File size: 2,157 Bytes
a5a1bbf
 
 
b9c5049
 
 
a5a1bbf
 
 
 
b9c5049
a5a1bbf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b9c5049
a5a1bbf
 
b9c5049
a5a1bbf
 
 
 
 
 
 
 
 
b9c5049
a5a1bbf
 
 
 
b9c5049
a5a1bbf
1f16ba7
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
import streamlit as st
import os
from utils import *
import uuid

st.write("Token exists:", bool(os.getenv("HUGGINGFACEHUB_API_TOKEN")))

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("Please paste the 'JOB DESCRIPTION' here...", key="1")
    document_count = st.text_input("No.of 'RESUMES' to return", key="2")
    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:
        API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
        if not API_TOKEN:
            st.error("ERROR: HUGGINGFACEHUB_API_TOKEN not found. Set it in Hugging Face Space Secrets.")
            return

        with st.spinner('Wait for it...'):
            st.session_state['unique_id'] = uuid.uuid4().hex
            final_docs_list = create_docs(pdf, st.session_state['unique_id'])

            st.write("*Resumes uploaded* :" + str(len(final_docs_list)))

            embeddings = create_embeddings_load_data()

            # --- CHROMADB REPLACEMENT ---
            push_to_chroma(st.session_state['unique_id'], embeddings, final_docs_list)
            relavant_docs = similar_docs(job_description, document_count, st.session_state['unique_id'])
            # ---------------------------

            st.write(":heavy_minus_sign:" * 30)

            for item in range(len(relavant_docs)):
                st.subheader("👉 " + str(item + 1))
                st.write("**File** : " + relavant_docs[item][0].metadata['name'])

                with st.expander('Show me 👀'):
                    st.info("**Match Score** : " + str(relavant_docs[item][1]))
                    summary = get_summary(relavant_docs[item][0])
                    st.write("**Summary** : " + summary)

        st.success("Hope I was able to save your time❤️")


if __name__ == '__main__':
    main()