, job: Any) -> WebElement:
         logger.debug("Searching for 'Easy Apply' button")
         attempt = 0
 
-        # Список методов поиска кнопки
         search_methods = [
             {
                 'description': "find all 'Easy Apply' buttons using find_elements",
-                'find_elements': True,  # Используем find_elements для поиска всех кнопок
+                'find_elements': True,
                 'xpath': '//button[contains(@class, "jobs-apply-button") and contains(., "Easy Apply")]'
             },
             {
@@ -137,20 +154,21 @@ class LinkedInEasyApplier:
         ]
 
         while attempt < 2:
+
+            self.check_for_premium_redirect(job)
             self._scroll_page()
 
             for method in search_methods:
                 try:
                     logger.debug(f"Attempting search using {method['description']}")
 
-                    # Если метод использует find_elements
                     if method.get('find_elements'):
                         # Поиск всех кнопок "Easy Apply"
                         buttons = self.driver.find_elements(By.XPATH, method['xpath'])
                         if buttons:
                             for index, button in enumerate(buttons):
                                 try:
-                                    # Проверка видимости и кликабельности каждой кнопки
+
                                     WebDriverWait(self.driver, 10).until(EC.visibility_of(button))
                                     WebDriverWait(self.driver, 10).until(EC.element_to_be_clickable(button))
                                     logger.debug(f"Found 'Easy Apply' button {index + 1}, attempting to click")
@@ -160,7 +178,7 @@ class LinkedInEasyApplier:
                         else:
                             raise TimeoutException("No 'Easy Apply' buttons found")
                     else:
-                        # Стандартный метод с WebDriverWait для одного элемента
+
                         button = WebDriverWait(self.driver, 10).until(
                             EC.presence_of_element_located((By.XPATH, method['xpath']))
                         )
@@ -172,7 +190,10 @@ class LinkedInEasyApplier:
                 except TimeoutException:
                     logger.warning(f"Timeout during search using {method['description']}")
                 except Exception as e:
-                    logger.warning(f"Failed to click 'Easy Apply' button using {method['description']} on attempt {attempt + 1}: {e}")
+                    logger.warning(
+                        f"Failed to click 'Easy Apply' button using {method['description']} on attempt {attempt + 1}: {e}")
+
+            self.check_for_premium_redirect(job)
 
             if attempt == 0:
                 logger.debug("Refreshing page to retry finding 'Easy Apply' button")
@@ -180,7 +201,6 @@ class LinkedInEasyApplier:
                 time.sleep(random.randint(3, 5))
             attempt += 1
 
-        # Если не удалось найти кнопку, выводим HTML для отладки
         page_source = self.driver.page_source
         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")
@@ -189,7 +209,8 @@ class LinkedInEasyApplier:
         logger.debug("Getting job description")
         try:
             try:
-                see_more_button = self.driver.find_element(By.XPATH, '//button[@aria-label="Click to see more description"]')
+                see_more_button = self.driver.find_element(By.XPATH,
+                                                           '//button[@aria-label="Click to see more description"]')
                 actions = ActionChains(self.driver)
                 actions.move_to_element(see_more_button).click().perform()
                 time.sleep(2)
@@ -216,7 +237,8 @@ class LinkedInEasyApplier:
             )
             logger.debug("Hiring team section found")
 
-            recruiter_elements = hiring_team_section.find_elements(By.XPATH, './/following::a[contains(@href, "linkedin.com/in/")]')
+            recruiter_elements = hiring_team_section.find_elements(By.XPATH,
+                                                                   './/following::a[contains(@href, "linkedin.com/in/")]')
 
             if recruiter_elements:
                 recruiter_element = recruiter_elements[0]
@@ -289,18 +311,17 @@ class LinkedInEasyApplier:
     def fill_up(self, job) -> None:
         logger.debug("Filling up form sections for job: %s", job)
 
-        # Используем WebDriverWait для ожидания элемента с классом 'jobs-easy-apply-content'
         try:
             easy_apply_content = WebDriverWait(self.driver, 10).until(
                 EC.presence_of_element_located((By.CLASS_NAME, 'jobs-easy-apply-content'))
             )
 
-            # После нахождения 'jobs-easy-apply-content' ищем элементы с классом 'pb4'
             pb4_elements = easy_apply_content.find_elements(By.CLASS_NAME, 'pb4')
             for element in pb4_elements:
                 self._process_form_element(element, job)
         except Exception as e:
             logger.error(f"Failed to find form elements: {e}")
+
     def _process_form_element(self, element: WebElement, job) -> None:
         logger.debug("Processing form element")
         if self._is_upload_field(element):
@@ -308,6 +329,47 @@ class LinkedInEasyApplier:
         else:
             self._fill_additional_questions()
 
