Update app.py

app.py

@@ -1,25 +1,30 @@
 import streamlit as st
 from transformers import pipeline
+
 # Load the text classification model pipeline
-classifier = pipeline("text-classification",model='isom5240sp24/bert-base-
-uncased-emotion', return_all_scores=True)
+classifier = pipeline("text-classification",model='isom5240sp24/bert-base-uncased-emotion', return_all_scores=True)
+
 # Streamlit application title
 st.title("Text Classification for you")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear,
-surprise")
+st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
+
 # Text input for user to enter the text to classify
 text = st.text_area("Enter the text to classify", "")
+
 # Perform text classification when the user clicks the "Classify" button
 if st.button("Classify"):
-# Perform text classification on the input text
-results = classifier(text)[0]
-# Display the classification result
-max_score = float('-inf')
-max_label = ''
-for result in results:
-if result['score'] > max_score:
-max_score = result['score']
-max_label = result['label']
-st.write("Text:", text)
-st.write("Label:", max_label)
-st.write("Score:", max_score)
+    # Perform text classification on the input text
+    results = classifier(text)[0]
+    
+    # Display the classification result
+    max_score = float('-inf')
+    max_label = ''
+    
+    for result in results:
+        if result['score'] > max_score:
+            max_score = result['score']
+            max_label = result['label']
+            
+    st.write("Text:", text)
+    st.write("Label:", max_label)
+    st.write("Score:", max_score)