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Sleeping
| import streamlit as st | |
| from utils import * | |
| import uuid | |
| #Creating session variables | |
| if 'unique_id' not in st.session_state: | |
| st.session_state['unique_id'] ='' | |
| def main(): | |
| st.set_page_config(page_title="Resume Screening Assistance") | |
| st.title("HR - Resume Screening Assistance...💁 ") | |
| st.subheader("I can help you in resume screening process") | |
| #st.sidebar.title("😎") | |
| st.sidebar.image('./resume_screening.jpg',width=300, use_column_width=True) | |
| job_description = st.text_area("Please paste the 'JOB DESCRIPTION' here...",key="1") | |
| document_count = st.text_input("No.of 'RESUMES' to return",key="2") | |
| # 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("Help me with the analysis") | |
| 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']) | |
| #st.write(final_docs_list) | |
| #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() | |
| #Fecth relavant documents from Vectorspace | |
| relavant_docs=close_matches(job_description,document_count,final_docs_list,embeddings) | |
| #Introducing a line separator | |
| st.write(":heavy_minus_sign:" * 30) | |
| #For each item in relavant docs - we are displaying some info of it on the UI | |
| for item in range(len(relavant_docs)): | |
| st.subheader("👉 "+str(item+1)) | |
| #Displaying Filepath | |
| st.write("**File** : "+relavant_docs[item][0].metadata['name']) | |
| #Introducing Expander feature | |
| with st.expander('Show me 👀'): | |
| st.info("**Match Score** : "+ str(1 - relavant_docs[item][1])) | |
| #st.write("***"+relavant_docs[item][0].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[item][0]) | |
| st.write("**Summary** : "+summary) | |
| st.success("Hope I was able to save your time❤️") | |
| #Invoking main function | |
| if __name__ == '__main__': | |
| main() |