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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): # Looping for 50 applicants
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 # Reset analysis flag
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)
# Encode the job description and resume text separately
job_embedding = model.encode([job_desc])
resume_embedding = model.encode([text_data])
# Calculate the cosine similarity between the two embeddings
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).")
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