# Create additional __init__.py files and package the project as ZIP # Create __init__.py files init_files = [ 'scrapers/__init__.py', 'automation/__init__.py', 'ai/__init__.py', 'database/__init__.py', 'config/__init__.py', 'utils/__init__.py' ] for init_file in init_files: files_content[init_file] = '"""Package initialization"""' # Add additional utility files files_content['config/qa_bank.py'] = """\"\"\"Pre-configured question-answer bank\"\"\" QA_BANK = { # Availability questions "when can you start": "I can start within my notice period of 30 days.", "notice period": "30 days", "immediate joiner": "No, I have a 30-day notice period.", # Relocation questions "willing to relocate": "Yes, I am willing to relocate for the right opportunity.", "relocate": "Yes", # Salary questions "current salary": "Please refer to my expected salary range.", "expected salary": "As per market standards for my experience level.", "salary expectations": "I'm looking for a competitive package commensurate with my skills and experience.", # Work authorization "work authorization": "Yes, I am authorized to work in India.", "citizenship": "Indian", # General questions "why this role": "This role aligns perfectly with my career goals and expertise.", "why this company": "I'm impressed by the company's innovation and growth trajectory.", "strengths": "Strong technical skills, problem-solving ability, and team collaboration.", "weakness": "I'm always looking to improve my skills and take on new challenges.", # Technical questions "years of experience": "5+ years", "programming languages": "Python, Java, JavaScript, Go", "frameworks": "Django, Flask, React, Node.js", "databases": "PostgreSQL, MySQL, MongoDB, Redis", "cloud platforms": "AWS, Azure, GCP", # Preference questions "preferred work mode": "Open to Remote, Hybrid, or On-site", "shift preference": "Day shift preferred, but flexible", "travel": "Yes, willing to travel as needed" } def get_answer(question): \"\"\"Get answer for a question\"\"\" question_lower = question.lower() for key, answer in QA_BANK.items(): if key in question_lower: return answer # Default answers based on question type if "?" in question: if "yes" in question_lower or "willing" in question_lower: return "Yes" elif "experience" in question_lower: return "5+ years" return "Please refer to my resume for detailed information." """ files_content['automation/login_handler.py'] = """\"\"\"Login handler for different platforms\"\"\" from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from utils.logger import get_logger import time class LoginHandler: \"\"\"Handles login for various job platforms\"\"\" def __init__(self, driver, settings): self.driver = driver self.settings = settings self.logger = get_logger(__name__) def login_linkedin(self): \"\"\"Login to LinkedIn\"\"\" try: self.driver.get("https://www.linkedin.com/login") time.sleep(2) email_field = self.driver.find_element(By.ID, "username") email_field.send_keys(self.settings.linkedin_email) password_field = self.driver.find_element(By.ID, "password") password_field.send_keys(self.settings.linkedin_password) login_button = self.driver.find_element(By.XPATH, "//button[@type='submit']") login_button.click() time.sleep(5) self.logger.info("LinkedIn login successful") return True except Exception as e: self.logger.error(f"LinkedIn login failed: {str(e)}") return False def login_naukri(self): \"\"\"Login to Naukri\"\"\" try: self.driver.get("https://www.naukri.com/nlogin/login") time.sleep(2) email_field = self.driver.find_element(By.ID, "usernameField") email_field.send_keys(self.settings.naukri_email) password_field = self.driver.find_element(By.ID, "passwordField") password_field.send_keys(self.settings.naukri_password) login_button = self.driver.find_element(By.XPATH, "//button[@type='submit']") login_button.click() time.sleep(5) self.logger.info("Naukri login successful") return True except Exception as e: self.logger.error(f"Naukri login failed: {str(e)}") return False """ files_content['ai/resume_tailor.py'] = """\"\"\"Resume tailoring based on job description\"\"\" from utils.logger import get_logger class ResumeTailor: \"\"\"Tailor resume based on job requirements\"\"\" def __init__(self, settings): self.settings = settings self.logger = get_logger(__name__) def extract_keywords(self, job_description): \"\"\"Extract key skills from job description\"\"\" # Simple keyword extraction keywords = [] common_skills = [ 'python', 'java', 'javascript', 'kubernetes', 'docker', 'aws', 'azure', 'gcp', 'devops', 'ci/cd', 'terraform', 'ansible', 'jenkins', 'git', 'linux', 'microservices' ] jd_lower = job_description.lower() for skill in common_skills: if skill in jd_lower: keywords.append(skill) return keywords def match_score(self, job_description): \"\"\"Calculate match score between profile and job\"\"\" job_keywords = set(self.extract_keywords(job_description)) user_skills = set([skill.lower() for skill in self.settings.skills]) if not job_keywords: return 0.5 # Default score if no keywords found matching = job_keywords.intersection(user_skills) score = len(matching) / len(job_keywords) return round(score, 2) """ files_content['database/models.py'] = """\"\"\"Data models for database\"\"\" from dataclasses import dataclass from datetime import datetime from typing import Optional @dataclass class Job: \"\"\"Job data model\"\"\" title: str company: str location: str url: str platform: str scraped_at: Optional[datetime] = None def to_dict(self): return { 'title': self.title, 'company': self.company, 'location': self.location, 'url': self.url, 'platform': self.platform } @dataclass class Application: \"\"\"Application data model\"\"\" job_url: str status: str = 'applied' applied_at: Optional[datetime] = None notes: str = '' def to_dict(self): return { 'job_url': self.job_url, 'status': self.status, 'notes': self.notes } """ print(f"\\nFinal file count: {len(files_content)}") print("\\nFiles created:") for filename in sorted(files_content.keys()): print(f" - {filename}")