Update app.py

app.py

@@ -178,7 +178,7 @@ article_dict, clusters = initialize(LIMIT, USE_CACHE)
 # We call a display function and get the user input.  
 # For this its still streamlit.
 
-st.warning(f"Welcome to TopigDig!")
+st.success(f"Welcome to TopicDig!")
 st.success(f"You select the topics, we summarize the relevant news and give you a digest, plus some info to help contextualize what the machine did.  Enjoy, and remember, if a program is slow to run, it MUST be doing something interesting. 🤨")
 
 # button to refresh topics
@@ -220,17 +220,17 @@ with st.form(key='columns_in_form'):
             # happens internally but may be used differently so it isn't automatic upon digestor creation.
             # Easily turn caching off for testing.
             st.success("What you'll see:")
-            st.write("First you'll see a list of links appear below.  These are the links to the original articles being summarized for your digest, so you can get the full story if you're interested, or check the summary against the source.")
-            st.write("In a few moments, your machine-generated digest will appear below the links, and below that you'll see an approximate word count of your digest and the time in seconds that the whole process took!")
-            st.write("You'll also see a graph showing, for each article and summary, the original and summarized lengths.")
-            st.write("Finally, you will see some possible errors detected in the summaries.  This area of NLP is far from perfection and always developing.  Hopefully this is an interesting step in the path!")
+            st.info("First you'll see a list of links appear below.  These are the links to the original articles being summarized for your digest, so you can get the full story if you're interested, or check the summary against the source.")
+            st.warning("In a few moments, your machine-generated digest will appear below the links, and below that you'll see an approximate word count of your digest and the time in seconds that the whole process took!")
+            st.success("You'll also see a graph showing, for each article and summary, the original and summarized lengths.")
+            st.info("Finally, you will see some possible errors detected in the summaries.  This area of NLP is far from perfection and always developing.  Hopefully this is an interesting step in the path!")
             digestor.digest() # creates summaries and stores them associated with the digest
 
 
 
             # Get displayable digest and digest data
             outdata = digestor.build_digest()
-            
+            st.balloons()
         if len(digestor.text) == 0:
             st.write("No text to return...huh.")    
         else: