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import re
import PyPDF2
import gradio as gr
from transformers import pipeline
from collections import Counter
# Load the Hugging Face NER pipeline
ner_pipeline = pipeline("ner", model="dslim/bert-base-NER", tokenizer="dslim/bert-base-NER", aggregation_strategy="simple")
def clean_resume_text(text):
"""Clean resume text by removing unwanted characters and formatting."""
text = re.sub(r'http\S+', ' ', text)
text = re.sub(r'#\S+', '', text)
text = re.sub(r'@\S+', ' ', text)
text = re.sub(r'[^\w\s]', ' ', text)
text = re.sub(r'[^\x00-\x7f]', ' ', text)
return re.sub(r'\s+', ' ', text).strip()
def extract_resume_text(file):
"""Extract raw text from uploaded PDF file."""
try:
reader = PyPDF2.PdfReader(file)
text = ""
for page in reader.pages:
page_text = page.extract_text()
if page_text:
text += page_text + " "
if not text.strip():
return None, "Error: No text extracted from PDF."
return text, None
except Exception as e:
return None, f"Error reading PDF: {str(e)}"
def classify_resume(entities):
"""Classify resume based on dominant entity types."""
orgs = [e['word'] for e in entities if e['entity_group'] == 'ORG']
locs = [e['word'] for e in entities if e['entity_group'] == 'LOC']
jobs = [e['word'] for e in entities if e['entity_group'] == 'MISC']
dominant_org = Counter(orgs).most_common(1)
dominant_loc = Counter(locs).most_common(1)
dominant_job = Counter(jobs).most_common(1)
return {
"Main_Organization": dominant_org[0][0] if dominant_org else "Unknown",
"Main_Location": dominant_loc[0][0] if dominant_loc else "Unknown",
"Possible_Job/Field": dominant_job[0][0] if dominant_job else "General"
}
def extract_entities_from_pdfs(files):
"""Process multiple resumes, extract entities, and classify."""
summary = {}
for file in files:
file_name = file.name.split("/")[-1]
resume_text, error = extract_resume_text(file)
if error:
summary[file_name] = {"error": error}
continue
cleaned_text = clean_resume_text(resume_text)
entities = ner_pipeline(cleaned_text)
result = {
"Persons": list({e["word"] for e in entities if e["entity_group"] == "PER"}),
"Organizations": list({e["word"] for e in entities if e["entity_group"] == "ORG"}),
"Locations": list({e["word"] for e in entities if e["entity_group"] == "LOC"}),
"Other": list({e["word"] for e in entities if e["entity_group"] not in ["PER", "ORG", "LOC"]}),
"Cleaned_Text": cleaned_text,
"Classification": classify_resume(entities)
}
summary[file_name] = result
return summary
# Gradio UI
iface = gr.Interface(
fn=extract_entities_from_pdfs,
inputs=gr.File(file_types=[".pdf"], label="Upload Resumes (PDF)", file_count="multiple"),
outputs=gr.JSON(label="Resume Classification & Entity Summary"),
title="📂 Multi-Resume Entity Extractor & Classifier",
description="Upload multiple PDF resumes. This tool extracts text, identifies key entities, and classifies each resume by organizations, locations, and possible job/field."
)
if __name__ == "__main__":
iface.launch()
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