app.py

from sentence_transformers import SentenceTransformer
import streamlit as st
import pandas as pd
from PyPDF2 import PdfReader

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")

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"):
                    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).")