app.py

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)
# Streamlit application title
st.title("Text Classification")
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)