app.py

import streamlit as st
from transformers import pipeline



# txt2Story
def text_summarize(text):
    pipe = pipeline("summarization", model="human-centered-summarization/financial-summarization-pegasus")
    summary = pipe(text)[0]['summary_text']
    print(summary)
    return summary

# Story2Audio
def sentiment(story_text):
    pipe = pipeline("text-classification", model="WillWEI0103/CustomModel_finance_sentiment_analytics")
    label = pipe(story_text)[0]['label']
    return label


def main():
    st.set_page_config(page_title="Your Finance news", page_icon="📰")
    st.header("Summarize Your Finance News and Analyze Sentiment")
    text=st.text_input('Input your Finance news: ')
    
    #Stage 1: Text Summarization
    st.text('Processing Finance News Summarization...')
    summary = text_summarize(text)
    st.write(summary)

    #Stage 2: Sentiment Analytics
    st.text('Processing Sentiment Analytics...')
    label = sentiment(summary)
    st.text('The sentiment of finance news is: ')
    st.write(label)

if __name__ == "__main__":
    main()