+    def _handle_dropdown_fields(self, element: WebElement) -> None:
+        logger.debug("Handling dropdown fields")
+
+        dropdown = element.find_element(By.TAG_NAME, 'select')
+        select = Select(dropdown)
+
+        options = [option.text for option in select.options]
+        logger.debug(f"Dropdown options found: {options}")
+
+        parent_element = dropdown.find_element(By.XPATH, '../..')
+
+        label_elements = parent_element.find_elements(By.TAG_NAME, 'label')
+        if label_elements:
+            question_text = label_elements[0].text.lower()
+        else:
+            question_text = "unknown"
+
+        logger.debug(f"Detected question text: {question_text}")
+
+        existing_answer = None
+        for item in self.all_data:
+            if self._sanitize_text(question_text) in item['question'] and item['type'] == 'dropdown':
+                existing_answer = item['answer']
+                break
+
+        if existing_answer:
+            logger.debug(f"Found existing answer for question '{question_text}': {existing_answer}")
+        else:
+
+            logger.debug(f"No existing answer found, querying model for: {question_text}")
+            existing_answer = self.gpt_answerer.answer_question_from_options(question_text, options)
+            logger.debug(f"Model provided answer: {existing_answer}")
+            self._save_questions_to_json({'type': 'dropdown', 'question': question_text, 'answer': existing_answer})
+
+        if existing_answer in options:
+            select.select_by_visible_text(existing_answer)
+            logger.debug(f"Selected option: {existing_answer}")
+        else:
+            logger.error(f"Answer '{existing_answer}' is not a valid option in the dropdown")
+            raise Exception(f"Invalid option selected: {existing_answer}")
+
     def _is_upload_field(self, element: WebElement) -> bool:
         is_upload = bool(element.find_elements(By.XPATH, ".//input[@type='file']"))
         logger.debug("Element is upload field: %s", is_upload)
@@ -317,7 +379,8 @@ class LinkedInEasyApplier:
         logger.debug("Handling upload fields")
 
         try:
-            show_more_button = self.driver.find_element(By.XPATH, "//button[contains(@aria-label, 'Show more resumes')]")
+            show_more_button = self.driver.find_element(By.XPATH,
+                                                        "//button[contains(@aria-label, 'Show more resumes')]")
             show_more_button.click()
             logger.debug("Clicked 'Show more resumes' button")
         except NoSuchElementException:
@@ -339,105 +402,154 @@ class LinkedInEasyApplier:
                     self._create_and_upload_resume(element, job)
             elif 'cover' in output:
                 logger.debug("Uploading cover letter")
-                self._create_and_upload_cover_letter(element)
+                self._create_and_upload_cover_letter(element, job)
 
         logger.debug("Finished handling upload fields")
 
     def _create_and_upload_resume(self, element, job):
-            logger.debug("Starting the process of creating and uploading resume.")
-            folder_path = 'generated_cv'
+        logger.debug("Starting the process of creating and uploading resume.")
+        folder_path = 'generated_cv'
 
