Spaces:
Sleeping
Sleeping
File size: 3,127 Bytes
c840fe5 e249a3c c840fe5 e249a3c c840fe5 e249a3c c0d4f29 c840fe5 e249a3c c840fe5 |
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 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import streamlit as st
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.neighbors import KNeighborsClassifier
import re
import pickle
import pdfminer
from pdfminer.high_level import extract_text
def cleanResume(resumeText):
# Your existing cleanResume function remains unchanged
resumeText = re.sub('http\S+\s*', ' ', resumeText)
resumeText = re.sub('RT|cc', ' ', resumeText)
resumeText = re.sub('#\S+', '', resumeText)
resumeText = re.sub('@\S+', ' ', resumeText)
resumeText = re.sub('[%s]' % re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"""), ' ', resumeText)
resumeText = re.sub(r'[^\x00-\x7f]',r' ', resumeText)
resumeText = re.sub('\s+', ' ', resumeText)
return resumeText
df = pd.read_csv('UpdatedResumeDataSet.csv')
df['cleaned'] = df['Resume'].apply(lambda x: cleanResume(x))
label = LabelEncoder()
df['Category'] = label.fit_transform(df['Category'])
text = df['cleaned'].values
target = df['Category'].values
word_vectorizer = TfidfVectorizer(
sublinear_tf=True,
stop_words='english',
max_features=1500)
word_vectorizer.fit(text)
WordFeatures = word_vectorizer.transform(text)
model = OneVsRestClassifier(KNeighborsClassifier())
model.fit(WordFeatures, target)
def pdf_to_text(file):
# Use pdfminer.six to extract text from the PDF file
text = extract_text(file)
return text
def predict_category(resumes_data, selected_category):
resumes_df = pd.DataFrame(resumes_data)
resumes_features = word_vectorizer.transform(resumes_df['ResumeText'])
predicted_probs = model.predict_proba(resumes_features)
# Assign probabilities to respective job categories
for i, category in enumerate(label.classes_):
resumes_df[category] = predicted_probs[:, i]
resumes_df_sorted = resumes_df.sort_values(by=selected_category, ascending=False)
# Get the ranks for the selected category
ranks = []
for rank, (idx, row) in enumerate(resumes_df_sorted.iterrows()):
rank = rank + 1
file_name = row['FileName']
ranks.append({'Rank': rank, 'FileName': file_name})
return ranks
def main():
st.title("Resume Ranking App")
st.text("Upload resumes and select a category to rank them.")
resumes_data = []
selected_category = ""
# Handle multiple file uploads
files = st.file_uploader("Upload resumes", type=["pdf"], accept_multiple_files=True)
if files:
for file in files:
text = cleanResume(pdf_to_text(file))
resumes_data.append({'ResumeText': text, 'FileName': file.name})
selected_category = st.selectbox("Select a category to rank by", label.classes_)
if st.button("Rank Resumes"):
if not resumes_data or not selected_category:
st.warning("Please upload resumes and select a category to continue.")
else:
ranks = predict_category(resumes_data, selected_category)
st.write(pd.DataFrame(ranks))
if __name__ == '__main__':
main()
|