|
|
from sentence_transformers import SentenceTransformer |
|
|
import streamlit as st |
|
|
import pandas as pd |
|
|
from PyPDF2 import PdfReader |
|
|
import nltk |
|
|
nltk.download('punkt') |
|
|
from nltk.corpus import stopwords |
|
|
nltk.download('stopwords') |
|
|
from nltk.tokenize import word_tokenize |
|
|
|
|
|
model = SentenceTransformer("all-mpnet-base-v2") |
|
|
st.title("AI Resume Analysis based on Keywords App") |
|
|
st.divider() |
|
|
job_desc = st.text_area("Paste the job description and then press Ctrl + Enter", key="job_desc") |
|
|
text_tokens = [] |
|
|
for sentence in job_desc: |
|
|
text_tokens.extend(word_tokenize(job_desc)) |
|
|
job_desc = [word for word in text_tokens if not word in stopwords.words()] |
|
|
st.write(job_desc) |
|
|
|
|
|
if 'applicant_data' not in st.session_state: |
|
|
st.session_state['applicant_data'] = {} |
|
|
max_attempts = 1 |
|
|
for i in range(1, 51): |
|
|
st.subheader(f"Applicant {i} Resume", divider="green") |
|
|
applicant_key = f"applicant_{i}" |
|
|
upload_key = f"candidate_{i}" |
|
|
if applicant_key not in st.session_state['applicant_data']: |
|
|
st.session_state['applicant_data'][applicant_key] = {'upload_count': 0, 'uploaded_file': None, 'analysis_done': False} |
|
|
if st.session_state['applicant_data'][applicant_key]['upload_count'] < max_attempts: |
|
|
uploaded_file = st.file_uploader(f"Upload Applicant's {i} resume", type="pdf", key=upload_key) |
|
|
if uploaded_file: |
|
|
st.session_state['applicant_data'][applicant_key]['uploaded_file'] = uploaded_file |
|
|
st.session_state['applicant_data'][applicant_key]['upload_count'] += 1 |
|
|
st.session_state['applicant_data'][applicant_key]['analysis_done'] = False |
|
|
if st.session_state['applicant_data'][applicant_key]['uploaded_file'] and not st.session_state['applicant_data'][applicant_key]['analysis_done']: |
|
|
try: |
|
|
pdf_reader = PdfReader(st.session_state['applicant_data'][applicant_key]['uploaded_file']) |
|
|
text_data = "" |
|
|
for page in pdf_reader.pages: |
|
|
text_data += page.extract_text() |
|
|
with st.expander(f"See Applicant's {i} resume"): |
|
|
text_tokens = [] |
|
|
for sentence in text_data: |
|
|
text_tokens.extend(word_tokenize(text_data)) |
|
|
text_data = [word for word in text_tokens if not word in stopwords.words()] |
|
|
st.write(text_data) |
|
|
|
|
|
|
|
|
|
|
|
job_embedding = model.encode([job_desc]) |
|
|
resume_embedding = model.encode([text_data]) |
|
|
|
|
|
|
|
|
similarity_score = model.similarity(job_embedding, resume_embedding)[0][0] |
|
|
|
|
|
with st.popover(f"See Result for Applicant {i}"): |
|
|
st.write(f"Similarity between Applicant's resume and job description based on keywords: {similarity_score:.2f}") |
|
|
st.info( |
|
|
f"A score closer to 1 (0.80, 0.90) means higher similarity between Applicant's {i} resume and job description. A score closer to 0 (0.20, 0.30) means lower similarity between Applicant's {i} resume and job description.") |
|
|
st.session_state['applicant_data'][applicant_key]['analysis_done'] = True |
|
|
except Exception as e: |
|
|
st.error(f"An error occurred while processing Applicant {i}'s resume: {e}") |
|
|
else: |
|
|
st.warning(f"Maximum upload attempts has been reached ({max_attempts}).") |
|
|
if st.session_state['applicant_data'][applicant_key]['upload_count'] > 0: |
|
|
st.info(f"Files uploaded for Applicant {i}: {st.session_state['applicant_data'][applicant_key]['upload_count']} time(s).") |
|
|
|
|
|
|
|
|
|
