Merge remote-tracking branch 'upstream/v3' into v3

2024-09-10 21:24:34 +02:00 · 2024-09-10 21:24:34 +02:00 · 40cf3e4d34
commit 40cf3e4d34
parent c85a2025f6 99b74fbb61
13 changed files with 725 additions and 308 deletions
--- a/.gitignore
+++ b/.gitignore
@ -1,14 +1,155 @@
-*.csv
-__pycache__/**
-.idea/**
-open_ai_calls.log
-test*
-openaiSelenium*
-open_ai_calls.json
-_*
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv’s dependency resolution may lead to different
+#   Pipfile.lock files generated on each colleague’s machine.
+#   Thus, uncomment the following line if the pipenv environment is expected to be identical
+#   across all environments.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
 .venv
-generated_cv*
-.vscode
-chrome_profile
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# PyCharm and all JetBrains IDEs
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+.idea/
+*.iml
+
+# Visual Studio Code
+.vscode/
+
+# Visual Studio 2015/2017/2019/2022
+.vs/
+*.opendb
+*.VC.db
+
+# User-specific files
+*.suo
+*.user
+*.userosscache
+*.sln.docstates
+
+# Mono Auto Generated Files
+mono_crash.*
+
+# Project Specific
+data_folder/output/* 
+generated_cv/*
+chrome_profile/*
 answers.json
-data*
--- a/README.md
+++ b/README.md
@ -133,6 +133,11 @@ LinkedIn_AIHawk steps in as a game-changing solution to these challenges. It's n
   source virtual/bin/activate
   ```

+   or for Windows-based machines -
+   ```bash
+   .\virtual\Scripts\activate
+   ```
+
 5. **Install the required packages:**
   ```bash
   pip install -r requirements.txt
@ -148,7 +153,7 @@ This file contains sensitive information. Never share or commit this file to ver
  - Replace with your LinkedIn account email address
 - `password: [Your LinkedIn password]`
  - Replace with your LinkedIn account password
- `llm_api_key: [Your OpenAI or Ollama API key]`
+- `llm_api_key: [Your OpenAI or Ollama API key or Gemini API key]`
  - Replace with your OpenAI API key for GPT integration
  - To obtain an API key, follow the tutorial at: https://medium.com/@lorenzozar/how-to-get-your-own-openai-api-key-f4d44e60c327
  - Note: You need to add credit to your OpenAI account to use the API. You can add credit by visiting the [OpenAI billing dashboard](https://platform.openai.com/account/billing).
@ -157,6 +162,7 @@ This file contains sensitive information. Never share or commit this file to ver
    `{'error': {'message': 'Rate limit reached for gpt-4o-mini in organization <org> on requests per day (RPD): Limit 200, Used 200, Requested 1.}}`  
    OpenAI will update your account automatically, but it might take some time, ranging from a couple of hours to a few days.  
    You can find more about your organization limits on the [official page](https://platform.openai.com/settings/organization/limits).
+  - For obtaining Gemini API key visit [Google AI for Devs](https://ai.google.dev/gemini-api/docs/api-key)


 ### 2. config.yaml
@ -220,17 +226,19 @@ This file defines your job search parameters and bot behavior. Each section cont
 #### 2.1 config.yaml - Customize LLM model endpoint

 - `llm_model_type`:
-  - Choose the model type, supported: openai / ollama / claude
+  - Choose the model type, supported: openai / ollama / claude / gemini
 - `llm_model`: 
  - Choose the LLM model, currently supported: 
    - openai: gpt-4o
    - ollama: llama2, mistral:v0.3
    - claude: any model 
+    - gemini: any model
 - `llm_api_url`: 
  - Link of the API endpoint for the LLM model
    - openai: https://api.pawan.krd/cosmosrp/v1
    - ollama: http://127.0.0.1:11434/
    - claude: https://api.anthropic.com/v1
+    - gemini: no api_url
 - Note: To run local Ollama, follow the guidelines here: [Guide to Ollama deployment](https://github.com/ollama/ollama)
  
 ### 3. plain_text_resume.yaml
@ -272,7 +280,8 @@ Each section has specific fields to fill out:
  - This section outlines your academic background, including degrees earned and relevant coursework.
    - **degree**: The type of degree obtained (e.g., Bachelor's Degree, Master's Degree).
    - **university**: The name of the university or institution where you studied.
-    - **gpa**: Your Grade Point Average or equivalent measure of academic performance.
+    - **final_evaluation_grade**: Your Grade Point Average or equivalent measure of academic performance.
+    - **start_date**: The start year of your studies.
    - **graduation_year**: The year you graduated.
    - **field_of_study**: The major or focus area of your studies.
    - **exam**: A list of courses or subjects taken along with their respective grades.
@ -280,11 +289,12 @@ Each section has specific fields to fill out:
  - Example:
  ```yaml
  education_details:
-    - degree: "Bachelor's Degree"
-      university: "University of Example"
-      gpa: "3.8/4"
-      graduation_year: "2022"
+    - education_level: "Bachelor's Degree"
+      institution: "University of Example"
      field_of_study: "Software Engineering"
+      final_evaluation_grade: "4/4"
+      start_date: "2021"
+      year_of_completion: "2023"
      exam:
        Algorithms: "A"
        Data Structures: "B+"
@ -354,7 +364,8 @@ Each section has specific fields to fill out:

 - `certifications:`
  - Include any professional certifications you have earned.
-    - **certification_name**: The name of the certification.
+      - name: "PMP"  
+      description: "Certification for project management professionals, issued by the Project Management Institute (PMI)"

  - Example:
  ```yaml
--- a/data_folder/config.yaml
+++ b/data_folder/config.yaml
@ -1,6 +1,6 @@
 remote: [true/false]

-experienceLevel:
+experience_level:
 internship: [true/false]
 entry: [true/false]
 associate: [true/false]
@ -31,18 +31,22 @@ locations:
  - Country1
  - Country2