+        try:
+            if not os.path.exists(folder_path):
+                logger.debug(f"Creating directory at path: {folder_path}")
+            os.makedirs(folder_path, exist_ok=True)
+        except Exception as e:
+            logger.error(f"Failed to create directory: {folder_path}. Error: {e}")
+            raise
+
+        while True:
             try:
-                if not os.path.exists(folder_path):
-                    logger.debug(f"Creating directory at path: {folder_path}")
-                os.makedirs(folder_path, exist_ok=True)
+                timestamp = int(time.time())
+                file_path_pdf = os.path.join(folder_path, f"CV_{timestamp}.pdf")
+                logger.debug(f"Generated file path for resume: {file_path_pdf}")
+
+                logger.debug(f"Generating resume for job: {job.title} at {job.company}")
+                resume_pdf_base64 = self.resume_generator_manager.pdf_base64(job_description_text=job.description)
+                with open(file_path_pdf, "xb") as f:
+                    f.write(base64.b64decode(resume_pdf_base64))
+                logger.debug(f"Resume successfully generated and saved to: {file_path_pdf}")
+
+                break
+            except HTTPStatusError as e:
+                if e.response.status_code == 429:
+
+                    retry_after = e.response.headers.get('retry-after')
+                    retry_after_ms = e.response.headers.get('retry-after-ms')
+
+                    if retry_after:
+                        wait_time = int(retry_after)
+                        logger.warning(f"Rate limit exceeded, waiting {wait_time} seconds before retrying...")
+                    elif retry_after_ms:
+                        wait_time = int(retry_after_ms) / 1000.0
+                        logger.warning(f"Rate limit exceeded, waiting {wait_time} milliseconds before retrying...")
+                    else:
+                        wait_time = 20
+                        logger.warning(f"Rate limit exceeded, waiting {wait_time} seconds before retrying...")
+
+                    time.sleep(wait_time)
+                else:
+                    logger.error(f"HTTP error: {e}")
+                    raise
+
             except Exception as e:
-                logger.error(f"Failed to create directory: {folder_path}. Error: {e}")
+                logger.error(f"Failed to generate resume: {e}")
+                tb_str = traceback.format_exc()
+                logger.error(f"Traceback: {tb_str}")
+                if "RateLimitError" in str(e):
+                    logger.warning("Rate limit error encountered, retrying...")
+                    time.sleep(20)
+                else:
+                    raise
+
+        file_size = os.path.getsize(file_path_pdf)
+        max_file_size = 2 * 1024 * 1024  # 2 MB
+        logger.debug(f"Resume file size: {file_size} bytes")
+        if file_size > max_file_size:
+            logger.error(f"Resume file size exceeds 2 MB: {file_size} bytes")
+            raise ValueError("Resume file size exceeds the maximum limit of 2 MB.")
+
+        allowed_extensions = {'.pdf', '.doc', '.docx'}
+        file_extension = os.path.splitext(file_path_pdf)[1].lower()
+        logger.debug(f"Resume file extension: {file_extension}")
+        if file_extension not in allowed_extensions:
+            logger.error(f"Invalid resume file format: {file_extension}")
+            raise ValueError("Resume file format is not allowed. Only PDF, DOC, and DOCX formats are supported.")
+
+        try:
+            logger.debug(f"Uploading resume from path: {file_path_pdf}")
+            element.send_keys(os.path.abspath(file_path_pdf))
+            job.pdf_path = os.path.abspath(file_path_pdf)
+            time.sleep(2)
+            logger.debug(f"Resume created and uploaded successfully: {file_path_pdf}")
+        except Exception as e:
+            tb_str = traceback.format_exc()
+            logger.error(f"Resume upload failed: {tb_str}")
+            raise Exception(f"Upload failed: \nTraceback:\n{tb_str}")
+
+    def _create_and_upload_cover_letter(self, element: WebElement, job) -> None:
+        logger.debug("Starting the process of creating and uploading cover letter.")
+
+        cover_letter_text = self.gpt_answerer.answer_question_textual_wide_range("Write a cover letter")
+
+        folder_path = 'generated_cv'
+
+        try:
+
+            if not os.path.exists(folder_path):
+                logger.debug(f"Creating directory at path: {folder_path}")
+            os.makedirs(folder_path, exist_ok=True)
+        except Exception as e:
+            logger.error(f"Failed to create directory: {folder_path}. Error: {e}")
+            raise
+
+        while True:
+            try:
+                timestamp = int(time.time())
+                file_path_pdf = os.path.join(folder_path, f"Cover_Letter_{timestamp}.pdf")
+                logger.debug(f"Generated file path for cover letter: {file_path_pdf}")
+
+                c = canvas.Canvas(file_path_pdf, pagesize=letter)
+                _, height = letter
+                text_object = c.beginText(100, height - 100)
+                text_object.setFont("Helvetica", 12)
+                text_object.textLines(cover_letter_text)
+                c.drawText(text_object)
+                c.save()
+                logger.debug(f"Cover letter successfully generated and saved to: {file_path_pdf}")
+
+                break
+            except Exception as e:
+                logger.error(f"Failed to generate cover letter: {e}")
+                tb_str = traceback.format_exc()
+                logger.error(f"Traceback: {tb_str}")
                 raise
 
-            while True:
-                try:
-                    timestamp = int(time.time())
-                    file_path_pdf = os.path.join(folder_path, f"CV_{timestamp}.pdf")
-                    logger.debug(f"Generated file path for resume: {file_path_pdf}")
+        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")
+        if file_size > max_file_size:
+            logger.error(f"Cover letter file size exceeds 2 MB: {file_size} bytes")
+            raise ValueError("Cover letter file size exceeds the maximum limit of 2 MB.")
 
-                    logger.debug(f"Generating resume for job: {job.title} at {job.company}")
-                    resume_pdf_base64 = self.resume_generator_manager.pdf_base64(job_description_text=job.description)
-                    with open(file_path_pdf, "xb") as f:
-                        f.write(base64.b64decode(resume_pdf_base64))
-                    logger.debug(f"Resume successfully generated and saved to: {file_path_pdf}")
+        allowed_extensions = {'.pdf', '.doc', '.docx'}
+        file_extension = os.path.splitext(file_path_pdf)[1].lower()
+        logger.debug(f"Cover letter file extension: {file_extension}")
+        if file_extension not in allowed_extensions:
+            logger.error(f"Invalid cover letter file format: {file_extension}")
+            raise ValueError("Cover letter file format is not allowed. Only PDF, DOC, and DOCX formats are supported.")
 
-                    break
-                except HTTPStatusError as e:
-                    if e.response.status_code == 429:
+        try:
 
