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import streamlit as st
import pandas as pd
from transformers import pipeline
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
# Download necessary NLTK resources
nltk.download('stopwords')
nltk.download('wordnet')
# Initialize the zero-shot classification pipeline
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
# Streamlit interface setup
st.title("Resume-based Personality Prediction by Serikov Ayanbek")
resume_text = st.text_area("Enter Resume Text Here", height=300)
# Load data from Excel
data = pd.read_excel("ResponseTest.xlsx")
data_open = pd.read_excel("ResponseOpen.xlsx")
# Define preprocessing function
def preprocess_text(text):
text = re.sub(r'\W', ' ', str(text))
text = text.lower()
text = re.sub(r'\s+[a-z]\s+', ' ', text)
text = re.sub(r'^[a-z]\s+', ' ', text)
text = re.sub(r'\s+', ' ', text)
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
tokens = text.split()
tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]
return ' '.join(tokens)
# Prepare the data for prediction
data['processed_text'] = data[['CV/Resume'] + [f'Q{i}' for i in range(1, 37)]].agg(lambda x: ', '.join(x), axis=1).apply(preprocess_text)
data_open['processed_text_open'] = data_open[['Demo_F', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
data_open['processed_text_mopen'] = data_open[['Demo_M', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]
confidence_threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5)
# Automatic prediction on resume text input
if resume_text:
processed_resume = preprocess_text(resume_text)
resume_prediction = classifier(processed_resume, labels)
highest_score_label = resume_prediction['labels'][0]
st.write("Predicted Personality for the given resume:", highest_score_label)
# Automatic prediction for each row in DataFrame
for index, row in data.iterrows():
result = classifier(row['processed_text'], labels)
data.at[index, 'Predicted'] = result['labels'][0]
st.dataframe(data[['True_label', 'Predicted']])
# Separate predictions for Female and Male questions
for index, row in data_open.iterrows():
result_f = classifier(row['processed_text_open'], labels)
result_m = classifier(row['processed_text_mopen'], labels)
data_open.at[index, 'Predicted_F'] = result_f['labels'][0]
data_open.at[index, 'Predicted_M'] = result_m['labels'][0]
st.dataframe(data_open[['True_label', 'Predicted_F', 'Predicted_M']])
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