Rrrrrrrita/Deep_Learning_Project

1import streamlit as st2from transformers import pipeline3 4# Load the text summarization model pipeline5summarizer = pipeline("summarization", model="facebook/bart-large-cnn")6 7# Streamlit application title8st.title("Sentiment Analysis with text summarization for Singapore Airline")9 10# Text input for user to enter the text to summarize11text = st.text_area("Enter the text to analyze:", "")12 13# Perform text summarization when the user clicks the "Go!" button14if st.button("Go!"):15    # Perform text summarization on the input text16    results = summarizer(text)[0]['summary_text']17    st.write("Step 1: Text after summarization:")18    st.write(results)19 20    # Sentiment analysis as the second step21    classifier = pipeline("text-classification", model="Rrrrrrrita/Custom_Sentiment", return_all_scores=True)22 23    st.write('Step 2: Sentiment Analysis:')24    st.write("\t\t Classification for 3 emotions: positve, neutral, and negative")25 26    labels = classifier(text)[0]27    max_score = float('-inf')28    max_label = ''29    30    for label in labels:31        if label['score'] > max_score:32            max_score = label['score']33            max_label = label['label']34            35 36    st.write("\tLabel:", max_label)37    st.write("\tScore:", max_score)