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app.py
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app.py
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import streamlit as st
import pandas as pd
import re
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
data = pd.read_csv('jobs.csv')
data = data.drop("Unnamed: 0", axis=1)
tfidf = TfidfVectorizer(input="content", stop_words="english")
all_industries = data["Industry"].unique()
all_industries = sorted(all_industries)
def get_skills_by_industry(industry):
return data[data["Industry"] == industry]["Key Skills"].str.split(",").explode().unique()
def recommend_jobs(skills, data):
skills = [skill.lower().strip() for skill in skills]
matching_jobs = data[data["Key Skills"].str.lower().str.contains('|'.join(skills), regex=re.DOTALL)]
tfidf_matrix = tfidf.fit_transform(matching_jobs["Key Skills"])
similarity = cosine_similarity(tfidf_matrix)
matching_jobs["Similarity"] = similarity.sum(axis=1) # Sum of similarities
matching_jobs = matching_jobs.sort_values(by="Similarity", ascending=False)
aggregated_jobs = matching_jobs.groupby("Job Title").first().reset_index()
return aggregated_jobs[["Job Title", "Job Experience Required", "Key Skills"]]
# Streamlit UI
st.title("Job Recommendation System")
st.sidebar.title("Input Information")
selected_industry = st.sidebar.selectbox("Select Industry", all_industries)
if selected_industry:
industry_skills = get_skills_by_industry(selected_industry)
selected_skills = st.sidebar.multiselect("Select skills", industry_skills)
if st.sidebar.button("Recommend Jobs"):
if selected_skills:
recommended_jobs = recommend_jobs(selected_skills, data)
st.subheader("Recommended Jobs")
st.dataframe(recommended_jobs)
else:
st.error("Please select some skills.")