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import os
from data import load_text
from generate_summary_and_embedding import generate_summary_and_embedding
from cal_sim import compute_similarity
def run_single_similarity(jd_path, cv_path, SECTION_WEIGHTS, model_id="sentence-transformers/all-MiniLM-L6-v2", llm_model="meta-llama/Meta-Llama-3-8B-Instruct", pipe=None):
jd_text = load_text(jd_path)
cv_text = load_text(cv_path)
jd_embeddings, _ = generate_summary_and_embedding(jd_text, text_type="job_description", model_id=model_id, llm_model=llm_model, pipe=pipe)
cv_embeddings, _ = generate_summary_and_embedding(cv_text, text_type="resume", model_id=model_id, llm_model=llm_model, pipe=pipe)
scores = compute_similarity(jd_embeddings, cv_embeddings)
weighted_scores = sum(scores[k] * SECTION_WEIGHTS.get(k, 0) for k in scores)
return round(float(weighted_scores), 4), scores
def run_batch_similarity(jd_path, cv_input, SECTION_WEIGHTS, model_id="sentence-transformers/all-MiniLM-L6-v2", llm_model="meta-llama/Meta-Llama-3-8B-Instruct", pipe=None):
jd_text = load_text(jd_path)
jd_embeddings, _ = generate_summary_and_embedding(jd_text, text_type="job_description", model_id=model_id, llm_model=llm_model, pipe=pipe)
if isinstance(cv_input, str):
cv_paths = [
os.path.join(cv_input, f)
for f in os.listdir(cv_input)
if os.path.isfile(os.path.join(cv_input, f)) and f.lower().endswith(('.pdf', '.txt'))
]
elif isinstance(cv_input, list):
cv_paths = cv_input
else:
raise ValueError("cv_input must be path directory (str) or list of file paths (list[str])")
results = []
for full_cv_path in cv_paths:
filename = os.path.basename(full_cv_path)
try:
cv_text = load_text(full_cv_path)
cv_embeddings, _ = generate_summary_and_embedding(cv_text, text_type="resume", model_id=model_id, llm_model=llm_model, pipe=pipe)
scores = compute_similarity(jd_embeddings, cv_embeddings)
weighted_scores = sum(scores[k] * SECTION_WEIGHTS.get(k, 0) for k in scores)
result = {
"JD": os.path.basename(jd_path),
"Resume": filename,
"Similarity Score": round(float(weighted_scores), 4)
}
for sec, val in scores.items():
result[f"{sec} Score"] = round(val, 4)
results.append(result)
except Exception as e:
print(f"❌ Failed to process {filename}: {e}")
return results
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