app.py

import streamlit as st
from dotenv import load_dotenv
from utils import *
import uuid

#Creating session variables
if 'unique_id' not in st.session_state:
    st.session_state['unique_id'] =''

def main():
    load_dotenv()

    st.set_page_config(page_title="Resume Screening Assistance", page_icon="📝")
    st.title("HR - Resume Screening Assistance...")
    # st.subheader("I can help you in resume screening process")

    job_description = st.text_area("Please paste the 'JOB DESCRIPTION' here...", key="desc")
    document_count = st.text_input("Enter the no.of resumes to return",key="count")
    # Upload the Resumes (pdf files)
    pdf = st.file_uploader("Upload resumes here, only PDF files allowed", type=["pdf"], accept_multiple_files=True)

    submit=st.button("Screen")

    if submit:
        with st.spinner('Wait for it...'):

            #Creating a unique ID, so that we can use to query and get only the user uploaded documents from PINECONE vector store
            st.session_state['unique_id']=uuid.uuid4().hex

            #Create a documents list out of all the user uploaded pdf files
            final_docs_list=create_docs(pdf,st.session_state['unique_id'])

            #Displaying the count of resumes that have been uploaded
            st.write("*Resumes uploaded* :"+str(len(final_docs_list)))

            #Create embeddings instance
            embeddings=create_embeddings_load_data()

            #Push data to PINECONE
            # using already created index automatic-ticket-tool
            table = push_to_lancedb(embeddings)

            #Fecth relavant documents from LanceDB
            relavant_docs = similar_docs_lancedb(job_description,table,embeddings,final_docs_list)

            #st.write(relavant_docs)

            #Introducing a line separator
            st.write(":heavy_minus_sign:" * 30)
            st.success(f"Find below the {document_count} Resumes")

            # For each item in relavant docs - we are displaying some info of it on the UI
            for idx, resume in enumerate(relavant_docs[:int(document_count)]):
                st.subheader("👉 Resume "+str(idx+1))
                # st.write(resume.page_content)

                #Gets the summary of the current item using 'get_summary' function that we have created which uses LLM & Langchain chain
                summary = get_summary(relavant_docs[idx])
                print([relavant_docs[idx]])
                st.write("**Summary** : "+summary)

        st.success("Hope I was able to save your time❤️")


#Invoking main function
if __name__ == '__main__':
    main()