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="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")  # Replace 'ResponseTest.xlsx' with your actual file name
data_open = pd.read_excel("ResponseOpen.xlsx")  # Replace 'ResponseTest.xlsx' with your actual file name

# 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 = 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 for processing
question_columns = [f'Q{i}' for i in range(1, 37)]  # Adjust the range based on your data columns
data['processed_text'] = data[['CV/Resume'] + question_columns].agg(lambda x: ', '.join(x), axis=1)
#data['processed_text'] = data[['CV/Resume'] + question_columns].agg(lambda x: ', '.join(x), axis=1).apply(preprocess_text)
#data_open['processed_text_open'] = data_open[['CV/Resume', 'Question']].agg(' '.join, axis=1).apply(preprocess_text)
data_open['processed_text_open'] = data_open[['Demo_F', 'Question']].agg(' '.join, axis=1)
data_open['processed_text_mopen'] = data_open[['Demo_M', 'Question']].agg(' '.join, axis=1)

labels = ["Peacemaker", "Loyalist", "Achiever", "Reformer", "Individualist", "Helper", "Challenger", "Investigator", "Enthusiast"]

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

if st.button("Predict Personality by Test"):
    # Function to apply predictions using dynamic labels from MAX1, MAX2, MAX3 and only return the highest scored label
    def get_predictions(row):
        custom_labels = [row['MAX1'], row['MAX2'], row['MAX3']]  # Get labels from each row
        processed_text = row['processed_text']
        result = classifier(processed_text, custom_labels)
        highest_score_label = result['labels'][0]  # Assumes the labels are sorted by score, highest first
        return highest_score_label

    # Apply predictions across all rows
    data['Predicted'] = data.apply(get_predictions, axis=1)
    st.dataframe(data[['True_label','MAX1','MAX2','MAX3', 'Predicted']])

if st.button("Predict Personality by Open Question F"):
    def get_predictions(row):
        processed_text = row['processed_text_open']
        result = classifier(processed_text, labels)
        highest_score_label = result['labels'][0]  # Assumes the labels are sorted by score, highest first
        return highest_score_label
        
    

    # Apply predictions across all rows
    data_open['Predicted_F'] = data_open.apply(get_predictions, axis=1)
    st.dataframe(data_open[['True_label', 'Predicted_F']])

if st.button("Predict Personality by Open Question M"):
    
        
    def get_predictionsM(row):
        processed_text = row['processed_text_mopen']
        result = classifier(processed_text, labels)
        highest_score_label = result['labels'][0]  # Assumes the labels are sorted by score, highest first
        return highest_score_label

    # Apply predictions across all rows
    data_open['Predicted_M'] = data_open.apply(get_predictionsM, axis=1)
    st.dataframe(data_open[['True_label', 'Predicted_M']])