Update app.py

app.py

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+import streamlit as st
+import requests
+from bs4 import BeautifulSoup
+from transformers import pipeline
+
+# Set up MBTI classifier
+classifier = pipeline("text-classification", model="pandalla/MBTIGPT_en_ENTP")
+
+def scrape_mbti_lounge(mbti_type):
+    url = f"https://mbtilounge.com/mbti/{mbti_type}"
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    # Extract relevant information (adjust selectors as needed)
+    description = soup.find('div', class_='type-description').text
+    return description
+
+st.title("MBTI Lookup and Classification")
+
+user_input = st.text_area("Enter text to classify MBTI type:")
+
+if user_input:
+    # Classify MBTI type
+    result = classifier(user_input)[0]
+    predicted_type = result['label']
+    confidence = result['score']
+
+    st.write(f"Predicted MBTI Type: {predicted_type}")
+    st.write(f"Confidence: {confidence:.2f}")
+
+    # Fetch MBTI type description from MBTI Lounge
+    description = scrape_mbti_lounge(predicted_type)
+    st.subheader(f"Description for {predicted_type}:")
+    st.write(description)