deeplearningwithpython5240/Emotion_helper

1import streamlit as st2from transformers import pipeline3 4# Load the text classification model pipeline5classifier = pipeline("text-classification",model='isom5240sp24/bert-base-uncased-emotion', return_all_scores=True)6 7# Streamlit application title8st.title("Text Classification")9st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")10 11# Text input for user to enter the text to classify12text = st.text_area("Enter the text to classify", "")13 14# Perform text classification when the user clicks the "Classify" button15if st.button("Classify"):16    # Perform text classification on the input text17    results = classifier(text)[0]18 19    # Display the classification result20    max_score = float('-inf')21    max_label = ''22 23    for result in results:24        if result['score'] > max_score:25            max_score = result['score']26            max_label = result['label']27 28    st.write("Text:", text)29    st.write("Label:", max_label)30    st.write("Score:", max_score)31