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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.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(Max lenth<=3000): ',max_chars=3000)
if isinstance(text,str):
#st.text('Your Finance news: ')
st.write('Your Finance news: ',str(text))
time.sleep(2)
#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)
#Stage 2: Sentiment Analytics
st.text('Processing Sentiment Analytics...')
label = sentiment(summary)
label=dicts[label]
st.text('The sentiment of finance news is: ')
st.write(label)
if __name__ == "__main__":
main() |