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import streamlit as st
import pickle
import re
#loading models
clf = pickle.load(open('clf.pkl','rb'))
tfidf = pickle.load(open('tfidf.pkl','rb'))
def clean_resume(resume_text):
"""
Clean the text in the resume i.e. remove unwanted chars in the text. For e.g.
1 URLs,
2 Hashtags,
3 Mentions,
4 Special Chars,
5 Punctuations
Parameters:
resume_text (str): The input resume text to be cleaned.
Returns:
clean_text (str): Clean Resume.
"""
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
def remove_stopwords(text, language='english'):
"""
Remove stopwords from a given text.
Parameters:
text (str): The input text from which to remove stopwords.
language (str): The language of the stopwords. Default is 'english'.
Returns:
filtered_text (str): Text without stopwords.
"""
stop_words = set([
"i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your",
"yours", "yourself", "yourselves", "he", "him", "his", "himself", "she",
"her", "hers", "herself", "it", "its", "itself", "they", "them", "their",
"theirs", "themselves", "what", "which", "who", "whom", "this", "that",
"these", "those", "am", "is", "are", "was", "were", "be", "been", "being",
"have", "has", "had", "having", "do", "does", "did", "doing", "a", "an",
"the", "and", "but", "if", "or", "because", "as", "until", "while", "of",
"at", "by", "for", "with", "about", "against", "between", "into", "through",
"during", "before", "after", "above", "below", "to", "from", "up", "down",
"in", "out", "on", "off", "over", "under", "again", "further", "then",
"once", "here", "there", "when", "where", "why", "how", "all", "any",
"both", "each", "few", "more", "most", "other", "some", "such", "no",
"nor", "not", "only", "own", "same", "so", "than", "too", "very", "s",
"t", "can", "will", "just", "don", "should", "now"
])
words = text.split()
filtered_words = [word for word in words if word.lower() not in stop_words]
filtered_text = ' '.join(filtered_words)
return filtered_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)
cleaned_resume = remove_stopwords(cleaned_resume)
input_features = tfidf.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() |