Update app.py

app.py

@@ -1,23 +1,24 @@
 import streamlit as st
 from transformers import pipeline
 
+# inital value of sentiment label
+sentiment_label = 0
+
 # 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.")
+st.title("Financial News Sentiment Analysis")
+st.write("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 and sentiment analysis
 user_input = st.text_area("Enter a short financial news article:")
+sentiment_label = pipe(user_input)[0]["label"]
 
 # Summarize and identify sentiment button
 if st.button("Identify Sentiment"):
 
-    # Analyze sentiment
-    sentiment_label = pipe(user_input)[0]["label"]
-
     # Display summary and sentiment
 
     st.write(f"Sentiment: {sentiment_label}")