Update app.py

app.py

@@ -20,7 +20,7 @@ vectara_instance = Vectara(
 model = CrossEncoder('vectara/hallucination_evaluation_model')
 
 # Streamlit app
-st.title('RAG-Based App')
+st.title('Fact-Checked Finance RAG-Based App Using Vectara HHEM')
 
 # Input message from the user
 message = st.text_input('Enter your message')
@@ -80,11 +80,10 @@ if st.button('Process'):
         # text_elements = []
         # docs = vectara_instance.similarity_search(message)
         # for source_idx, source_doc in enumerate(docs[:5]):
-        #     source_name = f"Source {source_idx + 1}"
         #     text_elements.append(source_doc.page_content)
 
         ans = f"{summary}\n HHEM Scores: {scores}"
         st.text(ans)
-        st.text("Sources:")
+        # st.text("Sources:")
         # for text in text_elements:
         #     st.text(text)