-applyOnceAtCompany: [true/false]
+apply_once_at_company: [true/false]

 distance: 100

-companyBlacklist:
+company_blacklist:
  - Company1
  - Company2

-titleBlacklist:
+title_blacklist:
  - word1
  - word2

+job_applicants_threshold:
+  min_applicants: 0
+  max_applicants: 100
+  
 llm_model_type: openai
 llm_model: gpt-4o 
 llm_api_url: https://api.pawan.krd/cosmosrp/v1
--- a/data_folder/plain_text_resume.yaml
+++ b/data_folder/plain_text_resume.yaml
@ -1,7 +1,7 @@
 personal_information:
  name: "[Your Name]"
  surname: "[Your Surname]"
-  date_of_birth: "[DD/MM/YYYY]"
+  date_of_birth: "[Your Date of Birth]"
  country: "[Your Country]"
  city: "[Your City]"
  address: "[Your Address]"
@ -12,74 +12,92 @@ personal_information:
  linkedin: "[Your LinkedIn Profile URL]"

 education_details:
-  - degree: "[Your Degree]"
-    university: "[Your University]"
-    gpa: "[Your GPA]"
-    graduation_year: "[Year of Graduation]"
+  - education_level: "[Your Education Level]"
+    institution: "[Your Institution]"
    field_of_study: "[Your Field of Study]"
+    final_evaluation_grade: "[Your Final Evaluation Grade]"
+    start_date: "[Start Date]"
+    year_of_completion: "[Year of Completion]"
    exam:
-      [Course Name 1]: "[Grade]"
-      [Course Name 2]: "[Grade]"
-      [Course Name 3]: "[Grade]"
-      [Course Name 4]: "[Grade]"
-      [Course Name 5]: "[Grade]"
+      exam_name_1: "[Grade]"
+      exam_name_2: "[Grade]"
+      exam_name_3: "[Grade]"
+      exam_name_4: "[Grade]"
+      exam_name_5: "[Grade]"
+      exam_name_6: "[Grade]"

 experience_details:
-  - position: "[Your Job Title]"
+  - position: "[Your Position]"
    company: "[Company Name]"
-    employment_period: "[Start Date] - [End Date]"
+    employment_period: "[Employment Period]"
    location: "[Location]"
    industry: "[Industry]"
    key_responsibilities:
-      - responsibility_1: "[Key Responsibility 1]"
-      - responsibility_2: "[Key Responsibility 2]"
-      - responsibility_3: "[Key Responsibility 3]"
+      - responsibility_1: "[Responsibility Description]"
+      - responsibility_2: "[Responsibility Description]"
+      - responsibility_3: "[Responsibility Description]"
    skills_acquired:
-      - "[Skill 1]"
-      - "[Skill 2]"
-      - "[Skill 3]"
+      - "[Skill]"
+      - "[Skill]"
+      - "[Skill]"
+
+  - position: "[Your Position]"
+    company: "[Company Name]"
+    employment_period: "[Employment Period]"
+    location: "[Location]"
+    industry: "[Industry]"
+    key_responsibilities:
+      - responsibility_1: "[Responsibility Description]"
+      - responsibility_2: "[Responsibility Description]"
+      - responsibility_3: "[Responsibility Description]"
+    skills_acquired:
+      - "[Skill]"
+      - "[Skill]"
+      - "[Skill]"

 projects:
  - name: "[Project Name]"
-    description: "[Brief Description of the Project]"
-    link: "[Project URL]"
+    description: "[Project Description]"
+    link: "[Project Link]"
+
  - name: "[Project Name]"
-    description: "[Brief Description of the Project]"
-    link: "[Project URL]"
+    description: "[Project Description]"
+    link: "[Project Link]"

 achievements:
-  - name: "[Achievement Title]"
-    description: "[Brief Description of the Achievement]"
-  - name: "[Achievement Title]"
-    description: "[Brief Description of the Achievement]"
+  - name: "[Achievement Name]"
+    description: "[Achievement Description]"
+  - name: "[Achievement Name]"
+    description: "[Achievement Description]"

 certifications:
-  - "[Certification Name]"
+  - name: "[Certification Name]"
+    description: "[Certification Description]"
+  - name: "[Certification Name]"
+    description: "[Certification Description]"

 languages:
-  - language: "[Language Name]"
+  - language: "[Language]"
    proficiency: "[Proficiency Level]"
-  - language: "[Language Name]"
+  - language: "[Language]"
    proficiency: "[Proficiency Level]"

 interests:
-  - "[Interest 1]"
-  - "[Interest 2]"
-  - "[Interest 3]"
-  - "[Interest 4]"
-  - "[Interest 5]"
+  - "[Interest]"
+  - "[Interest]"
+  - "[Interest]"

 availability:
  notice_period: "[Notice Period]"

 salary_expectations:
-  salary_range_usd: "[Expected Salary Range in USD]"
+  salary_range_usd: "[Salary Range]"

 self_identification:
  gender: "[Gender]"
  pronouns: "[Pronouns]"
-  veteran: "[Veteran Status]"
-  disability: "[Disability Status]"
+  veteran: "[Yes/No]"
+  disability: "[Yes/No]"
  ethnicity: "[Ethnicity]"

 legal_authorization:
--- a/data_folder_example/config.yaml
+++ b/data_folder_example/config.yaml
@ -1,6 +1,6 @@
 remote: true

-experienceLevel:
+experience_level:
  internship: true
  entry: true
  associate: true
@ -29,15 +29,21 @@ positions:
 locations:
  - USA

-applyOnceAtCompany: [true/false]
+apply_once_at_company: [true/false]

 distance: 100

-companyBlacklist:
+company_blacklist:
  - Noir
  - Crossover

-titleBlacklist:
+title_blacklist:
+  - word1
+  - word2
+
+job_applicants_threshold:
+  min_applicants: 0
+  max_applicants: 100

 llm_model_type: openai
 llm_model: 'gpt-4o'
--- a/data_folder_example/plain_text_resume.yaml
+++ b/data_folder_example/plain_text_resume.yaml
@ -1,118 +1,124 @@
 personal_information:
-  name: "Liam"
-  surname: "Murphy"
-  date_of_birth: "15/08/1995"
-  country: "Ireland"
-  city: "Galway"
-  address: "Galway City Center"
-  phone_prefix: "+353"
-  phone: "871234567"
-  email: "liam.murphy@gmail.com"
-  github: "https://github.com/liam-murphy"
-  linkedin: "https://www.linkedin.com/in/liam-murphy/"
+  name: "Giovanni"
+  surname: "Bianchi"
+  date_of_birth: "12/02/1988"
+  country: "Italy"
+  city: "Rome"
+  address: "Via Nazionale, 45"
+  phone_prefix: "+39"
+  phone: "3345678901"
+  email: "giovanni.bianchi@example.com"
+  github: "https://github.com/giovanni-bianchi"
+  linkedin: "https://www.linkedin.com/in/giovanni-bianchi/"

