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| # Application that will help the Resume Screener will llms to find the best fits for the job | |
| import streamlit as st | |
| import uuid | |
| from utils import * | |
| # 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') | |
| job_description = st.text_area("Enter your job description", key="1") | |
| document_count = st.text_area("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 the 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 | |
| docs = create_docs(pdf, st.session_state["unique_id"]) | |
| # Display the count of the docs that were uploaded | |
| st.write(len(docs)) | |
| # Create embeddings instance | |
| embeddings = create_embeddings_load_data() | |
| # Push data to PINECONE | |
| st.write(push_to_pinecone( | |
| '63987f25-a66d-448f-8a91-d64a9dd71d9a', | |
| "test", embeddings, docs) | |
| ) | |
| st.success('Hope I was able to save your time <3') | |
| if __name__ == '__main__': | |
| main() | |