Create app.py

app.py

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+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}")