 education_details:
-  - degree: "Bachelor's Degree"
-    university: "National University of Ireland, Galway"
-    gpa: "4/4"
-    graduation_year: "2020"
-    field_of_study: "Computer Science"
+  - education_level: "Master's Degree"
+    institution: "University of Rome"
+    field_of_study: "Computer Engineering"
+    final_evaluation_grade: "110/110"
+    start_date: "2011"
+    year_of_completion: "2013"
    exam:
-      Information Theory and Inference: "4"
-      Algorithm Analysis and Design: "4"
-      Object-Oriented Languages and Programming: "4"
-      Linear Algebra and Numerical Analysis: "4"
-      Database: "4"
+      Computer Networks: "30/30"
+      Advanced Algorithms: "30/30"
+      Database Systems: "30/30"
+      Embedded Systems: "30/30"
+      Artificial Intelligence: "30/30"

 experience_details:
-  - position: "Co-Founder & Software Engineer"
-    company: "CryptoWave Solutions"
-    employment_period: "03/2021 - Present"
-    location: "Ireland"
-    industry: "Blockchain Technology"
+  - position: "Senior Software Engineer"
+    company: "TechSolutions"
+    employment_period: "01/2018 - Present"
+    location: "Rome, Italy"
+    industry: "Software Development"
    key_responsibilities:
-      - responsibility_1: "Co-founded and led a startup specializing in app and software development with a focus on blockchain technology"
-      - responsibility_2: "Provided blockchain consultations for 10+ companies, enhancing their software capabilities with secure, decentralized solutions"
-      - responsibility_3: "Developed blockchain applications, integrated cutting-edge technology to meet client needs and drive industry innovation"
+      - responsibility_1: "Led a team of developers in designing and implementing enterprise software solutions"
+      - responsibility_2: "Architected scalable systems to handle high-volume data processing"
+      - responsibility_3: "Optimized application performance and reduced downtime by 20%"
    skills_acquired:
-      - "Blockchain development"
-      - "Software engineering"
-      - "Consultancy"
+      - "Software architecture"
+      - "Team leadership"
+      - "Performance optimization"

-  - position: "Research Intern"
-    company: "National University of Ireland, Galway"
-    employment_period: "11/2022 - 03/2023"
-    location: "Galway, Ireland"
-    industry: "IoT Security Research"
+  - position: "Software Developer"
+    company: "Innovatech"
+    employment_period: "06/2015 - 12/2017"
+    location: "Milan, Italy"
+    industry: "Technology"
    key_responsibilities:
-      - responsibility_1: "Conducted in-depth research on IoT security, focusing on binary instrumentation and runtime monitoring"
-      - responsibility_2: "Performed in-depth study of the MQTT protocol and Falco"
-      - responsibility_3: "Developed multiple software components including MQTT packet analysis library, Falco adapter, and RML monitor in Prolog"
-      - responsibility_4: "Authored thesis 'Binary Instrumentation for Runtime Monitoring of Internet of Things Systems Using Falco'"
+      - responsibility_1: "Developed and maintained web applications using modern technologies"
+      - responsibility_2: "Collaborated with UX/UI designers to enhance user experience"
+      - responsibility_3: "Implemented automated testing procedures to ensure code quality"
    skills_acquired:
-      - "IoT security"
-      - "Binary instrumentation"
-      - "MQTT protocol"
-      - "Prolog programming"
+      - "Web development"
+      - "User experience design"
+      - "Automated testing"

-  - position: "Software Engineer"
-    company: "University Hospital Galway"
-    employment_period: "05/2022 - 11/2022"
-    location: "Galway, Ireland"
-    industry: "Healthcare IT"
+  - position: "Junior Developer"
+    company: "StartUp Hub"
+    employment_period: "01/2014 - 05/2015"
+    location: "Florence, Italy"
+    industry: "Startups"
    key_responsibilities:
-      - responsibility_1: "Integrated and enforced robust security protocols"
-      - responsibility_2:  "Developed and maintained a critical software tool for password validation used by over 1,600 employees"
-      - responsibility_3:  "Played an integral role in the hospital's cybersecurity team"
+      - responsibility_1: "Assisted in the development of mobile applications and web platforms"
+      - responsibility_2: "Participated in code reviews and contributed to software design discussions"
+      - responsibility_3: "Resolved bugs and implemented feature enhancements"
    skills_acquired:
-      - "Cybersecurity"
-      - "Software development"
-      - "Password validation"
+      - "Mobile app development"
+      - "Code reviews"
+      - "Bug fixing"

 projects:
-  - name: "JobBot"
-    description: "AI-driven tool to automate and personalize job applications on LinkedIn, gained over 3000 stars on GitHub, improving efficiency and reducing application time"
-    link: "https://github.com/liam-murphy/jobbot"
-  - name: "mqtt-packet-parser"
-    description: "Developed a Node.js module for parsing MQTT packets, improved parsing efficiency by 40%"
-    link: "https://github.com/liam-murphy/mqtt-packet-parser"
+  - name: "E-Commerce Platform"
+    description: "Developed a scalable e-commerce platform with advanced features like real-time inventory tracking and user analytics"
+    link: "https://github.com/giovanni-bianchi/ecommerce-platform"
+  - name: "Smart Home Automation"
+    description: "Created a smart home automation system integrating various IoT devices for remote control and monitoring"
+    link: "https://github.com/giovanni-bianchi/smart-home-automation"

 achievements:
-  - name: "Winner of an Irish public competition"
-    description: "Won first place in a public competition with a perfect score of 70/70, securing a Software Developer position at University Hospital Galway"
-  - name: "Galway Merit Scholarship"
-    description: "Awarded annually from 2018 to 2020 in recognition of academic excellence and contribution"
-  - name: "GitHub Recognition"
-    description: "Gained over 3000 stars on GitHub with JobBot project"
+  - name: "Top Innovator Award"
+    description: "Recognized for innovative solutions and contributions to high-impact projects at TechSolutions"
+  - name: "Best Young Developer"
+    description: "Awarded for outstanding performance and contributions during the first three years at Innovatech"

 certifications:
-  - "C1"
+  - name: "Certified Ethical Hacker (CEH)"
+    description: "Certification for expertise in ethical hacking and cybersecurity practices"
+  - name: "AWS Certified DevOps Engineer"
+    description: "Certification for DevOps practices and using AWS for cloud services"
+  - name: "Microsoft Certified: Azure Solutions Architect Expert"
+    description: "Certification for designing and implementing Azure solutions"
+  - name: "Certified Kubernetes Administrator (CKA)"
+    description: "Certification for managing and orchestrating Kubernetes clusters"
+  - name: "Certified Data Privacy Professional (CDPP)"
+    description: "Certification for ensuring data privacy and compliance with regulations"

 languages:
-  - language: "English"
+  - language: "Italian"
    proficiency: "Native"
-  - language: "Spanish"
-    proficiency: "Professional"
+  - language: "English"
+    proficiency: "Fluent"

