Update app.py

app.py

@@ -10,14 +10,53 @@ st.write("Summarize long financial news and identify the sentiment to help you m
 pipe = pipeline("text-classification", model="roselyu/FinSent-XLMR-FinNews")
 
 # User input
-user_input = st.text_area("Enter a financial news article:")
+user_input = st.text_area("Enter a short financial news article:")
 
 # Summarize and identify sentiment button
-if st.button("Summarize and Identify Sentiment"):
+if st.button("Identify Sentiment"):
 
     # Analyze sentiment
     sentiment_label = pipe(user_input)[0]["label"]
 
     # Display summary and sentiment
 
-    st.write(f"Sentiment: {sentiment_label}")
+    st.write(f"Sentiment: {sentiment_label}")
+
+    
+# Initialize the question-answering pipeline
+qa_pipe = pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+# Set the context and question based on sentiment
+if sentiment_label == "positive":
+    context = user_input
+    question = "What's the good news?"
+elif sentiment_label == "negative":
+    context = user_input
+    question = "What's the issue here?"
+else:
+    context = user_input
+    question = "What's the opinion?"
+    
+ # Generate the answer
+result = qa_pipe(question=question, context=context)  
+
+# Display different buttons based on sentiment
+if sentiment_label == "positive":
+    button_label = "What's the good news?"
+elif sentiment_label == "negative":
+    button_label = "What's the issue here?"
+else:
+    button_label = "What's the opinion?"
+
+# show the answers
+if st.button(button_label):
+    # Callback logic: Display the result based on the button clicked
+    if sentiment_label == "positive":
+        st.write(f"Here's the good news: {result}") 
+    elif sentiment_label == "negative":
+        st.write(f"The issue is: {result}") 
+    else:
+        st.write(f"The opinion is: {result}") 
+
+
+