Update app.py

app.py

@@ -67,7 +67,7 @@ predict_and_log(data_open, 'Predicted_M', 'processed_text_mopen', true_label_col
 
 # Optionally display a confirmation message 
 st.write("Predictions have been logged. Check your logs for details.")
-"""
+
 import streamlit as st
 import pandas as pd
 import seaborn as sns
@@ -141,3 +141,58 @@ st.write("Distribution for Predicted_F")
 plot_predictions_distribution(data, 'Predicted_F_encoded', 'Distribution of Predictions for Female Demographic')
 st.write("Distribution for Predicted_M")
 plot_predictions_distribution(data, 'Predicted_M_encoded', 'Distribution of Predictions for Male Demographic')
+"""
+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.")