-                        retry_after = e.response.headers.get('retry-after')
-                        retry_after_ms = e.response.headers.get('retry-after-ms')
-
-                        if retry_after:
-                            wait_time = int(retry_after)
-                            logger.warning(f"Rate limit exceeded, waiting {wait_time} seconds before retrying...")
-                        elif retry_after_ms:
-                            wait_time = int(retry_after_ms) / 1000.0
-                            logger.warning(f"Rate limit exceeded, waiting {wait_time} milliseconds before retrying...")
-                        else:
-                            wait_time = 20
-                            logger.warning(f"Rate limit exceeded, waiting {wait_time} seconds before retrying...")
-
-                        time.sleep(wait_time)
-                    else:
-                        logger.error(f"HTTP error: {e}")
-                        raise
-
-                except Exception as e:
-                    logger.error(f"Failed to generate resume: {e}")
-                    tb_str = traceback.format_exc()
-                    logger.error(f"Traceback: {tb_str}")
-                    if "RateLimitError" in str(e):
-                        logger.warning("Rate limit error encountered, retrying...")
-                        time.sleep(20)
-                    else:
-                        raise
-
-            file_size = os.path.getsize(file_path_pdf)
-            max_file_size = 2 * 1024 * 1024  # 2 MB
-            logger.debug(f"Resume file size: {file_size} bytes")
-            if file_size > max_file_size:
-                logger.error(f"Resume file size exceeds 2 MB: {file_size} bytes")
-                raise ValueError("Resume file size exceeds the maximum limit of 2 MB.")
-
-            allowed_extensions = {'.pdf', '.doc', '.docx'}
-            file_extension = os.path.splitext(file_path_pdf)[1].lower()
-            logger.debug(f"Resume file extension: {file_extension}")
-            if file_extension not in allowed_extensions:
-                logger.error(f"Invalid resume file format: {file_extension}")
-                raise ValueError("Resume file format is not allowed. Only PDF, DOC, and DOCX formats are supported.")
-
-            try:
-                logger.debug(f"Uploading resume from path: {file_path_pdf}")
-                element.send_keys(os.path.abspath(file_path_pdf))
-                job.pdf_path = os.path.abspath(file_path_pdf)
-                time.sleep(2)
-                logger.debug(f"Resume created and uploaded successfully: {file_path_pdf}")
-            except Exception as e:
-                tb_str = traceback.format_exc()
-                logger.error(f"Resume upload failed: {tb_str}")
-                raise Exception(f"Upload failed: \nTraceback:\n{tb_str}")
-
-    def _create_and_upload_cover_letter(self, element: WebElement) -> None:
-        logger.debug("Creating and uploading cover letter")
-        cover_letter = self.gpt_answerer.answer_question_textual_wide_range("Write a cover letter")
-        with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as temp_pdf_file:
-            letter_path = temp_pdf_file.name
-            c = canvas.Canvas(letter_path, pagesize=letter)
-            _, height = letter
-            text_object = c.beginText(100, height - 100)
-            text_object.setFont("Helvetica", 12)
-            text_object.textLines(cover_letter)
-            c.drawText(text_object)
-            c.save()
-            element.send_keys(letter_path)
-            logger.debug("Cover letter created and uploaded successfully: %s", letter_path)
+            logger.debug(f"Uploading cover letter from path: {file_path_pdf}")
+            element.send_keys(os.path.abspath(file_path_pdf))
+            job.cover_letter_path = os.path.abspath(file_path_pdf)
+            time.sleep(2)
+            logger.debug(f"Cover letter created and uploaded successfully: {file_path_pdf}")
+        except Exception as e:
+            tb_str = traceback.format_exc()
+            logger.error(f"Cover letter upload failed: {tb_str}")
+            raise Exception(f"Upload failed: \nTraceback:\n{tb_str}")
 
     def _fill_additional_questions(self) -> None:
         logger.debug("Filling additional questions")
@@ -445,7 +557,6 @@ class LinkedInEasyApplier:
         for section in form_sections:
             self._process_form_section(section)
 
-
     def _process_form_section(self, section: WebElement) -> None:
         logger.debug("Processing form section")
         if self._handle_terms_of_service(section):
@@ -460,13 +571,15 @@ class LinkedInEasyApplier:
         if self._find_and_handle_date_question(section):
             logger.debug("Handled date question")
             return
+
         if self._find_and_handle_dropdown_question(section):
             logger.debug("Handled dropdown question")
             return
 
     def _handle_terms_of_service(self, element: WebElement) -> bool:
         checkbox = element.find_elements(By.TAG_NAME, 'label')
-        if checkbox and any(term in checkbox[0].text.lower() for term in ['terms of service', 'privacy policy', 'terms of use']):
+        if checkbox and any(
+                term in checkbox[0].text.lower() for term in ['terms of service', 'privacy policy', 'terms of use']):
             checkbox[0].click()
             logger.debug("Clicked terms of service checkbox")
             return True
@@ -502,31 +615,29 @@ class LinkedInEasyApplier:
 
         if text_fields:
             text_field = text_fields[0]
-            question_text = section.find_element(By.TAG_NAME, 'label').text.lower()
+            question_text = section.find_element(By.TAG_NAME, 'label').text.lower().strip()
             logger.debug(f"Found text field with label: {question_text}")
 
             is_numeric = self._is_numeric_field(text_field)
             logger.debug(f"Is the field numeric? {'Yes' if is_numeric else 'No'}")
 
