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| # import packages | |
| import streamlit as st | |
| import os | |
| from utils 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'>🚀 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("🤖 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("📃 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("🔍 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) |