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from __future__ import annotations
import os
import re
import subprocess
import zipfile
from typing import List
from transformers import pipeline
# Load the NER model for resume parsing
ner = pipeline("ner", model="Kiet/ResumeParserBERT", aggregation_strategy="simple")
def extract_text(file_path: str) -> str:
"""Extract text from PDF or DOCX."""
if not file_path or not os.path.isfile(file_path):
return ""
lower_name = file_path.lower()
try:
if lower_name.endswith('.pdf'):
try:
result = subprocess.run(
['pdftotext', '-layout', file_path, '-'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
check=False
)
return result.stdout.decode('utf-8', errors='ignore')
except Exception:
return ""
elif lower_name.endswith('.docx'):
try:
with zipfile.ZipFile(file_path) as zf:
with zf.open('word/document.xml') as docx_xml:
xml_bytes = docx_xml.read()
xml_text = xml_bytes.decode('utf-8', errors='ignore')
xml_text = re.sub(r'<w:p[^>]*>', '\n', xml_text, flags=re.I)
text = re.sub(r'<[^>]+>', ' ', xml_text)
text = re.sub(r'\s+', ' ', text)
return text
except Exception:
return ""
else:
return ""
except Exception:
return ""
def extract_name(text: str, filename: str) -> str:
"""Extract candidate's name from text or filename."""
if text:
lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
for line in lines[:10]:
if re.match(r'(?i)resume|curriculum vitae', line):
continue
words = line.split()
if 1 < len(words) <= 4:
if all(re.match(r'^[A-ZÀ-ÖØ-Þ][\w\-]*', w) for w in words):
return line
base = os.path.basename(filename)
base = re.sub(r'\.(pdf|docx|doc)$', '', base, flags=re.I)
base = re.sub(r'[\._-]+', ' ', base)
base = re.sub(r'(?i)\b(cv|resume)\b', '', base)
base = re.sub(r'\s+', ' ', base).strip()
return base.title() if base else ''
def extract_entities(text: str) -> dict:
"""Extract structured info using NER model."""
entities = ner(text)
skills, education, experience = [], [], []
for ent in entities:
label = ent['entity_group'].upper()
word = ent['word'].strip()
if label in ["SKILL", "TECH", "TECHNOLOGY"]:
skills.append(word)
elif label in ["EDUCATION", "DEGREE", "QUALIFICATION"]:
education.append(word)
elif label in ["EXPERIENCE", "JOB", "ROLE"]:
experience.append(word)
return {
"skills": list(dict.fromkeys(skills)),
"education": list(dict.fromkeys(education)),
"experience": list(dict.fromkeys(experience))
}
def parse_resume(file_path: str, filename: str) -> dict:
"""Main function to parse resume fields."""
text = extract_text(file_path)
name = extract_name(text, filename)
ents = extract_entities(text)
return {
'name': name or '',
'skills': ', '.join(ents["skills"]) if ents["skills"] else '',
'education': ', '.join(ents["education"]) if ents["education"] else '',
'experience': ', '.join(ents["experience"]) if ents["experience"] else ''
}
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