| import streamlit as st | |
| from transformers import pipeline | |
| # Streamlit application title | |
| st.title("Financial News Summarization & Sentiment Analysis") | |
| st.write("Summarize long financial news and identify the sentiment to help you make decisions.") | |
| # Load the summarization and sentiment analysis pipelines | |
| pipe = pipeline("text-classification", model="roselyu/FinSent-XLMR-FinNews") | |
| # User input | |
| user_input = st.text_area("Enter a financial news article:") | |
| # Summarize and identify sentiment button | |
| if st.button("Summarize and Identify Sentiment"): | |
| # Analyze sentiment | |
| sentiment_label = pipe(user_input)[0]["label"] | |
| # Display summary and sentiment | |
| st.write(f"Sentiment: {sentiment_label}") |