-            if is_numeric:
-                question_type = 'numeric'
-                answer = self.gpt_answerer.answer_question_numeric(question_text)
-                logger.debug(f"Generated numeric answer: {answer}")
-            else:
-                question_type = 'textbox'
-                answer = self.gpt_answerer.answer_question_textual_wide_range(question_text)
-                logger.debug(f"Generated textual answer: {answer}")
-
             existing_answer = None
+            question_type = 'numeric' if is_numeric else 'textbox'
+
             for item in self.all_data:
-                if item['question'] == self._sanitize_text(question_text) and item['type'] == question_type:
+
+                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}'")
+
+                if self._sanitize_text(item['question']) == self._sanitize_text(question_text) and item.get(
+                        'type') == question_type:
                     existing_answer = item
                     logger.debug(f"Found existing answer in the data: {existing_answer['answer']}")
                     break
 
             if existing_answer:
                 self._enter_text(text_field, existing_answer['answer'])
-                logger.debug("Entered existing textbox answer.")
+                logger.debug("Entered existing answer into the textbox.")
 
                 time.sleep(1)
                 text_field.send_keys(Keys.ARROW_DOWN)
@@ -534,9 +645,16 @@ class LinkedInEasyApplier:
                 logger.debug("Selected first option from the dropdown.")
                 return True
 
+            if is_numeric:
+                answer = self.gpt_answerer.answer_question_numeric(question_text)
+                logger.debug(f"Generated numeric answer: {answer}")
+            else:
+                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 textbox answer and saved it to JSON.")
+            logger.debug("Entered new answer into the textbox and saved it to JSON.")
 
             time.sleep(1)
             text_field.send_keys(Keys.ARROW_DOWN)
@@ -555,7 +673,6 @@ class LinkedInEasyApplier:
             answer_date = self.gpt_answerer.answer_question_date()
             answer_text = answer_date.strftime("%Y-%m-%d")
 
-
             existing_answer = None
             for item in self.all_data:
                 if self._sanitize_text(question_text) in item['question'] and item['type'] == 'date':
@@ -574,56 +691,44 @@ class LinkedInEasyApplier:
 
     def _find_and_handle_dropdown_question(self, section: WebElement) -> bool:
         try:
+
             question = section.find_element(By.CLASS_NAME, 'jobs-easy-apply-form-element')
             question_text = question.find_element(By.TAG_NAME, 'label').text.lower()
             logger.debug(f"Processing dropdown or combobox question: {question_text}")
 
-            try:
-                dropdown = question.find_element(By.TAG_NAME, 'select')
+            dropdowns = question.find_elements(By.TAG_NAME, 'select')
+            if dropdowns:
+                dropdown = dropdowns[0]
                 select = Select(dropdown)
                 options = [option.text for option in select.options]
                 logger.debug(f"Dropdown options found: {options}")
 
+                current_selection = select.first_selected_option.text
+                logger.debug(f"Current selection: {current_selection}")
+
                 existing_answer = None
                 for item in self.all_data:
                     if self._sanitize_text(question_text) in item['question'] and item['type'] == 'dropdown':
-                        existing_answer = item
+                        existing_answer = item['answer']
                         break
 
                 if existing_answer:
-                    self._select_dropdown_option(dropdown, existing_answer['answer'])
-                    logger.debug("Selected existing dropdown answer")
+                    logger.debug(f"Found existing answer for question '{question_text}': {existing_answer}")
+                    if current_selection != existing_answer:
+                        logger.debug(f"Updating selection to: {existing_answer}")
+                        self._select_dropdown_option(dropdown, existing_answer)
                     return True
 
+                logger.debug(f"No existing answer found, querying model for: {question_text}")
                 answer = self.gpt_answerer.answer_question_from_options(question_text, options)
                 self._save_questions_to_json({'type': 'dropdown', 'question': question_text, 'answer': answer})
                 self._select_dropdown_option(dropdown, answer)
-                logger.debug("Selected new dropdown answer")
-                return True
-
-            except NoSuchElementException:
-                combobox = question.find_element(By.TAG_NAME, 'input')
-                logger.debug(f"Found combobox with ID: {combobox.get_attribute('id')}")
-
-                existing_answer = None
-                for item in self.all_data:
-                    if self._sanitize_text(question_text) in item['question'] and item['type'] == 'combobox':
-                        existing_answer = item
-                        break
-
-                if existing_answer:
-                    self._enter_text(combobox, existing_answer['answer'])
-                    logger.debug("Entered existing combobox answer")
-                    return True
-
-                answer = self.gpt_answerer.answer_question_textual_wide_range(question_text)
-                self._save_questions_to_json({'type': 'combobox', 'question': question_text, 'answer': answer})
-                self._enter_text(combobox, answer)
-                logger.debug("Entered new combobox answer")
+                logger.debug(f"Selected new dropdown answer: {answer}")
                 return True
 
+            return False
         except Exception as e:
-            logger.warning("Failed to handle dropdown or combobox question: %s", e)
+            logger.warning(f"Failed to handle dropdown or combobox question: {e}")
             return False
 
     def _is_numeric_field(self, field: WebElement) -> bool:
@@ -677,7 +782,6 @@ class LinkedInEasyApplier:
             logger.error("Error saving questions data to JSON file: %s", tb_str)
             raise Exception(f"Error saving questions data to JSON file: \nTraceback:\n{tb_str}")
 
