Update app.py

app.py

@@ -60,7 +60,7 @@ def initialize(limit, rando, use_cache=True):
         cluster_data.append(c_data)
         cluster_meta.append(article_meta(data_source.source_name, c_meta))
         st.session_state[data_source.source_name] = f"Number of articles from source: {c_meta}"
-    st.write("Finished acquiring current headlines....\nmoving on to clustering...")
+
     cluster_data = cluster_data[0] + cluster_data[1]
     # NER
     # iterate the list of namedtuples, 
@@ -98,7 +98,6 @@ def perform_ner(tup:namedtuple('article',['link','hed','entities', 'source']), c
 
 
 def ner_query(payload):
-    print("making a query....")
     data = json.dumps(payload)
     response = requests.request("POST", NER_API_URL, headers=headers, data=data)
     return json.loads(response.content.decode("utf-8"))
@@ -163,7 +162,7 @@ LIMIT = 20 # Controls time and number of clusters.
 USE_CACHE = True
 
 if not USE_CACHE:
-    print("NOT USING CACHE--ARE YOU GATHERING DATA?")
+    print("NOT USING CACHE")
 if LIMIT is not None:
     print(f"LIMIT: {LIMIT}")
 
@@ -201,9 +200,9 @@ with st.form(key='columns_in_form'):
         selections.append(col.selectbox(f'Make a Selection', choices, key=i))
     submitted = st.form_submit_button('Submit')
     if submitted:
-        st.write("Submitted")
+        st.write("Submitted.\nWhat you'll see:\n\t•The links of the articles being summarized for you digest.\n\t•The digest.\n\t•A graph showing the reduction in articles lengths from original to summary, for each article.\n\t•Some probably issues with the summary.")
         selections = [i for i in selections if i is not None]
-        with st.spinner(text="Digesting...please wait, this will take a few moments...Maybe check some messages or start reading the latest papers on summarization with transformers...."):
+        with st.spinner(text="Digesting...please wait, this will take a few moments...Maybe check some messages or start reading the latest papers on summarization with transformers....\n\nTransformers are so called because they make heavy use of mathematical transformations on the input data, in order to detect contextually related words or phrases in a sentence. \nThis project uses a checkpoint called distilbart-cnn-12-6, created by Sam Shleifer ()."):
             chosen = []
             
             for i in selections: # i is supposed to be a list of stubs, mostly one