 interests:
-  - "Full-Stack Development"
-  - "Software Architecture"
-  - "IoT system design and development"
+  - "Cloud Computing"
+  - "Cybersecurity"
+  - "IoT Development"
  - "Artificial Intelligence"
-  - "Cloud Technologies"
+  - "Data Privacy"

 availability:
-  notice_period: "immediately"
+  notice_period: "2 months"

 salary_expectations:
-  salary_range_usd: "100000"
+  salary_range_usd: "90000 - 110000"

 self_identification:
  gender: "Male"
-  pronouns: "He"
+  pronouns: "He/Him"
  veteran: "No"
  disability: "No"
-  ethnicity: "white"
+  ethnicity: "White"

 legal_authorization:
  eu_work_authorization: "Yes"
--- a/main.py
+++ b/main.py
@ -9,7 +9,7 @@ from selenium.webdriver.chrome.service import Service as ChromeService
 from webdriver_manager.chrome import ChromeDriverManager
 from selenium.common.exceptions import WebDriverException, TimeoutException
 from lib_resume_builder_AIHawk import Resume,StyleManager,FacadeManager,ResumeGenerator
-from src.utils import chromeBrowserOptions
+from src.utils import chrome_browser_options
 from src.gpt import GPTAnswerer
 from src.linkedIn_authenticator import LinkedInAuthenticator
 from src.linkedIn_bot_facade import LinkedInBotFacade
@ -149,7 +149,7 @@ class FileManager:

 def init_browser() -> webdriver.Chrome:
    try:
-        options = chromeBrowserOptions()
+        options = chrome_browser_options()
        service = ChromeService(ChromeDriverManager().install())
        return webdriver.Chrome(service=service, options=options)
    except Exception as e:
--- a/requirements.txt
+++ b/requirements.txt
@ -14,3 +14,12 @@ click
 git+https://github.com/feder-cr/lib_resume_builder_AIHawk.git
 linkedin-api
 pdfminer.six==20221105
+inputimeout==1.0.4
+langchain-ollama==0.1.3
+langchain-anthropic==0.1.3
+langchain-google-genai==1.0.10
+jsonschema==4.23.0
+jsonschema-specifications==2023.12.1
+httpx~=0.27.2
+python-dotenv~=1.0.1
+PyYAML~=6.0.2
--- a/src/gpt.py
+++ b/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
@ -39,16 +40,18 @@ 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
        self.model = ChatAnthropic(model=llm_model, api_key=api_key,
-                                temperature=0.4, base_url=llm_api_url)
+                                   temperature=0.4, base_url=llm_api_url)

    def invoke(self, prompt: str) -> str:
        response = self.model.invoke(prompt)
        return response

+
 class OllamaModel(AIModel):
    def __init__(self, api_key: str, llm_model: str, llm_api_url: str):
        from langchain_ollama import ChatOllama
@ -58,6 +61,17 @@ class OllamaModel(AIModel):
        response = self.model.invoke(prompt)
        return response

+
+class GeminiModel(AIModel):
+    def __init__(self, api_key:str, llm_model: str, llm_api_url: str):
+        from langchain_google_genai import ChatGoogleGenerativeAI
+        self.model = ChatGoogleGenerativeAI(model=llm_model, google_api_key=api_key)
+
+    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)
@ -66,7 +80,8 @@ class AIAdapter:
        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))
+        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)
@ -74,16 +89,18 @@ class AIAdapter:
            return ClaudeModel(api_key, llm_model, llm_api_url)
        elif llm_model_type == "ollama":
            return OllamaModel(api_key, llm_model, llm_api_url)
+        elif llm_model_type == "gemini":
+            return GeminiModel(api_key, llm_model, llm_api_url)
        else:
-            raise ValueError(f"Unsupported model type: {model_type}")
+            raise ValueError(f"Unsupported model type: {llm_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: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):

        self.llm = llm
        logger.debug("LLMLogger successfully initialized with LLM: %s", llm)
@ -95,7 +112,8 @@ class LLMLogger:
        logger.debug("Parsed reply received: %s", parsed_reply)

        try:
-            calls_log = os.path.join(Path("data_folder/output"), "open_ai_calls.json")
+            calls_log = os.path.join(
+                Path("data_folder/output"), "open_ai_calls.json")
            logger.debug("Logging path determined: %s", calls_log)
        except Exception as e:
            logger.error("Error determining the log path: %s", str(e))
@ -114,18 +132,22 @@ class LLMLogger:
                }
                logger.debug("Prompts converted to dictionary: %s", prompts)
            except Exception as e:
-                logger.error("Error converting prompts to dictionary: %s", str(e))
+                logger.error(
+                    "Error converting prompts to dictionary: %s", str(e))
                raise
        else:
-            logger.debug("Prompts are of unknown type, attempting default conversion")
+            logger.debug(
+                "Prompts are of unknown type, attempting default conversion")
            try:
                prompts = {
                    f"prompt_{i + 1}": prompt.content
                    for i, prompt in enumerate(prompts.messages)
                }
-                logger.debug("Prompts converted to dictionary using default method: %s", prompts)
+                logger.debug(
+                    "Prompts converted to dictionary using default method: %s", prompts)
            except Exception as e:
-                logger.error("Error converting prompts using default method: %s", str(e))
+                logger.error(
+                    "Error converting prompts using default method: %s", str(e))
                raise

        try:
@ -140,7 +162,8 @@ class LLMLogger:
            output_tokens = token_usage["output_tokens"]
            input_tokens = token_usage["input_tokens"]
            total_tokens = token_usage["total_tokens"]
-            logger.debug("Token usage - Input: %d, Output: %d, Total: %d", input_tokens, output_tokens, total_tokens)
+            logger.debug("Token usage - Input: %d, Output: %d, Total: %d",
+                         input_tokens, output_tokens, total_tokens)
        except KeyError as e:
            logger.error("KeyError in parsed_reply structure: %s", str(e))
            raise
@ -155,7 +178,8 @@ class LLMLogger:
        try:
            prompt_price_per_token = 0.00000015
            completion_price_per_token = 0.0000006
-            total_cost = (input_tokens * prompt_price_per_token) + (output_tokens * completion_price_per_token)
+            total_cost = (input_tokens * prompt_price_per_token) + \
+                (output_tokens * completion_price_per_token)
            logger.debug("Total cost calculated: %f", total_cost)
        except Exception as e:
            logger.error("Error calculating total cost: %s", str(e))
@ -174,12 +198,14 @@ class LLMLogger:
            }
            logger.debug("Log entry created: %s", log_entry)
        except KeyError as e:
-            logger.error("Error creating log entry: missing key %s in parsed_reply", str(e))
+            logger.error(
+                "Error creating log entry: missing key %s in parsed_reply", str(e))
            raise

        try:
            with open(calls_log, "a", encoding="utf-8") as f:
-                json_string = json.dumps(log_entry, ensure_ascii=False, indent=4)
+                json_string = json.dumps(
+                    log_entry, ensure_ascii=False, indent=4)
                f.write(json_string + "\n")
                logger.debug("Log entry written to file: %s", calls_log)
        except Exception as e:
@ -189,25 +215,25 @@ class LLMLogger:

 class LoggerChatModel:

-
-    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel]):
-
+    def __init__(self, llm: Union[OpenAIModel, OllamaModel, ClaudeModel, GeminiModel]):
        self.llm = llm
-        logger.debug("LoggerChatModel successfully initialized with LLM: %s", llm)
+        logger.debug(
+            "LoggerChatModel successfully initialized with LLM: %s", llm)

    def __call__(self, messages: List[Dict[str, str]]) -> str:
-
        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)
+
+                reply = self.llm.invoke(messages)
                logger.debug("LLM response received: %s", reply)

                parsed_reply = self.parse_llmresult(reply)
                logger.debug("Parsed LLM reply: %s", parsed_reply)

-                LLMLogger.log_request(prompts=messages, parsed_reply=parsed_reply)
+                LLMLogger.log_request(
+                    prompts=messages, parsed_reply=parsed_reply)
                logger.debug("Request successfully logged")

                return reply
@ -243,11 +269,11 @@ class LoggerChatModel:

            except Exception as e:
                logger.error("Unexpected error occurred: %s", str(e))
-                logger.info("Waiting for 30 seconds before retrying due to an unexpected error.")
+                logger.info(
+                    "Waiting for 30 seconds before retrying due to an unexpected error.")
                time.sleep(30)
                continue

-
    def parse_llmresult(self, llmresult: AIMessage) -> Dict[str, Dict]:
        logger.debug("Parsing LLM result: %s", llmresult)

@ -277,11 +303,13 @@ class LoggerChatModel:
            return parsed_result

        except KeyError as e:
-            logger.error("KeyError while parsing LLM result: missing key %s", str(e))
+            logger.error(
+                "KeyError while parsing LLM result: missing key %s", str(e))
            raise

        except Exception as e:
-            logger.error("Unexpected error while parsing LLM result: %s", str(e))
+            logger.error(
+                "Unexpected error while parsing LLM result: %s", str(e))
            raise


@ -297,7 +325,8 @@ class GPTAnswerer:

    @staticmethod
    def find_best_match(text: str, options: list[str]) -> str:
-        logger.debug("Finding best match for text: '%s' in options: %s", text, options)
+        logger.debug(
+            "Finding best match for text: '%s' in options: %s", text, options)
        distances = [
            (option, distance(text.lower(), option.lower())) for option in options
        ]
@ -323,10 +352,12 @@ class GPTAnswerer:
    def set_job(self, job):
        logger.debug("Setting job: %s", job)
        self.job = job
-        self.job.set_summarize_job_description(self.summarize_job_description(self.job.description))
+        self.job.set_summarize_job_description(
+            self.summarize_job_description(self.job.description))

    def set_job_application_profile(self, job_application_profile):
-        logger.debug("Setting job application profile: %s", job_application_profile)
+        logger.debug("Setting job application profile: %s",
+                     job_application_profile)
        self.job_application_profile = job_application_profile

    def summarize_job_description(self, text: str) -> str:
@ -334,7 +365,8 @@ class GPTAnswerer:
        strings.summarize_prompt_template = self._preprocess_template_string(
            strings.summarize_prompt_template
        )
-        prompt = ChatPromptTemplate.from_template(strings.summarize_prompt_template)
+        prompt = ChatPromptTemplate.from_template(
+            strings.summarize_prompt_template)
        chain = prompt | self.llm_cheap | StrOutputParser()
        output = chain.invoke({"text": text})
        logger.debug("Summary generated: %s", output)
@ -454,33 +486,40 @@ 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.")
+            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})
+            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)
        if resume_section is None:
-            logger.error("Section '%s' not found in either resume or job_application_profile.", section_name)
+            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.")
        chain = chains.get(section_name)
        if chain is None:
            logger.error("Chain not defined for section '%s'", section_name)
            raise ValueError(f"Chain not defined for section '{section_name}'")
-        output = chain.invoke({"resume_section": resume_section, "question": question})
+        output = chain.invoke(
+            {"resume_section": resume_section, "question": question})
        logger.debug("Question answered: %s", output)
        return output

    def answer_question_numeric(self, question: str, default_experience: int = 3) -> int:
        logger.debug("Answering numeric question: %s", question)
-        func_template = self._preprocess_template_string(strings.numeric_question_template)
+        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(
@ -491,7 +530,8 @@ class GPTAnswerer:
            output = self.extract_number_from_string(output_str)
            logger.debug("Extracted number: %d", output)
        except ValueError:
-            logger.warning("Failed to extract number, using default experience: %d", default_experience)
+            logger.warning(
+                "Failed to extract number, using default experience: %d", default_experience)
            output = default_experience
        return output

@ -507,17 +547,20 @@ class GPTAnswerer:

    def answer_question_from_options(self, question: str, options: list[str]) -> str:
        logger.debug("Answering question from options: %s", question)
-        func_template = self._preprocess_template_string(strings.options_template)
+        func_template = self._preprocess_template_string(
+            strings.options_template)
        prompt = ChatPromptTemplate.from_template(func_template)
        chain = prompt | self.llm_cheap | StrOutputParser()
-        output_str = chain.invoke({"resume": self.resume, "question": question, "options": options})
+        output_str = chain.invoke(
+            {"resume": self.resume, "question": question, "options": options})
        logger.debug("Raw output for options question: %s", output_str)
        best_option = self.find_best_match(output_str, options)
        logger.debug("Best option determined: %s", best_option)
        return best_option

    def resume_or_cover(self, phrase: str) -> str:
-        logger.debug("Determining if phrase refers to resume or cover letter: %s", phrase)
+        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 one word 'upload', consider it as 'cover'.
--- a/src/linkedIn_easy_applier.py
+++ b/src/linkedIn_easy_applier.py
@ -8,7 +8,7 @@ import traceback
 from typing import List, Optional, Any, Tuple

 from httpx import HTTPStatusError
-from reportlab.lib.pagesizes import letter
+from reportlab.lib.pagesizes import A4
 from reportlab.pdfgen import canvas
 from selenium.common.exceptions import NoSuchElementException, TimeoutException
 from selenium.webdriver import ActionChains
@ -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)
@ -60,10 +59,8 @@ 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

