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import streamlit as st
import pickle
import re
import nltk
nltk.download('punkt')
nltk.download('stopwords')
#loading models
clf = pickle.load(open('clf.pkl','rb'))
tfidfd = pickle.load(open('tfidf.pkl','rb'))
def clean_resume(resume_text):
clean_text = re.sub('http\S+\s*', ' ', resume_text)
clean_text = re.sub('RT|cc', ' ', clean_text)
clean_text = re.sub('#\S+', '', clean_text)
clean_text = re.sub('@\S+', ' ', clean_text)
clean_text = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', clean_text)
clean_text = re.sub(r'[^\x00-\x7f]', r' ', clean_text)
clean_text = re.sub('\s+', ' ', clean_text)
return clean_text
# web app
def main():
st.title("Resume Screening App")
uploaded_file = st.file_uploader('Upload Resume', type=['txt','pdf'])
if uploaded_file is not None:
try:
resume_bytes = uploaded_file.read()
resume_text = resume_bytes.decode('utf-8')
except UnicodeDecodeError:
# If UTF-8 decoding fails, try decoding with 'latin-1'
resume_text = resume_bytes.decode('latin-1')
cleaned_resume = clean_resume(resume_text)
input_features = tfidfd.transform([cleaned_resume])
prediction_id = clf.predict(input_features)[0]
st.write(prediction_id)
# Map category ID to category name
category_mapping = {
15: "Java Developer",
23: "Testing",
8: "DevOps Engineer",
20: "Python Developer",
24: "Web Designing",
12: "HR",
13: "Hadoop",
3: "Blockchain",
10: "ETL Developer",
18: "Operations Manager",
6: "Data Science",
22: "Sales",
16: "Mechanical Engineer",
1: "Arts",
7: "Database",
11: "Electrical Engineering",
14: "Health and fitness",
19: "PMO",
4: "Business Analyst",
9: "DotNet Developer",
2: "Automation Testing",
17: "Network Security Engineer",
21: "SAP Developer",
5: "Civil Engineer",
0: "Advocate",
}
category_name = category_mapping.get(prediction_id, "Unknown")
st.write("Predicted Category:", category_name)
# python main
if __name__ == "__main__":
main()
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