-
     def _sanitize_text(self, text: str) -> str:
         sanitized_text = text.lower().strip().replace('"', '').replace('\\', '')
         sanitized_text = re.sub(r'[\x00-\x1F\x7F]', '', sanitized_text).replace('\n', ' ').replace('\r', '').rstrip(',')

src/linkedIn_job_manager.py

@@ -1,15 +1,16 @@
+import json
 import os
 import random
 import time
-import traceback
 from itertools import product
 from pathlib import Path
+
 from selenium.common.exceptions import NoSuchElementException
 from selenium.webdriver.common.by import By
+
 import src.utils as utils
 from src.job import Job
 from src.linkedIn_easy_applier import LinkedInEasyApplier
-import json
 from src.utils import logger
 
 
@@ -33,6 +34,7 @@ class EnvironmentKeys:
         logger.debug("Read environment key %s as bool: %s", key, value)
         return value
 
+
 class LinkedInJobManager:
     def __init__(self, driver):
         logger.debug("Initializing LinkedInJobManager")
@@ -47,7 +49,6 @@ class LinkedInJobManager:
         self.title_blacklist = parameters.get('titleBlacklist', []) or []
         self.positions = parameters.get('positions', [])
         self.locations = parameters.get('locations', [])
-        self.apply_once_at_company = parameters.get('applyOnceAtCompany', False)
         self.base_search_url = self.get_base_search_url(parameters)
         self.seen_jobs = []
         resume_path = parameters.get('uploads', {}).get('resume', None)
@@ -66,7 +67,8 @@ class LinkedInJobManager:
 
     def start_applying(self):
         logger.debug("Starting job application process")
-        self.easy_applier_component = LinkedInEasyApplier(self.driver, self.resume_path, self.set_old_answers, self.gpt_answerer, self.resume_generator_manager)
+        self.easy_applier_component = LinkedInEasyApplier(self.driver, self.resume_path, self.set_old_answers,
+                                                          self.gpt_answerer, self.resume_generator_manager)
         searches = list(product(self.positions, self.locations))
         random.shuffle(searches)
         page_sleep = 0
@@ -134,9 +136,13 @@ class LinkedInJobManager:
                 time.sleep(sleep_time)
                 page_sleep += 1
 
-
     def get_jobs_from_page(self):
+        """
+        Функция для получения списка вакансий на текущей странице.
+        Если вакансии не найдены, возвращает пустой список.
+        """
         try:
+
             no_jobs_element = self.driver.find_element(By.CLASS_NAME, 'jobs-search-two-pane__no-results-banner--expand')
             if 'No matching jobs found' in no_jobs_element.text or 'unfortunately, things aren' in self.driver.page_source.lower():
                 utils.printyellow("No matching jobs found on this page.")
@@ -151,7 +157,8 @@ class LinkedInJobManager:
             utils.scroll_slow(self.driver, job_results)
             utils.scroll_slow(self.driver, job_results, step=300, reverse=True)
 
-            job_list_elements = self.driver.find_elements(By.CLASS_NAME, 'scaffold-layout__list-container')[0].find_elements(By.CLASS_NAME, 'jobs-search-results__list-item')
+            job_list_elements = self.driver.find_elements(By.CLASS_NAME, 'scaffold-layout__list-container')[
+                0].find_elements(By.CLASS_NAME, 'jobs-search-results__list-item')
             if not job_list_elements:
                 utils.printyellow("No job class elements found on page.")
                 logger.debug("No job class elements found on page, skipping.")
@@ -180,7 +187,8 @@ class LinkedInJobManager:
         job_results = self.driver.find_element(By.CLASS_NAME, "jobs-search-results-list")
         utils.scroll_slow(self.driver, job_results)
         utils.scroll_slow(self.driver, job_results, step=300, reverse=True)
-        job_list_elements = self.driver.find_elements(By.CLASS_NAME, 'scaffold-layout__list-container')[0].find_elements(By.CLASS_NAME, 'jobs-search-results__list-item')
+        job_list_elements = self.driver.find_elements(By.CLASS_NAME, 'scaffold-layout__list-container')[
+            0].find_elements(By.CLASS_NAME, 'jobs-search-results__list-item')
         if not job_list_elements:
             utils.printyellow("No job class elements found on page, moving to next page.")
             logger.debug("No job class elements found on page, skipping")
@@ -192,12 +200,6 @@ class LinkedInJobManager:
                 logger.debug("Job blacklisted: %s at %s", job.title, job.company)
                 self.write_to_file(job, "skipped")
                 continue
-            if self.is_already_applied_to_job(job.title, job.company, job.link):
-                self.write_to_file(job, "skipped")
-                continue
-            if self.is_already_applied_to_company(job.company):
-                self.write_to_file(job, "skipped")
-                continue
             try:
                 if job.apply_method not in {"Continue", "Applied", "Apply"}:
                     self.easy_applier_component.job_apply(job)
@@ -244,7 +246,8 @@ class LinkedInJobManager:
         url_parts = []
         if parameters['remote']:
             url_parts.append("f_CF=f_WRA")
-        experience_levels = [str(i+1) for i, (level, v) in enumerate(parameters.get('experienceLevel', {}).items()) if v]
+        experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experienceLevel', {}).items()) if
+                             v]
         if experience_levels:
             url_parts.append(f"f_E={','.join(experience_levels)}")
         url_parts.append(f"distance={parameters['distance']}")
@@ -266,7 +269,8 @@ class LinkedInJobManager:
 
