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import argparse
from data import load_text
from utils import save_result, list_ranked_results, load_weights_from_file
from model import load_model
from explanation import result_explanation
from inference import run_single_similarity, run_batch_similarity
DEFAULT_SECTION_WEIGHTS = {
"Experience & Achievements": 0.2,
"Skills & Competencies": 0.2,
"Culture Fit": 0.2,
"Personal Traits": 0.2,
"Education": 0.2
}
def parse_arguments():
parser = argparse.ArgumentParser(description="Resume vs Job Description Similarity Calculator")
# Basic parameter
parser.add_argument("--jd", required=True, help="Job Description file (PDF or TXT)")
# Resume (single or batch)
parser.add_argument("--cv", help="Resume file (PDF or TXT)")
parser.add_argument("--cv_folder", help="Folder containing multiple resume files (PDF/TXT)")
# Text chunking & embedding model setting
parser.add_argument("--model_id", type=str, default="sentence-transformers/all-MiniLM-L6-v2", help="Model ID for embedding")
parser.add_argument("--llm_model", type=str, default="meta-llama/Meta-Llama-3-8B-Instruct", help="LLM model path or ID")
# Record & Search history and comparison results
parser.add_argument("--records", type=str, help="The records of previous matching results")
parser.add_argument("--list", action="store_true", help="List and view similarity results")
parser.add_argument("--filter_jd", type=str, help="Filter results by specific JD filename")
parser.add_argument("--sort", action="store_true", help="Sort results by similarity score")
parser.add_argument("--top_k", type=int, help="Only show top K results")
# score weights
parser.add_argument("--weight_config", type=str, help="YAML file with custom section weights")
return parser.parse_args()
def main():
args = parse_arguments()
llm_pipe = load_model(model_path=args.llm_model)
if args.weight_config:
SECTION_WEIGHTS = load_weights_from_file(args.weight_config)
else:
SECTION_WEIGHTS = DEFAULT_SECTION_WEIGHTS
RESULTS_FILE = args.records if args.records else 'results.csv'
if args.list:
list_ranked_results(
jd_name=args.filter_jd,
result_file=RESULTS_FILE,
sort=args.sort,
top_k=args.top_k
)
return
if args.cv_folder:
print("Running in batch mode...")
results = run_batch_similarity(
jd_path=args.jd,
cv_input=args.cv_folder,
SECTION_WEIGHTS=SECTION_WEIGHTS,
model_id=args.model_id,
llm_model=args.llm_model,
pipe=llm_pipe
)
for r in results:
print(f"{r['Resume']} - Score: {r['Similarity Score']}")
section_scores = {k.replace(" Score", ""): r[k] for k in r if k.endswith(" Score")}
save_result(r['JD'], r['Resume'], r['Similarity Score'], section_scores, RESULTS_FILE)
elif args.cv:
print("Running in single resume mode...")
final_score, section_scores = run_single_similarity(
jd_path=args.jd,
cv_path=args.cv,
SECTION_WEIGHTS=SECTION_WEIGHTS,
model_id=args.model_id,
llm_model=args.llm_model,
pipe=llm_pipe
)
print(final_score)
print(f"Final Similarity Score: {final_score}")
for sec, val in section_scores.items():
print(f" - {sec}: {round(val, 4)}")
save_result(args.jd, args.cv, final_score, section_scores, RESULTS_FILE)
jd_text = load_text(args.jd)
cv_text = load_text(args.cv)
explanation_prompt = result_explanation(cv_text, jd_text)
output = llm_pipe(explanation_prompt, max_new_tokens=1024)[0]["generated_text"][-1]["content"]
print("\n Explanation:\n" + output.strip())
else:
print("Please provide either --cv or --cv_folder.")
if __name__ == "__main__":
main()
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