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bb27315 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
#Embedding Model
embedder = SentenceTransformer("all-mpnet-base-v2")
#Summarization Model
model_name = "MBZUAI/LaMini-Flan-T5-248M"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
device = torch.device("cpu")
model.to(device)
def extract_key_sections(resume_text):
sections = {"education": [], "experience": [], "skills": [], "projects": []}
lines = resume_text.splitlines()
current = None
for line in lines:
line = line.strip()
if not line:
continue
l = line.lower()
if "education" in l:
current = "education"
elif "experience" in l or "work history" in l:
current = "experience"
elif "skills" in l:
current = "skills"
elif "projects" in l or "certifications" in l:
current = "projects"
elif current:
sections[current].append(line)
return sections
def extract_applicant_name(resume_text, filename):
# first 3 lines
lines = resume_text.strip().split("\n")[:3]
possible_name = None
for line in lines:
clean_line = line.strip()
if clean_line and 2 <= len(clean_line.split()) <= 4:
possible_name = clean_line
break
if possible_name:
return possible_name
return filename.rsplit(".", 1)[0] #fallback to filename if name not found.
def rank_resumes(job_description, resume_texts):
if not resume_texts:
return []
texts = [job_description] + [text for _, text in resume_texts]
embeddings = embedder.encode(texts)
job_embedding = embeddings[0].reshape(1, -1)
resume_embeddings = embeddings[1:]
similarities = cosine_similarity(job_embedding, resume_embeddings)[0]
results = []
for (filename, resume_text), sim in zip(resume_texts, similarities):
applicant_name = extract_applicant_name(resume_text, filename)
results.append({
"filename": filename,
"applicant_name": applicant_name,
"text": resume_text,
"score": round(float(sim), 4)
})
results.sort(key=lambda x: x["score"], reverse=True)
return results[:4]
# ===== Summarization =====
def summarize_resume_flan(resume_text, job_description):
prompt = f"""
Summarize this resume in 3 bullet points, focusing on skills and experience relevant to the job description.
Job Description:
{job_description}
Resume:
{resume_text}
"""
inputs = tokenizer(
prompt,
return_tensors="pt",
truncation=True,
max_length=512
).to(device)
outputs = model.generate(
**inputs,
max_length=200,
num_beams=4,
early_stopping=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
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