     def next_job_page(self, position, location, job_page):
         logger.debug("Navigating to next job page: %s in %s, page %d", position, location, job_page)
-        self.driver.get(f"https://www.linkedin.com/jobs/search/{self.base_search_url}&keywords={position}{location}&start={job_page * 25}")
+        self.driver.get(
+            f"https://www.linkedin.com/jobs/search/{self.base_search_url}&keywords={position}{location}&start={job_page * 25}")
 
     def extract_job_information_from_tile(self, job_tile):
         logger.debug("Extracting job information from tile")
@@ -287,7 +291,7 @@ class LinkedInJobManager:
         try:
             apply_method = job_tile.find_element(By.CLASS_NAME, 'job-card-container__apply-method').text
         except NoSuchElementException:
-            apply_method = "Applied"  # Подразумеваем, что вакансия уже подана
+            apply_method = "Applied"
             utils.printyellow("Apply method not found, assuming 'Applied'.")
             logger.warning("Apply method not found, assuming 'Applied'.")
 
@@ -299,33 +303,6 @@ class LinkedInJobManager:
         title_blacklisted = any(word in job_title_words for word in self.title_blacklist)
         company_blacklisted = company.strip().lower() in (word.strip().lower() for word in self.company_blacklist)
         link_seen = link in self.seen_jobs
-
         is_blacklisted = title_blacklisted or company_blacklisted or link_seen
         logger.debug("Job blacklisted status: %s", is_blacklisted)
         return is_blacklisted
-
-
-    def is_already_applied_to_job(self, job_title, company, link):
-        link_seen = link in self.seen_jobs
-        if link_seen:
-            utils.printyellow(f"Already applied to job: {job_title} at {company}, skipping...")
-        return link_seen
-
-    def is_already_applied_to_company(self, company):
-        if not self.apply_once_at_company:
-            return False
-
-        output_files = ["success.json"]
-        for file_name in output_files:
-            file_path = self.output_file_directory / file_name
-            if file_path.exists():
-                with open(file_path, 'r', encoding='utf-8') as f:
-                    try:
-                        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...")
-                                return True
-                    except json.JSONDecodeError:
-                        continue
-        return False

src/strings.py

@@ -181,7 +181,7 @@ Answer the following question based on the provided language skills.
 - Answer questions directly.
 - If it seems likely that you have the experience, even if not explicitly defined, answer as if you have the experience.
 - If unsure, respond with "I have no experience with that, but I learn fast" or "Not yet, but willing to learn."
-- Keep the answer under 140 characters.
+- Keep the answer under 140 characters. Do not add any additional languages what is not in my experience
 
 ## Example
 My resume: Fluent in Italian and English.
@@ -238,7 +238,6 @@ This comprehensive overview will serve as a guideline for the recruitment proces
 
 # Job Description Summary"""
 
-
 coverletter_template = """
 Compose a brief and impactful cover letter based on the provided job description and resume. The letter should be no longer than three paragraphs and should be written in a professional, yet conversational tone. Avoid using any placeholders, and ensure that the letter flows naturally and is tailored to the job.
 
@@ -371,7 +370,6 @@ Options: [1-2, 3-5, 6-10, 10+]
 
 ## """
 
-
 try_to_fix_template = """\
 The objective is to fix the text of a form input on a web page.
 

src/utils.py

@@ -1,23 +1,32 @@
+import logging
 import os
 import random
 import time
 
 from selenium import webdriver
 
-import logging
+log_file = "app_log.log"
+
+logging.basicConfig(
+    level=logging.DEBUG,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.FileHandler(log_file, mode='a', encoding='utf-8'),
+        logging.StreamHandler()
+    ],
+    force=True  # This will reset the root logger's handlers and apply the new configuration
+)
 
-# Настройка логирования
-logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
-
-# Отключаем логирование для selenium и urllib3
-logging.getLogger("selenium.webdriver.remote.remote_connection").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("httpcore").setLevel(logging.WARNING)
-
+file_handler = logging.FileHandler(log_file, mode='a', encoding='utf-8')
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+file_handler.setFormatter(formatter)
+logger.addHandler(file_handler)
+logger.setLevel(logging.DEBUG)
 
 chromeProfilePath = os.path.join(os.getcwd(), "chrome_profile", "linkedin_profile")
 
+
 def ensure_chrome_profile():
     logger.debug("Ensuring Chrome profile exists at path: %s", chromeProfilePath)
     profile_dir = os.path.dirname(chromeProfilePath)
@@ -29,51 +38,74 @@ def ensure_chrome_profile():
         logger.debug("Created Chrome profile directory: %s", chromeProfilePath)
     return chromeProfilePath
 
