app.py

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="Fralet/personality")

# Define the default candidate labels (modifiable if different labels are needed)
default_labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]

# Streamlit interface setup
st.title("Resume-based Personality Prediction by Serikov Ayanbek")

# Load data from Excel
data = pd.read_excel("your_excel_file.xlsx")  # Adjust file path/name as necessary

# Preprocess text 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 is 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)

# Combine relevant text columns
data['combined_text'] = data[['CV/Resume'] + [f'Q{i}' for i in range(1, 37)]].agg(' '.join, axis=1)
data['processed_text'] = data['combined_text'].apply(preprocess_text)

# Streamlit user inputs
user_labels = st.text_input("Enter custom labels separated by comma (optional)")
labels = user_labels.split(',') if user_labels else default_labels
confidence_threshold = st.slider("Confidence Threshold", 0.0, 1.0, 0.5)

if st.button("Predict Personality"):
    # Predict personality from processed text
    data['predictions'] = data['processed_text'].apply(lambda x: classifier(x, labels))
    
    # Extract predictions above confidence threshold and display alongside MAX labels
    data['predicted_labels'] = data['predictions'].apply(lambda x: {label: f"{score*100:.2f}%" for label, score in zip(x['labels'], x['scores']) if score >= confidence_threshold})
    data['MAX_labels'] = data.apply(lambda x: [x['MAX1'], x['MAX2'], x['MAX3']], axis=1)
    
    st.write("Predictions and Labels:")
    st.dataframe(data[['True_label', 'Predicted', 'predicted_labels', 'MAX_labels']])

# Run this last part to show the DataFrame outside the button press if needed
st.dataframe(data)