resolve issues

src/gpt.py

@@ -3,6 +3,8 @@ 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
@@ -17,15 +19,73 @@ 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: ChatOpenAI):
+    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
         logger.debug("Initializing LLMLogger with LLM: %s", llm)
         self.llm = llm
         logger.debug("LLMLogger successfully initialized with LLM: %s", llm)
@@ -48,6 +108,7 @@ 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 = {
@@ -76,7 +137,7 @@ 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"]
@@ -86,14 +147,14 @@ 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
@@ -108,7 +169,7 @@ class LLMLogger:
                 "model": model_name,
                 "time": current_time,
                 "prompts": prompts,
-                "replies": parsed_reply["content"],  # Контент ответа
+                "replies": parsed_reply["content"],  # Response content
                 "total_tokens": total_tokens,
                 "input_tokens": input_tokens,
                 "output_tokens": output_tokens,
@@ -118,7 +179,7 @@ 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)
@@ -130,17 +191,18 @@ class LLMLogger:
 
 
 class LoggerChatModel:
-    def __init__(self, llm: ChatOpenAI):
+    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
         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:
                 logger.debug("Attempting to call the LLM with messages")
-                reply = self.llm(messages)  # Вызов LLM
+                reply = self.llm(messages)
                 logger.debug("LLM response received: %s", reply)
 
                 parsed_reply = self.parse_llmresult(reply)
@@ -180,6 +242,8 @@ 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:
@@ -218,10 +282,9 @@ class LoggerChatModel:
 
 
 class GPTAnswerer:
-    def __init__(self, openai_api_key):
+    def __init__(self, config, llm_api_key):
-        self.llm_cheap = LoggerChatModel(
+        self.ai_adapter = AIAdapter(config, llm_api_key)
-            ChatOpenAI(model_name="gpt-4o-mini", openai_api_key=openai_api_key, temperature=0.4)
+        self.llm_cheap = LoggerChatModel(self.ai_adapter)
-        )
         logger.debug("GPTAnswerer initialized with API key")
 
     @property
@@ -246,6 +309,7 @@ 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)
 
@@ -279,6 +343,7 @@ 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),
@@ -387,7 +452,11 @@ class GPTAnswerer:
         chain = prompt | self.llm_cheap | StrOutputParser()
         output = chain.invoke({"question": question})
         logger.debug("Section determined from question: %s", output)
-        section_name = output.lower().replace(" ", "_")
+        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})
@@ -442,6 +511,7 @@ 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.

src/linkedIn_easy_applier.py

@@ -8,7 +8,6 @@ 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
@@ -175,7 +174,6 @@ class LinkedInEasyApplier:
                 except Exception as e:
                     logger.warning(f"Failed to click 'Easy Apply' button using {method['description']} on attempt {attempt + 1}: {e}")
 
-            # Обновление страницы после первой неудачной попытки
             if attempt == 0:
                 logger.debug("Refreshing page to retry finding 'Easy Apply' button")
                 self.driver.refresh()
@@ -530,8 +528,7 @@ class LinkedInEasyApplier:
                 self._enter_text(text_field, existing_answer['answer'])
                 logger.debug("Entered existing textbox answer.")
 
-                # Нажать "Вниз" и "Enter" для выбора первого элемента в выпадающем списке
+                time.sleep(1)
-                time.sleep(1)  # Ожидание появления выпадающего списка
                 text_field.send_keys(Keys.ARROW_DOWN)
                 text_field.send_keys(Keys.ENTER)
                 logger.debug("Selected first option from the dropdown.")
@@ -541,8 +538,7 @@ class LinkedInEasyApplier:
             self._enter_text(text_field, answer)
             logger.debug("Entered new textbox answer and saved it to JSON.")
 
-            # Нажать "Вниз" и "Enter" для выбора первого элемента в выпадающем списке
+            time.sleep(1)
-            time.sleep(1)  # Ожидание появления выпадающего списка
             text_field.send_keys(Keys.ARROW_DOWN)
             text_field.send_keys(Keys.ENTER)
             logger.debug("Selected first option from the dropdown.")

src/linkedIn_job_manager.py

@@ -47,6 +47,7 @@ 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)
@@ -86,7 +87,6 @@ class LinkedInJobManager:
                     time.sleep(random.uniform(1.5, 3.5))
                     utils.printyellow("Starting the application process for this page...")
 
-                    # Проверка на наличие вакансий на странице
                     try:
                         jobs = self.get_jobs_from_page()
                         if not jobs:
@@ -94,7 +94,7 @@ class LinkedInJobManager:
                             break
                     except Exception as e:
                         logger.error(f"Failed to retrieve jobs: {e}")
-                        break  # Выходим из цикла, если не удалось получить вакансии
+                        break
 
                     try:
                         self.apply_jobs()
@@ -136,40 +136,32 @@ class LinkedInJobManager:
 
 
     def get_jobs_from_page(self):
-        """
-        Функция для получения списка вакансий на текущей странице.
-        Если вакансии не найдены, возвращает пустой список.
-        """
         try:
-            # Проверка на отсутствие вакансий
             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.")
                 logger.debug("No matching jobs found on this page, skipping.")
-                return []  # Возвращаем пустой список, если нет вакансий
+                return []
 
         except NoSuchElementException:
-            pass  # Если элемент не найден, продолжаем поиск вакансий
+            pass
 
-        # Поиск контейнера результатов с вакансиями
         try:
             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)
 
-            # Поиск элементов списка вакансий
             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.")
                 return []
 
-            # Возвращаем список найденных вакансий
             return job_list_elements
 
         except NoSuchElementException:
             logger.debug("No job results found on the page.")
-            return []  # Если не найден контейнер с результатами, возвращаем пустой список
+            return []
 
         except Exception as e:
             logger.error(f"Error while fetching job elements: {e}")
@@ -181,9 +173,9 @@ class LinkedInJobManager:
             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, moving to next.")
                 logger.debug("No matching jobs found on this page, skipping")
-                return  # Выход из метода, если нет больше подходящих вакансий
+                return
         except NoSuchElementException:
-            pass  # Если элемент не найден, просто продолжаем
+            pass
 
         job_results = self.driver.find_element(By.CLASS_NAME, "jobs-search-results-list")
         utils.scroll_slow(self.driver, job_results)
@@ -192,7 +184,7 @@ class LinkedInJobManager:
         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")
-            return  # Выход из метода, если нет вакансий на странице
+            return
         job_list = [Job(*self.extract_job_information_from_tile(job_element)) for job_element in job_list_elements] 
         for job in job_list:
             if self.is_blacklisted(job.title, job.company, job.link):
@@ -200,6 +192,12 @@ 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)
@@ -301,6 +299,33 @@ 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