@ -80,7 +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 +163,7 @@ class LinkedInEasyApplier:
                    logger.debug(f"Attempting search using {method['description']}")

                    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):
@ -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:
@ -514,11 +509,47 @@ class LinkedInEasyApplier:
                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)
+                c = canvas.Canvas(file_path_pdf, pagesize=A4)
+                page_width, page_height = A4
+                text_object = c.beginText(50, page_height - 50)
                text_object.setFont("Helvetica", 12)
-                text_object.textLines(cover_letter_text)
+
+                max_width = page_width - 100
+                bottom_margin = 50
+                available_height = page_height - bottom_margin - 50
+
+                def split_text_by_width(text, font, font_size, max_width):
+                    wrapped_lines = []
+                    for line in text.splitlines():
+
+                        if utils.stringWidth(line, font, font_size) > max_width:
+                            words = line.split()
+                            new_line = ""
+                            for word in words:
+                                if utils.stringWidth(new_line + word + " ", font, font_size) <= max_width:
+                                    new_line += word + " "
+                                else:
+                                    wrapped_lines.append(new_line.strip())
+                                    new_line = word + " "
+                            wrapped_lines.append(new_line.strip())
+                        else:
+                            wrapped_lines.append(line)
+                    return wrapped_lines
+
+                lines = split_text_by_width(cover_letter_text, "Helvetica", 12, max_width)
+
+                for line in lines:
+                    text_height = text_object.getY()
+                    if text_height > bottom_margin:
+                        text_object.textLine(line)
+                    else:
+
+                        c.drawText(text_object)
+                        c.showPage()
+                        text_object = c.beginText(50, page_height - 50)
+                        text_object.setFont("Helvetica", 12)
+                        text_object.textLine(line)
+
                c.drawText(text_object)
                c.save()
                logger.debug(f"Cover letter successfully generated and saved to: {file_path_pdf}")
@ -632,7 +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}'")

@ -659,7 +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.")
@ -692,7 +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")
@ -701,12 +729,12 @@ 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}")

            dropdowns = question.find_elements(By.TAG_NAME, 'select')
+            if not dropdowns:
+                dropdowns = section.find_elements(By.CSS_SELECTOR, '[data-test-text-entity-list-form-select]')
+
            if dropdowns:
                dropdown = dropdowns[0]
                select = Select(dropdown)
@ -714,6 +742,9 @@ 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}")
+
                current_selection = select.first_selected_option.text
                logger.debug(f"Current selection: {current_selection}")

@ -738,9 +769,15 @@ class LinkedInEasyApplier:
                logger.debug(f"Selected new dropdown answer: {answer}")
                return True

-            return False
+            else:
+
+                logger.debug(f"No dropdown found. Logging elements for debugging.")
+                elements = section.find_elements(By.XPATH, ".//*")
+                logger.debug(f"Elements found: {[element.tag_name for element in elements]}")
+                return False
+
        except Exception as e:
-            logger.warning(f"Failed to handle dropdown or combobox question: {e}")
+            logger.warning(f"Failed to handle dropdown or combobox question: {e}", exc_info=True)
            return False

    def _is_numeric_field(self, field: WebElement) -> bool:
--- a/src/linkedIn_job_manager.py
+++ b/src/linkedIn_job_manager.py
@ -5,6 +5,7 @@ import time
 from itertools import product
 from pathlib import Path

+from inputimeout import inputimeout, TimeoutOccurred
 from selenium.common.exceptions import NoSuchElementException
 from selenium.webdriver.common.by import By

@ -45,13 +46,18 @@ class LinkedInJobManager:

    def set_parameters(self, parameters):
        logger.debug("Setting parameters for LinkedInJobManager")
-        self.company_blacklist = parameters.get('companyBlacklist', []) or []
-        self.title_blacklist = parameters.get('titleBlacklist', []) or []
+        self.company_blacklist = parameters.get('company_blacklist', []) or []
+        self.title_blacklist = parameters.get('title_blacklist', []) or []
        self.positions = parameters.get('positions', [])
        self.locations = parameters.get('locations', [])
-        self.apply_once_at_company = parameters.get('applyOnceAtCompany', False)
+        self.apply_once_at_company = parameters.get('apply_once_at_company', False)
        self.base_search_url = self.get_base_search_url(parameters)
        self.seen_jobs = []
+
+        job_applicants_threshold = parameters.get('job_applicants_threshold', {})
+        self.min_applicants = job_applicants_threshold.get('min_applicants', 0)
+        self.max_applicants = job_applicants_threshold.get('max_applicants', float('inf'))
+
        resume_path = parameters.get('uploads', {}).get('resume', None)
        self.resume_path = Path(resume_path) if resume_path and Path(resume_path).exists() else None
        self.output_file_directory = Path(parameters['outputFileDirectory'])
@ -109,32 +115,80 @@ class LinkedInJobManager:
                    utils.printyellow("Applying to jobs on this page has been completed!")

