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import json
from pathlib import Path
from typing import Dict
from pdfminer.high_level import extract_text as pdf_extract_text
from docx import Document
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
# --------------------
# Load PyTorch Resume NER Model
# --------------------
MODEL_NAME = "manishiitg/resume-ner"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME)
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
# --------------------
# Extract Text from PDF/DOCX
# --------------------
def extract_text(file_path: str) -> str:
path = Path(file_path)
if path.suffix.lower() == ".pdf":
return pdf_extract_text(file_path)
elif path.suffix.lower() == ".docx":
doc = Document(file_path)
return "\n".join([p.text for p in doc.paragraphs])
else:
raise ValueError("Unsupported file format")
# --------------------
# Parse Resume
# --------------------
def parse_resume(file_path: str) -> Dict[str, str]:
text = extract_text(file_path)
entities = ner_pipeline(text)
name = []
skills = []
education = []
experience = []
for ent in entities:
label = ent["entity_group"].upper()
value = ent["word"].strip()
if label == "NAME":
name.append(value)
elif label == "SKILL":
skills.append(value)
elif label in ["EDUCATION", "DEGREE"]:
education.append(value)
elif label in ["EXPERIENCE", "JOB", "ROLE", "POSITION"]:
experience.append(value)
return {
"name": " ".join(dict.fromkeys(name)) or "Not Found",
"skills": ", ".join(dict.fromkeys(skills)) or "Not Found",
"education": ", ".join(dict.fromkeys(education)) or "Not Found",
"experience": ", ".join(dict.fromkeys(experience)) or "Not Found"
}
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