import re
import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

class JobTitlePreprocessor():
    """Preprocesses job titles by converting to lowercase, removing unwanted words, special characters, numbers greater than 10, and content from location, states, regions, etc."""

    def __init__(self):
        self.unwanted_words = ['remote', 'hybrid', 'flexible location', 'location', 'open to work', 'role', 'job', 'level', 'remot']

    def remove_location_unwanted_words_brackets(self, title: str) -> str:
        """Removes parts of the title based on unwanted words, bracketed content, numbers greater than 10, and also removes symbols other than alphanumeric."""
        # Remove unwanted words
        for word in self.unwanted_words:
            pattern = r'\b{}\b'.format(re.escape(word))
            title = re.sub(pattern, '', title, flags=re.IGNORECASE)

        # Remove content within brackets
        title = re.sub(r'\[.*?\]|\(.*?\)|\{.*?\}', '', title)

        # Remove any non-alphanumeric characters (keeping spaces)
        title = re.sub(r'[^a-zA-Z0-9\s]', '', title)

        # Remove numbers greater than 10
        title = re.sub(r'\b(?:[1-9][0-9]+|1[1-9]|[2-9][0-9])\b', '', title)

        # Clean up extra spaces
        title = re.sub(r'\s+', ' ', title).strip()

        return title

    def preprocess(self, title: str) -> str:
        """Converts title to lowercase, removes unwanted words, replaces specific terms, and standardizes job titles."""
        if not isinstance(title, str):
            return title
        
        # Convert to lowercase
        title = title.lower()
        
        # Remove unwanted words
        for word in self.unwanted_words:
            title = re.sub(r'\b{}\b'.format(re.escape(word)), '', title, flags=re.IGNORECASE)
        
        # Replace specific terms and Roman numerals
        replacements = [
            (r'\b(?:SR|sr|Sr\.?|SR\.?|Senior|senior)\b', 'Senior'),
            (r'\b(?:JR|jr|Jr\.?|JR\.?|Junior|junior)\b', 'Junior'),
            (r'\b(?:AIML|aiml|ML|ml|MachineLearning|machinelearning|machine[_\-]learning)\b', 'Machine Learning'),
            (r'\b(?:GenAI|genai|Genai|generative[_\-]ai|GenerativeAI|generativeai)\b', 'Generative AI'),
            (r'\b(?:NLP|nlp|natural[_\-]language[_\-]processing|natural language processing)\b', 'NLP'),
            (r'\b(?:i|I)\b', '1'),
            (r'\b(?:ii|II)\b', '2'),
            (r'\b(?:iii|III)\b', '3'),
            (r'\b(?:iv|IV)\b', '4'),
            (r'\b(?:v|V)\b', '5')
        ]
        
        for pattern, replacement in replacements:
            title = re.sub(pattern, replacement, title, flags=re.IGNORECASE)
        
        # Handle specific Data Scientist cases
        title = re.sub(r'\b(director|dir\.?|dir)\b.*?(data\s*scientist|data\s*science)', 'Director Data Scientist', title, flags=re.IGNORECASE)
        title = re.sub(r'\b(manager|mgr)\b.*?(data\s*scientist|data\s*science)', 'Manager Data Scientist', title, flags=re.IGNORECASE)
        title = re.sub(r'\b(lead)\b.*?(data\s*scientist|data\s*science)', 'Lead Data Scientist', title, flags=re.IGNORECASE)
        title = re.sub(r'\b(associate|associates?)\b.*?(data\s*scientist|data\s*science)', 'Associate Data Scientist', title, flags=re.IGNORECASE)
        title = re.sub(r'\b(applied)\b.*?(data\s*scientist|data\s*science)', 'Applied Data Scientist', title, flags=re.IGNORECASE)
        title = re.sub(r'\b(intern|internship|trainee)\b.*?(data\s*scientist|data\s*science)', 'Intern Data Scientist', title, flags=re.IGNORECASE)

        # Clean up extra spaces
        title = re.sub(r'\s+', ' ', title).strip()

        return title


def preprocess_single_title(title: str) -> str:
    preprocessor = JobTitlePreprocessor()
    clean_title = preprocessor.remove_location_unwanted_words_brackets(title)
    clean_title = preprocessor.preprocess(clean_title)
    return clean_title


if __name__ == "__main__":
    # Example single title
    title = "Senior Remote Machine Learning Data Scientist (Manager)"
    clean_title = preprocess_single_title(title)
    logger.info(f"Original title: {title}")
    logger.info(f"Preprocessed title: {clean_title}")