                    time_left = minimum_page_time - time.time()
+
+                    # Ask user if they want to skip waiting, with timeout
                    if time_left > 0:
-                        utils.printyellow(f"Sleeping for {time_left} seconds.")
-                        logger.debug("Sleeping for %d seconds", time_left)
-                        time.sleep(time_left)
-                        minimum_page_time = time.time() + minimum_time
+                        try:
+                            user_input = inputimeout(
+                                prompt=f"Sleeping for {time_left} seconds. Press 'y' to skip waiting. Timeout 60 seconds : ",
+                                timeout=60).strip().lower()
+                        except TimeoutOccurred:
+                            user_input = ''  # No input after timeout
+                        if user_input == 'y':
+                            logger.debug("User chose to skip waiting.")
+                            utils.printyellow("User skipped waiting.")
+                        else:
+                            logger.debug(f"Sleeping for {time_left} seconds as user chose not to skip.")
+                            utils.printyellow(f"Sleeping for {time_left} seconds.")
+                            time.sleep(time_left)
+
+                    minimum_page_time = time.time() + minimum_time
+
                    if page_sleep % 5 == 0:
                        sleep_time = random.randint(5, 34)
-                        utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
-                        logger.debug("Sleeping for %d seconds", sleep_time)
-                        time.sleep(sleep_time)
+                        try:
+                            user_input = inputimeout(
+                                prompt=f"Sleeping for {sleep_time / 60} minutes. Press 'y' to skip waiting. Timeout 60 seconds : ",
+                                timeout=60).strip().lower()
+                        except TimeoutOccurred:
+                            user_input = ''  # No input after timeout
+                        if user_input == 'y':
+                            logger.debug("User chose to skip waiting.")
+                            utils.printyellow("User skipped waiting.")
+                        else:
+                            logger.debug(f"Sleeping for {sleep_time} seconds.")
+                            utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
+                            time.sleep(sleep_time)
                        page_sleep += 1
            except Exception as e:
                logger.error("Unexpected error during job search: %s", e)
                utils.printred(f"Unexpected error: {e}")
                continue
+
            time_left = minimum_page_time - time.time()
+
            if time_left > 0:
-                utils.printyellow(f"Sleeping for {time_left} seconds.")
-                logger.debug("Sleeping for %d seconds", time_left)
-                time.sleep(time_left)
-                minimum_page_time = time.time() + minimum_time
+                try:
+                    user_input = inputimeout(
+                        prompt=f"Sleeping for {time_left} seconds. Press 'y' to skip waiting. Timeout 60 seconds : ",
+                        timeout=60).strip().lower()
+                except TimeoutOccurred:
+                    user_input = ''  # No input after timeout
+                if user_input == 'y':
+                    logger.debug("User chose to skip waiting.")
+                    utils.printyellow("User skipped waiting.")
+                else:
+                    logger.debug(f"Sleeping for {time_left} seconds as user chose not to skip.")
+                    utils.printyellow(f"Sleeping for {time_left} seconds.")
+                    time.sleep(time_left)
+
+            minimum_page_time = time.time() + minimum_time
+
            if page_sleep % 5 == 0:
                sleep_time = random.randint(50, 90)
-                utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
-                logger.debug("Sleeping for %d seconds", sleep_time)
-                time.sleep(sleep_time)
+                try:
+                    user_input = inputimeout(
+                        prompt=f"Sleeping for {sleep_time / 60} minutes. Press 'y' to skip waiting: ",
+                        timeout=60).strip().lower()
+                except TimeoutOccurred:
+                    user_input = ''  # No input after timeout
+                if user_input == 'y':
+                    logger.debug("User chose to skip waiting.")
+                    utils.printyellow("User skipped waiting.")
+                else:
+                    logger.debug(f"Sleeping for {sleep_time} seconds.")
+                    utils.printyellow(f"Sleeping for {sleep_time / 60} minutes.")
+                    time.sleep(sleep_time)
                page_sleep += 1

    def get_jobs_from_page(self):
@ -183,16 +237,82 @@ class LinkedInJobManager:
            pass

        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)
+        # 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')
+
        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
+
        job_list = [Job(*self.extract_job_information_from_tile(job_element)) for job_element in job_list_elements]
+
        for job in job_list:
+
+            try:
+                logger.debug(f"Starting applicant count search for job: {job.title} at {job.company}")
+
+                # Find all job insight elements
+                job_insight_elements = self.driver.find_elements(By.CLASS_NAME,
+                                                                 "job-details-jobs-unified-top-card__job-insight")
+                logger.debug(f"Found {len(job_insight_elements)} job insight elements")
+
+                # Initialize applicants_count as None
+                applicants_count = None
+
+                # Iterate over each job insight element to find the one containing the word "applicant"
+                for element in job_insight_elements:
+                    logger.debug(f"Checking element text: {element.text}")
+                    if "applicant" in element.text.lower():
+                        # Found an element containing "applicant"
+                        applicants_text = element.text.strip()
+                        logger.debug(f"Applicants text found: {applicants_text}")
+
+                        # Extract numeric digits from the text (e.g., "70 applicants" -> "70")
+                        applicants_count = ''.join(filter(str.isdigit, applicants_text))
+                        logger.debug(f"Extracted applicants count: {applicants_count}")
+
+                        if applicants_count:
+                            if "over" in applicants_text.lower():
+                                applicants_count = int(applicants_count) + 1  # Handle "over X applicants"
+                                logger.debug(f"Applicants count adjusted for 'over': {applicants_count}")
+                            else:
+                                applicants_count = int(applicants_count)  # Convert the extracted number to an integer
+                        break
+
+                # Check if applicants_count is valid (not None) before performing comparisons
+                if applicants_count is not None:
+                    # Perform the threshold check for applicants count
+                    if applicants_count < self.min_applicants or applicants_count > self.max_applicants:
+                        utils.printyellow(
+                            f"Skipping {job.title} at {job.company} due to applicants count: {applicants_count}")
+                        logger.debug(f"Skipping {job.title} at {job.company}, applicants count: {applicants_count}")
+                        self.write_to_file(job, "skipped_due_to_applicants")
+                        continue  # Skip this job if applicants count is outside the threshold
+                    else:
+                        logger.debug(f"Applicants count {applicants_count} is within the threshold")
+                else:
+                    # If no applicants count was found, log a warning but continue the process
+                    logger.warning(
+                        f"Applicants count not found for {job.title} at {job.company}, continuing with application.")
+            except NoSuchElementException:
+                # Log a warning if the job insight elements are not found, but do not stop the job application process
+                logger.warning(
+                    f"Applicants count elements not found for {job.title} at {job.company}, continuing with application.")
+            except ValueError as e:
+                # Handle errors when parsing the applicants count
+                logger.error(f"Error parsing applicants count for {job.title} at {job.company}: {e}")
+            except Exception as e:
+                # Catch any other exceptions to ensure the process continues
+                logger.error(
+                    f"Unexpected error during applicants count processing for {job.title} at {job.company}: {e}")
+
+            # Continue with the job application process regardless of the applicants count check
+            logger.debug(f"Continuing with job application for {job.title} at {job.company}")
+
            if self.is_blacklisted(job.title, job.company, job.link):
                utils.printyellow(f"Blacklisted {job.title} at {job.company}, skipping...")
                logger.debug("Job blacklisted: %s at %s", job.title, job.company)
@ -250,7 +370,7 @@ 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
+        experience_levels = [str(i + 1) for i, (level, v) in enumerate(parameters.get('experience_level', {}).items()) if
                             v]
        if experience_levels:
            url_parts.append(f"f_E={','.join(experience_levels)}")
@ -307,10 +427,8 @@ 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

        return title_blacklisted or company_blacklisted or link_seen

@ -333,9 +451,9 @@ 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
-
--- a/src/linkedin-api.py
+++ b/src/linkedin-api.py
@ -1,13 +1,21 @@
+<<<<<<< HEAD
 from typing import Dict, List
 from linkedin_api import Linkedin
 from typing import Optional, Union, Literal
 from urllib.parse import quote, urlencode, parse_qs, urlparse
+=======
+>>>>>>> upstream/v3
 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] = []

