app.py

import streamlit as st
from transformers import pipeline
import re
import nltk
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
nltk.download('stopwords')
nltk.download('wordnet')

# Initialize the zero-shot classification pipeline
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# Define the candidate labels according to the Enneagram types
default_labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]

# Streamlit interface
st.title("Resume-based Personality Prediction")
resume_text = st.text_area("Enter Resume Text Here", height=300)

# User-defined labels option
user_labels = st.text_input("Enter custom labels separated by comma (optional)")
labels = user_labels.split(',') if user_labels else default_labels

# Prediction confidence threshold
confidence_threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5)

if st.button("Predict Personality"):
    # Text Preprocessing
    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)

    processed_text = preprocess_text(resume_text)
    
    # Make prediction
    result = classifier(processed_text, labels)
    
    # Display the results
    st.write("Predictions (above confidence threshold):")
    displayed = False
    for label, score in zip(result['labels'], result['scores']):
        if score >= confidence_threshold:
            st.write(f"{label}: {score*100:.2f}%")
            displayed = True
    if not displayed:
        st.write("No predictions exceed the confidence threshold.")