Spaces:
Sleeping
Sleeping
| 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 using custom labels | |
| def predict_personality(row): | |
| custom_labels = [row['MAX1'], row['MAX2'], row['MAX3']] | |
| processed_text = row['processed_text'] | |
| result = classifier(processed_text, custom_labels) | |
| return result['labels'][0] # Assumes the labels are sorted by score, highest first | |
| data['Predicted'] = data.apply(predict_personality, axis=1) | |
| st.dataframe(data[['True_label', 'MAX1', 'MAX2', 'MAX3', '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']]) | |