@ -15,44 +23,44 @@ class LinkedInEvolvedAPI(Linkedin):
        super().__init__(username, password)

    def search_jobs(
-        self,
-        keywords: Optional[str] = None,
-        companies: Optional[List[str]] = None,
-        experience: Optional[
-            List[
-                Union[
-                    Literal["1"],
-                    Literal["2"],
-                    Literal["3"],
-                    Literal["4"],
-                    Literal["5"],
-                    Literal["6"],
+            self,
+            keywords: Optional[str] = None,
+            companies: Optional[List[str]] = None,
+            experience: Optional[
+                List[
+                    Union[
+                        Literal["1"],
+                        Literal["2"],
+                        Literal["3"],
+                        Literal["4"],
+                        Literal["5"],
+                        Literal["6"],
+                    ]
                ]
-            ]
-        ] = None,
-        job_type: Optional[
-            List[
-                Union[
-                    Literal["F"],
-                    Literal["C"],
-                    Literal["P"],
-                    Literal["T"],
-                    Literal["I"],
-                    Literal["V"],
-                    Literal["O"],
+            ] = None,
+            job_type: Optional[
+                List[
+                    Union[
+                        Literal["F"],
+                        Literal["C"],
+                        Literal["P"],
+                        Literal["T"],
+                        Literal["I"],
+                        Literal["V"],
+                        Literal["O"],
+                    ]
                ]
-            ]
-        ] = None,
-        job_title: Optional[List[str]] = None,
-        industries: Optional[List[str]] = None,
-        location_name: Optional[str] = None,
-        remote: Optional[List[Union[Literal["1"], Literal["2"], Literal["3"]]]] = None,
-        listed_at: None | int = None,
-        distance: Optional[int] = None,
-        easy_apply: Optional[bool] = True,
-        limit=-1,
-        offset=0,
-        **kwargs,
+            ] = None,
+            job_title: Optional[List[str]] = None,
+            industries: Optional[List[str]] = None,
+            location_name: Optional[str] = None,
+            remote: Optional[List[Union[Literal["1"], Literal["2"], Literal["3"]]]] = None,
+            listed_at: None | int = None,
+            distance: Optional[int] = None,
+            easy_apply: Optional[bool] = True,
+            limit=-1,
+            offset=0,
+            **kwargs,
    ) -> List[Dict]:
        """Perform a LinkedIn search for jobs.

@ -159,8 +167,8 @@ class LinkedInEvolvedAPI(Linkedin):
                break
            results.extend(new_data)
            if (
-                (-1 < limit <= len(results))
-                or len(results) / count >= Linkedin._MAX_REPEATED_REQUESTS
+                    (-1 < limit <= len(results))
+                    or len(results) / count >= Linkedin._MAX_REPEATED_REQUESTS
            ) or len(elements) == 0:
                break

@ -168,7 +176,7 @@ class LinkedInEvolvedAPI(Linkedin):

        return results

-    def get_fields_for_easy_apply(self,job_id: str) -> List[Dict]:
+    def get_fields_for_easy_apply(self, job_id: str) -> List[Dict]:
        """Get fields needed for easy apply jobs.

        :param job_id: Job ID
@ -182,13 +190,11 @@ 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}",
@ -253,7 +259,7 @@ class LinkedInEvolvedAPI(Linkedin):

        return form_components

-    def apply_to_job(self,job_id: str, fields: dict, followCompany: bool = True) -> bool:
+    def apply_to_job(self, job_id: str, fields: dict, followCompany: bool = True) -> bool:
        return False

        # ToDo: Implement apply to job parser first
@ -269,7 +275,7 @@ class LinkedInEvolvedAPI(Linkedin):
        # EXAMPLE OF WORKING PAYLOAD
        # 4005350454 is job_id, so need to be replaced with the job_id

-        #{
+        # {
        #    "followCompany": true,
        #    "responses": [
        #        {
@ -349,9 +355,10 @@ class LinkedInEvolvedAPI(Linkedin):
        #        }
        #    ],
        #    "trackingId": ""
-        #}
+        # }

        # Push the commit to the repository and create a pull request to the v3 branch.
+<<<<<<< HEAD
        
    def create_request_pdf(self, filename: str) -> str | None:
        """
@ -469,10 +476,13 @@ class LinkedInEvolvedAPI(Linkedin):
        with open(file_path, 'rb') as file:
            binary_data = file.read()
        return binary_data
+=======
+>>>>>>> upstream/v3

    def set_job_as_applied(self, job_id: str) -> None:
        self.already_applied_jobs.append(job_id)

+<<<<<<< HEAD
    def upload_linkedin_resume(self, cv_path: str) -> str | bool:
        url = self.create_request_pdf("resume.pdf")
        if url:
@ -491,6 +501,14 @@ 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)
+=======
+
+## 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)
+>>>>>>> upstream/v3
    for job in jobs:
        job_id: str = job["job_id"]

@ -514,7 +532,3 @@ if __name__ == "__main__":
            print(field)

        break
-
-        
-
-    
--- a/src/utils.py
+++ b/src/utils.py
@ -8,7 +8,7 @@ from selenium import webdriver
 log_file = "app_log.log"

 logging.basicConfig(
-    level=logging.DEBUG,
+    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler(log_file, mode='a', encoding='utf-8'),
@ -22,7 +22,7 @@ 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)
+logger.setLevel(logging.INFO)

 chromeProfilePath = os.path.join(os.getcwd(), "chrome_profile", "linkedin_profile")

@ -90,7 +90,13 @@ def scroll_slow(driver, scrollable_element, start=0, end=3600, step=300, reverse
                return

            position = start
+            previous_position = None  # Tracking the previous position to avoid duplicate scrolls
            while (step > 0 and position < end) or (step < 0 and position > end):
+                if position == previous_position:
+                    # Avoid re-scrolling to the same position
+                    logger.debug("Stopping scroll as position hasn't changed: %d", position)
+                    break
+
                try:
                    driver.execute_script(script_scroll_to, scrollable_element, position)
                    logger.debug("Scrolled to position: %d", position)
@ -98,11 +104,15 @@ def scroll_slow(driver, scrollable_element, start=0, end=3600, step=300, reverse
                    logger.error("Error during scrolling: %s", e)
                    print(f"Error during scrolling: {e}")

+                previous_position = position
                position += step
+
+                # Decrease the step but ensure it doesn't reverse direction
                step = max(10, abs(step) - 10) * (-1 if reverse else 1)

                time.sleep(random.uniform(0.6, 1.5))

+            # Ensure the final scroll position is correct
            driver.execute_script(script_scroll_to, scrollable_element, end)
            logger.debug("Scrolled to final position: %d", end)
            time.sleep(0.5)