app.py

import streamlit as st
from transformers import pipeline

# Load the summariztion model pipeline
summarizer_ntg = pipeline("text2text-generation", model="mrm8488/t5-base-finetuned-summarize-news")
classifier = pipeline("text-classification", model='Lauraayu/News_Classi_Model', return_all_scores=True)

# Streamlit application title
st.title("News Classification")
st.write("Classification for different News types")

# Text input for user to enter the text to classify
text = st.text_area("Enter the News to classify","")

# Perform text classification when the user clicks the "Classify" button
if st.button("Classify"):
    
    # Perform text classification on the input text
    result0 = summarizer_ntg(text)
    result = classifier(result0)
    # 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)