Update app.py

app.py

@@ -13,7 +13,7 @@ st.write("Identify the sentiment and look into the key point to help you make de
 pipe = pipeline("text-classification", model="roselyu/FinSent-XLMR-FinNews")
 
 # User input and sentiment analysis
-user_input = st.text_area("Enter a short financial news article:")
+user_input = st.text_area("Enter a short financial news article to identify its sentiments:")
 sentiment_label = pipe(user_input)[0]["label"]
 
 # Summarize and identify sentiment button
@@ -25,7 +25,7 @@ if st.button("Identify Sentiment"):
 
 
 # Initialize the question-answering pipeline
-qa_pipe = pipeline("question-answering", model="deepset/roberta-base-squad2")
+qa_pipe = pipeline("question-answering", model="Intel/dynamic_tinybert")
 
 # Set the context and question based on sentiment
 if sentiment_label == "positive":