app.py

import streamlit as st
from transformers import pipeline
import time

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


def main():
    dicts={"bullish":'Positive📈',"bearish":'Negative📉','neutral':"Neutral😐"}
    st.header("Summarize Your Finance News and Analyze Sentiment📰")
    text=st.text_input('Input your Finance news(Max lenth<=3000): ',None,max_chars=3000)
    if text is not None:
        st.text('Your Finance news: ')
        st.write(text)
        st.slider(min_value=0, max_value=100,value=[0,100])
        st.divider()  # Draws a horizontal rule
        #Stage 1: Text Summarization
        st.text('Processing Finance News Summarization...')
        text_summarize=pipeline("summarization", model="nickmuchi/fb-bart-large-finetuned-trade-the-event-finance-summarizer")
        summary=text_summarize(text)[0]['summary_text']
        st.write(summary)
        st.slider(min_value=0, max_value=100,value=[0,100])
        st.divider()  # Draws a horizontal rule
        #Stage 2: Sentiment Analytics
        st.text('Processing Sentiment Analytics...')
        label = sentiment(summary)
        label=dicts[label]
        st.slider(min_value=0, max_value=100,value=[0,100])
        st.divider()  # Draws a horizontal rule
        st.text('The sentiment of finance news is: ')
        st.write(label)

if __name__ == "__main__":
    main()