[Feature] JD-Augmented Scoring and Multi-Candidate Batch Processing

[0ameyasr]

Implementation Details

Usage

1. JD-Augmented 1-to-1 Scoring

Enables users to provide both a resume and a job description for detailed fit analysis:

python score.py /path/to/resume.pdf /path/to/jd.pdf

2. Multi-Candidate Batch Processing and Shortlisting

Accepts a directory of resumes, a single job description, and a cutoff score threshold. Processes all resumes, ranks them, and outputs a shortlist of candidates meeting the minimum score:

python score.py /path/to/resume_directory /path/to/jd.pdf <cutoff_score_int>

Core Module Changes

models.py

• Added ScoresWithJD and EvaluationDataWithJD models to structure enriched evaluation (with JD) output

template_manager.py

• Introduced jd_system_message and jd_evaluation_criteria prompts for fit analysis

evaluator.py

• Implemented _load_evaluation_prompt_with_jd for loading the final, decorated JD-based evaluation prompt
• Added evaluate_resume_with_jd to handle the JD-based evaluation flow

transform.py

• Created transform_evaluation_response_with_jd to parse LLM output into CSV rows (to be saved)

score.py

• Significantly refactored the driver script to support new CLI argument patterns
• Added _jd_context function for job description parsing from PDF to text
• Implemented evaluate_resume_with_jd as the core 1-to-1 scoring function
• Created dir_main function to orchestrate batch processing and ranking workflow in the directory
• Added print_evaluation_results_with_jd for clean, user-facing output formatting like the base evaluation output
• Sanity checks have been added for all CLI usage arguments

Output (1-to-1):

Output (N-to-1) (snippet)

If this PR is accepted, it will close #166.