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import streamlit as st
import pandas as pd
import numpy as np
import re
import pickle
import pdfminer
from pdfminer.high_level import extract_text
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Conv1D, MaxPooling1D, LSTM, Dense, GlobalMaxPooling1D
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import LabelEncoder
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
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,max_sequence_length):
# Load the trained DeepRank model
model = load_deeprank_model(max_sequence_length)
# Process the resumes data
resumes_df = pd.DataFrame(resumes_data)
resumes_text = resumes_df['ResumeText'].values
# Tokenize the text and convert to sequences
tokenized_text = tokenizer.texts_to_sequences(resumes_text)
# Pad sequences to have the same length
max_sequence_length = 500 # Assuming maximum sequence length of 500 words
padded_text = pad_sequences(tokenized_text, maxlen=max_sequence_length)
# Make predictions
predicted_probs = model.predict(padded_text)
# 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 load_deeprank_model(max_sequence_length):
# Load the saved DeepRank model
model = Sequential()
# Add layers to the model (example architecture, adjust as needed)
model.add(Embedding(input_dim=vocab_size, output_dim=128, input_length=max_sequence_length))
model.add(Conv1D(filters=128, kernel_size=5, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(LSTM(64))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.load_weights('deeprank_model.h5') # Replace 'deeprank_model.h5' with your saved model file
return model
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,max_sequence_length)
st.write(pd.DataFrame(ranks))
if __name__ == '__main__':
# Load label encoder and tokenizer
df = pd.read_csv('UpdatedResumeDataSet.csv')
df['cleaned'] = df['Resume'].apply(lambda x: cleanResume(x))
label = LabelEncoder()
df['Category'] = label.fit_transform(df['Category'])
# Tokenize text and get vocabulary size and number of classes
text = df['cleaned'].values
tokenizer = Tokenizer()
tokenizer.fit_on_texts(text)
vocab_size = len(tokenizer.word_index) + 1
num_classes = len(label.classes_)
max_sequence_length = 500
main()
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