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Resume-analyser-streamlit
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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()