app.py

import streamlit as st
from transformers import pipeline

sentiment_analysis = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
def perform_sentiment_analysis(text):
    result = sentiment_analysis(text)
    return {'label': result[0]['label'], 'score': result[0]['score']}

def main():
    st.title("Financial Sentiment Analysis")

    # Input for financial content
    financial_content = st.text_area("Enter Financial Content:", "With the launch of Apple Silicon, Apple shares have increased")

    # Perform sentiment analysis on button click
    if st.button("Submit"):
        sentiment="Neutral"
        if financial_content.strip():
            sentiment_result = perform_sentiment_analysis(financial_content)
            if sentiment_result['label'][0]=="5" or sentiment_result['label'][0]=="4" :
                sentiment= "Positive"
            elif sentiment_result['label'][0]=="1" or sentiment_result['label'][0]=="2" :
                sentiment= "Negative"
            st.success(f"Sentiment: {sentiment}     Score: {sentiment_result['score']:.2f}")
        else:
            st.warning("Please enter financial content before submitting.")

if __name__ == "__main__":
    main()