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Parent(s):
9971fc6
Create HR Resume Screening Assistance.py
Browse files
pages/HR Resume Screening Assistance.py
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import streamlit as st
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from utils import *
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import uuid
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#https://streamlit-emoji-shortcodes-streamlit-app-gwckff.streamlit.app/
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#Creating session variables
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if 'unique_id' not in st.session_state:
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st.session_state['unique_id'] =''
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def main():
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#st.set_page_config(page_title="Resume Screening BOT")
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st.set_page_config(
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page_title="Resume Screening Assistance",
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page_icon="π",
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layout="wide",
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initial_sidebar_state="expanded",
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menu_items={
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'Get Help': 'https://www.extremelycoolapp.com/help',
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'Report a bug': "https://www.extremelycoolapp.com/bug",
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'About': "# This is a header. This is an *extremely* cool app!"
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}
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)
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st.title("HR - Resume Screening Assistance π ")
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st.subheader("I can help you in resume screening process")
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#st.sidebar.title("π")
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st.sidebar.image('./resume_screening.jpg',width=300, use_column_width=True)
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# Applying Styling
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st.markdown("""
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<style>
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div.stButton > button:first-child {
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background-color: #0099ff;
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color:#ffffff;
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}
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div.stButton > button:hover {
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background-color: #00ff00;
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color:#FFFFFF;
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}
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</style>""", unsafe_allow_html=True)
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job_description = st.text_area("Please paste the 'JOB DESCRIPTION' here...π",key="1")
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document_count = st.text_input("No.of 'RESUMES' to return",key="2")
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# Upload the Resumes (pdf files)
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pdf = st.file_uploader("Upload resumes here, only PDF files allowed", type=["pdf"],accept_multiple_files=True)
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submit=st.button("Help me with the analysis")
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if submit:
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with st.spinner('Wait for it...'):
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#Creating a unique ID, so that we can use to query and get only the user uploaded documents from PINECONE vector store
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st.session_state['unique_id']=uuid.uuid4().hex
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#Create a documents list out of all the user uploaded pdf files
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final_docs_list=create_docs(pdf,st.session_state['unique_id'])
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#st.write(final_docs_list)
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#Displaying the count of resumes that have been uploaded
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st.write("*Resumes uploaded* :"+str(len(final_docs_list)))
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#Create embeddings instance
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embeddings=create_embeddings_load_data()
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#Fecth relavant documents from Vectorspace
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relavant_docs=close_matches(job_description,document_count,final_docs_list,embeddings)
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#Introducing a line separator
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st.write(":heavy_minus_sign:" * 30)
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#For each item in relavant docs - we are displaying some info of it on the UI
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for item in range(len(relavant_docs)):
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st.subheader("π "+str(item+1))
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#Displaying Filepath
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st.write("**File** : "+relavant_docs[item][0].metadata['name'])
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#Introducing Expander feature
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with st.expander('Show me π'):
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st.info("**Match Score** : "+ str(1 - relavant_docs[item][1]))
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#st.write("***"+relavant_docs[item][0].page_content)
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#Gets the summary of the current item using 'get_summary' function that we have created which uses LLM & Langchain chain
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summary = get_summary(relavant_docs[item][0])
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st.write("**Summary** : "+summary)
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st.success("Hope I was able to save your timeβ€οΈ")
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#Invoking main function
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if __name__ == '__main__':
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main()
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