+
 def is_scrollable(element):
     scroll_height = element.get_attribute("scrollHeight")
     client_height = element.get_attribute("clientHeight")
     scrollable = int(scroll_height) > int(client_height)
-    logger.debug("Element scrollable check: scrollHeight=%s, clientHeight=%s, scrollable=%s", scroll_height, client_height, scrollable)
+    logger.debug("Element scrollable check: scrollHeight=%s, clientHeight=%s, scrollable=%s", scroll_height,
+                 client_height, scrollable)
     return scrollable
 
+
 def scroll_slow(driver, scrollable_element, start=0, end=3600, step=300, reverse=False):
     logger.debug("Starting slow scroll: start=%d, end=%d, step=%d, reverse=%s", start, end, step, reverse)
+
     if reverse:
         start, end = end, start
         step = -step
+
     if step == 0:
         logger.error("Step value cannot be zero.")
         raise ValueError("Step cannot be zero.")
 
     max_scroll_height = int(scrollable_element.get_attribute("scrollHeight"))
+    current_scroll_position = int(scrollable_element.get_attribute("scrollTop"))
     logger.debug("Max scroll height of the element: %d", max_scroll_height)
+    logger.debug("Current scroll position: %d", current_scroll_position)
 
-    if end > max_scroll_height:
-        logger.warning("End value exceeds the scroll height. Adjusting end to %d", max_scroll_height)
-        end = max_scroll_height
+    if reverse:
+
+        if current_scroll_position < start:
+            start = current_scroll_position
+        logger.debug("Adjusted start position for upward scroll: %d", start)
+    else:
+
+        if end > max_scroll_height:
+            logger.warning("End value exceeds the scroll height. Adjusting end to %d", max_scroll_height)
+            end = max_scroll_height
 
     script_scroll_to = "arguments[0].scrollTop = arguments[1];"
+
     try:
         if scrollable_element.is_displayed():
             if not is_scrollable(scrollable_element):
                 logger.warning("The element is not scrollable.")
                 print("The element is not scrollable.")
                 return
+
             if (step > 0 and start >= end) or (step < 0 and start <= end):
                 logger.warning("No scrolling will occur due to incorrect start/end values.")
                 print("No scrolling will occur due to incorrect start/end values.")
                 return
-            for position in range(start, end, step):
+
+            position = start
+            while (step > 0 and position < end) or (step < 0 and position > end):
                 try:
                     driver.execute_script(script_scroll_to, scrollable_element, position)
                     logger.debug("Scrolled to position: %d", position)
                 except Exception as e:
                     logger.error("Error during scrolling: %s", e)
                     print(f"Error during scrolling: {e}")
-                time.sleep(random.uniform(1.0, 1.6))
+
+                position += step
+                step = max(10, abs(step) - 10) * (-1 if reverse else 1)
+
+                time.sleep(random.uniform(0.6, 1.5))
+
             driver.execute_script(script_scroll_to, scrollable_element, end)
             logger.debug("Scrolled to final position: %d", end)
-            time.sleep(1)
+            time.sleep(0.5)
         else:
             logger.warning("The element is not visible.")
             print("The element is not visible.")
@@ -81,7 +113,8 @@ def scroll_slow(driver, scrollable_element, start=0, end=3600, step=300, reverse
         logger.error("Exception occurred during scrolling: %s", e)
         print(f"Exception occurred: {e}")
 
-def chromeBrowserOptions():
+
+def chrome_browser_options():
     logger.debug("Setting Chrome browser options")
     ensure_chrome_profile()
     options = webdriver.ChromeOptions()
@@ -112,10 +145,10 @@ def chromeBrowserOptions():
     options.add_experimental_option("prefs", prefs)
 
     if len(chromeProfilePath) > 0:
-        initialPath = os.path.dirname(chromeProfilePath)
-        profileDir = os.path.basename(chromeProfilePath)
-        options.add_argument('--user-data-dir=' + initialPath)
-        options.add_argument("--profile-directory=" + profileDir)
+        initial_path = os.path.dirname(chromeProfilePath)
+        profile_dir = os.path.basename(chromeProfilePath)
+        options.add_argument('--user-data-dir=' + initial_path)
+        options.add_argument("--profile-directory=" + profile_dir)
         logger.debug("Using Chrome profile directory: %s", chromeProfilePath)
     else:
         options.add_argument("--incognito")
@@ -123,14 +156,16 @@ def chromeBrowserOptions():
 
     return options
 
+
 def printred(text):
-    RED = "\033[91m"
-    RESET = "\033[0m"
+    red = "\033[91m"
+    reset = "\033[0m"
     logger.debug("Printing text in red: %s", text)
-    print(f"{RED}{text}{RESET}")
+    print(f"{red}{text}{reset}")
+
 
 def printyellow(text):
-    YELLOW = "\033[93m"
-    RESET = "\033[0m"
+    yellow = "\033[93m"
+    reset = "\033[0m"
     logger.debug("Printing text in yellow: %s", text)
-    print(f"{YELLOW}{text}{RESET}")
+    print(f"{yellow}{text}{reset}")