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from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
import subprocess, zipfile, re, os
# === Load pretrained HF model ===
MODEL_NAME = "sravya-abburi/ResumeParserBERT" # or "Kiet/autotrain-resume_parser-1159242747"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForTokenClassification.from_pretrained(MODEL_NAME)
# Use CPU for stability (device=-1) to avoid GPU memory issues from other parts of the app
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple", device=-1)
# === Extract text from PDF/DOCX ===
def extract_text(file_path: str) -> str:
"""Extract text from PDF or DOCX resumes."""
if file_path.lower().endswith(".pdf"):
result = subprocess.run(
["pdftotext", "-layout", file_path, "-"],
stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False
)
return result.stdout.decode("utf-8", errors="ignore")
elif file_path.lower().endswith(".docx"):
with zipfile.ZipFile(file_path) as zf:
with zf.open("word/document.xml") as docx_xml:
xml_text = docx_xml.read().decode("utf-8", errors="ignore")
xml_text = re.sub(r"<w:p[^>]*>", "\n", xml_text, flags=re.I)
return re.sub(r"<[^>]+>", " ", xml_text)
return ""
# === Parse resume with NER ===
def parse_resume(file_path: str, filename: str = None) -> dict:
"""Parse resume and extract Name, Skills, Education, Experience."""
text = extract_text(file_path)
entities = ner_pipeline(text)
name, skills, education, experience = [], [], [], []
for ent in entities:
word = ent["word"].strip()
label = ent["entity_group"].upper()
# Skip empty or placeholder tokens
if not word or word.startswith("LABEL_"):
continue
if label == "NAME":
name.append(word)
elif label == "SKILL":
skills.append(word)
elif label in ["EDUCATION", "DEGREE"]:
education.append(word)
elif label in ["EXPERIENCE", "JOB", "ROLE"]:
experience.append(word)
return {
"name": " ".join(dict.fromkeys(name)),
"skills": ", ".join(dict.fromkeys(skills)),
"education": ", ".join(dict.fromkeys(education)),
"experience": ", ".join(dict.fromkeys(experience))
}
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