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# import packages | |
import streamlit as st | |
import os | |
from utils1 import load_file, plot_similarity_scores | |
from constants import StreamlitException | |
from nlp import ( | |
clean_text, split_text, summarize_text, | |
extract_person_names_and_email, extract_tech_skills, | |
calculate_similarity, qna_query, lang_model | |
) | |
from constants import API_TOKEN, VESRION | |
os.environ["HUGGINGFACEHUB_API_TOKEN"] = API_TOKEN | |
def process_exception(e): | |
st.error(e.message) | |
st.stop() | |
if __name__ == "__main__": | |
# Set page width to a larger size | |
st.set_page_config(layout="wide") | |
# Streamlit app UI | |
st.write( | |
"""<h1 style='display: inline-block; color: black;'>ResuMate</h1> | |
<h3 style='display: inline-block; color: grey'>π Transforming the recruitment and staffing experience through Generative AI </h3>""", | |
unsafe_allow_html=True | |
) | |
st.write("") | |
st.write("") | |
st.sidebar.write("") | |
with st.sidebar.expander("π€ About", expanded=False): | |
st.write("This app is powered by free and open-source **Langchain** and **LLM technology**.") | |
st.write("Developed by **[Chandramauli Chaudhuri](https://www.linkedin.com/in/chandramaulic/)**.") | |
st.write("") | |
st.write(f"Version **{VESRION}**.") | |
# Upload a file, share job description and summarize its content | |
#st.sidebar.write("") | |
st.sidebar.header("User Inputs") | |
#st.sidebar.write("") | |
job_description_raw = st.sidebar.text_area("Enter the job description:") | |
#st.sidebar.write("") | |
job_description = clean_text(job_description_raw) | |
#st.write("") | |
uploaded_file = st.sidebar.file_uploader("Upload a resume:", type=["docx", "pdf", "ppt", "pptx"]) | |
#st.sidebar.write("") | |
# Spinner set-up | |
with st.spinner("Details loading, please wait.."): | |
if uploaded_file is not None: | |
load_file_result = load_file(st, uploaded_file) | |
if type(load_file_result) is StreamlitException: | |
process_exception(load_file_result) | |
else: | |
resume_text_raw, lang_loader = load_file_result | |
resume_text = clean_text(resume_text_raw) | |
doc = lang_model(resume_text) | |
st.subheader("π Overview") | |
st.write("") | |
# Set up candidate name & email extraction | |
person_names, emails = extract_person_names_and_email(resume_text) | |
st.write("**Candidate's name:** " + ", ".join(person_names)) | |
st.write("") | |
st.write("**Candidate's email address:** " + ", ".join(emails)) | |
st.write("") | |
# Set up job description summarization | |
summarization_result = summarize_text(job_description) | |
if type(summarization_result) is StreamlitException: | |
process_exception(summarization_result) | |
else: | |
st.write("**Job description summary:** " + summarization_result) | |
st.write("") | |
# Set up resume summarization | |
summarization_result = summarize_text(resume_text) | |
if type(summarization_result) is StreamlitException: | |
process_exception(summarization_result) | |
else: | |
st.write("**Candidate's resume summary:** " + summarization_result) | |
st.write("") | |
st.write("") | |
st.subheader("π Fitment") | |
st.write("") | |
# Set up technical skill extraction | |
st.write("**Candidate's key technical skills:** " + ", ".join(extract_tech_skills(doc))) | |
st.write("") | |
# Set up percentage match calculation | |
st.write("**Percentage match between job description and candidate's resume:** " + f"{calculate_similarity(job_description, resume_text):.2f}%" + "\n") | |
st.write("") | |
# Set up percentage match calculation at sentence level | |
job_description_phrases = split_text(job_description) | |
resume_phrases = split_text(resume_text) | |
st.write('**Percentage resume match against TOP 10 job description items:**') | |
if job_description_raw != '': | |
fig = plot_similarity_scores(job_description_phrases, resume_phrases) | |
st.plotly_chart(fig, use_container_width=True) | |
# Set up user Q&A | |
user_input = st.sidebar.text_input("Ask any other resume-related questions:", "") | |
if user_input: | |
answer = qna_query(lang_loader, user_input) | |
st.sidebar.write(answer) |