main.py

from helper.job_parser import extract_job_description
from helper.resume_parser import parse_resume_sections
from helper.llm_interface import update_resume_with_llm, run_final_coherence_check
from helper.pdf_exporter import convert_md_to_pdf

import argparse
from pathlib import Path


def main():
    parser = argparse.ArgumentParser(description="Tailor your resume to a specific job description.")
    parser.add_argument('--job', required=True, help='Job description URL or path to a text file')
    parser.add_argument('--resume', required=True, help='Path to the Markdown resume file')
    parser.add_argument('--company', required=True, help='Company name to tag the output resume')

    args = parser.parse_args()

    # Step 1: Extract job description
    job_description = extract_job_description(args.job)

    # Step 2: Parse resume into sections
    sections = parse_resume_sections(args.resume)

    # Step 3: Update each section using the LLM
    updated_sections = update_resume_with_llm(sections, job_description)

    # Step 4: Combine sections and run final polish
    updated_resume = "\n\n".join(updated_sections)
    final_resume = run_final_coherence_check(updated_resume, job_description)

    # Step 5: Save to Markdown file
    output_md_path = f"{Path(args.resume).stem}_{args.company}.md"
    with open(output_md_path, "w", encoding='utf-8') as f:
        f.write(final_resume)

    # Step 6: Convert to PDF
    convert_md_to_pdf(output_md_path)

    print(f"\n✅ Resume tailored and saved as: {output_md_path}")
    print(f"📄 PDF generated as: {Path(output_md_path).stem}.pdf")


if __name__ == "__main__":
    main()