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| import streamlit as st | |
| from dotenv import load_dotenv | |
| from utils import * | |
| import uuid | |
| from langchain_community.vectorstores import FAISS | |
| from langchain_chroma import Chroma | |
| import pandas as pd | |
| st.set_page_config(page_title="Resume Screening Assistance", layout="wide") | |
| custom_html = """ | |
| <style> | |
| h1 { | |
| background-color: #e6763b; | |
| padding: 20px; | |
| margin: 0; | |
| text-align: center; /* Center the text */ | |
| } | |
| img { | |
| max-width: 100%; | |
| height: 31px; | |
| margin-right: 20px; | |
| margin-bottom: 6px; | |
| } | |
| h1 img { | |
| vertical-align: middle; | |
| } | |
| </style> | |
| <body> | |
| <h1><img src="https://www.athmick.com/static/media/athmick-logo-with-name.32abc7ca97607204825eed0610ae2eea.svg">HR Resume Copilot ... 💁</h1> | |
| </body> | |
| """ | |
| # Render the custom HTML | |
| st.markdown(custom_html, unsafe_allow_html=True) | |
| #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") | |
| # st.title("HR - Resume Screening Assistance💁") | |
| st.subheader("I can help you in resume screening process") | |
| # Upload the Job Description (pdf files) | |
| job_description = st.file_uploader("JOB DESCRIPTION", type=["pdf"]) | |
| # Upload the Resumes (pdf files) | |
| pdf = st.file_uploader("RESUME", type=["pdf"],accept_multiple_files=True) | |
| #document retrun count | |
| document_count = st.text_input("No.of 'RESUMES' to return",key="2") | |
| #submit button | |
| 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']) | |
| #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() | |
| #using faiss db | |
| db = Chroma.from_documents(final_docs_list, embeddings) | |
| job_description_txt = get_pdf_text(job_description) | |
| #using faiss db for similarity search with similarity score | |
| relavant_docs = db.similarity_search_with_relevance_scores(job_description_txt,k=int(document_count)) | |
| data = [] | |
| #For each item in relavant docs - we are displaying some info of it on the UI | |
| for item in pdf: | |
| # st.subheader("👉 "+str(item+1)) | |
| resume_txt = get_pdf_text(item) | |
| # #Displaying Filepath | |
| # document_object = final_docs_list[item][0] | |
| # file_name = document_object.metadata.get('name', None) | |
| finel_name = item.name | |
| # st.write("**File** : "+str(finel_name)) | |
| #Introducing Expander feature | |
| with st.expander('Show me 👀'): | |
| # st.info("**Matched Vector Score** : "+str(relavant_docs[item][1])) | |
| matched_result = opeani_response(resume_txt, job_description_txt) | |
| matched_percentage, reason, skills_to_improve, keywords, irrelevant = get_strip_response(matched_result) | |
| #Gets the summary of the current item using 'get_summary' function that we have created which uses LLM & Langchain chain | |
| summary = get_summary(resume_txt) | |
| # Append the information to the DataFrame | |
| data.append([finel_name, matched_percentage, reason, skills_to_improve, keywords, irrelevant, summary]) | |
| table_data = pd.DataFrame(data, columns=["File", "Matched Score", "Matched Reason" , "Skills to improve", "Keywords", "Irrelevant", "Summary"]) | |
| # Sort the DataFrame based on the 'Matched Score' column in descending order | |
| df_sorted = table_data.sort_values(by='Matched Score', ascending=False) | |
| # Reset the index | |
| df_sorted.reset_index(drop=True, inplace=True) | |
| # Loop through each row and print in the specified format | |
| for index, row in df_sorted.iterrows(): | |
| st.write("**File** : "+row["File"]) | |
| st.info("**Matched Score 💯** : " + str(row["Matched Score"]) + "%") | |
| st.write("**Matched Reason 🌟** : " + row["Matched Reason"]) | |
| st.write("**Skills to improve 🎯** : " + row["Skills to improve"]) | |
| st.write("**Keywords 🗝️** : " + row["Keywords"]) | |
| st.write("**Irrelevant 📛** : " + row["Irrelevant"]) | |
| st.write("**Summary 📜** : " + row["Summary"]) | |
| st.write("## Relevant Documents") | |
| st.table(df_sorted) | |
| # graph = ploty_graph(matched_percentage_dict) | |
| # st.plotly_chart(graph) | |
| csv = df_sorted.to_csv().encode('utf-8') | |
| st.download_button( | |
| label="Download data as CSV", | |
| data=csv, | |
| file_name='Result Data.csv', | |
| mime='text/csv', | |
| ) | |
| st.success("Hope I was able to save your time❤️") | |
| #Invoking main function | |
| if __name__ == '__main__': | |
| main() |