|
import streamlit as st
|
|
from functions import extract_text_from_pdf, git_most_similar_job, generate_gemini_content
|
|
import os
|
|
|
|
|
|
st.title("Jobs Suitable for each CV ")
|
|
|
|
info = """- The user uploads his CV in PDF format and passes the number of jobs he wants,
|
|
and we display the number of jobs most suitable for each CV from the following recruitment sites,
|
|
LinkedIn, Wazzaf, Indeed and Bayt"""
|
|
st.write(f":blue[{info}]")
|
|
|
|
note = " Note : that the jobs currently available are in the fields of programming and technology only"
|
|
st.write(f":red[{note}]")
|
|
|
|
|
|
|
|
st.markdown(
|
|
"""
|
|
<style>
|
|
.dataframe {
|
|
max-width: 3000px;
|
|
margin: auto;
|
|
}
|
|
</style>
|
|
""",
|
|
unsafe_allow_html=True,
|
|
)
|
|
|
|
|
|
|
|
|
|
uploaded_file = st.file_uploader("Choose a CV file", "pdf")
|
|
number_of_jobs = st.number_input("Number of Jobs", min_value=1, step=1, max_value=2000)
|
|
submit = st.button("get jobs")
|
|
|
|
if submit and uploaded_file and number_of_jobs:
|
|
st.subheader("The Most recommended jobs is : ")
|
|
pdf_content = uploaded_file.read()
|
|
|
|
pdf_text = extract_text_from_pdf(pdf_content)
|
|
cv_summary = generate_gemini_content(transcript_text=pdf_text)
|
|
data_df = git_most_similar_job(cv_summarize=cv_summary, number_of_jobs=number_of_jobs)
|
|
|
|
|
|
|
|
st.data_editor(
|
|
data_df,
|
|
column_config={
|
|
"job_link": st.column_config.LinkColumn(
|
|
"Job Link",
|
|
help=f"The top {number_of_jobs} jobs links",
|
|
validate="^https://[a-z]+\.streamlit\.app$",
|
|
max_chars=1000,
|
|
),
|
|
},
|
|
hide_index=True,
|
|
)
|
|
st.success(f"The top {number_of_jobs} jobs")
|
|
|
|
else:
|
|
st.warning("Please upload a PDF file and add number of job .") |
