Upload app.py

app.py

@@ -187,31 +187,8 @@ def article_sentiment(arti):
 
 st.markdown(md_intro)
 tab_news, tab_pred = st.tabs(["News Report", "Sentiment Prediction"])
-with tab_news:
-    st.markdown(md_sumstats)
-    method_col, range_col = st.columns(2)
-    with method_col: 
-        method_selection = st.selectbox("Select Method", ('Lexicon', 'Transformer'))
-    with range_col:
-        range_selection = st.selectbox("Statistics Range", ('1 day', '3 days'))
-    overall_sentiment, news_count = news_stats(news, method_selection, range_selection)
-    senti_col, count_col = st.columns(2)
-    senti_col.metric("Overall Sentiment", str(round(overall_sentiment, 3)))
-    count_col.metric("Number of News", str(news_count))
-    st.markdown(md_table)
-    date_selection = st.selectbox("Extraction Date", ('Yesterday', '2 Days Ago', '3 Days Ago'))
-    clean_news = news_table(news, date_selection, method_selection)
-    st.dataframe(data=clean_news, 
-                column_config={"Title": st.column_config.Column(width=250),
-                               "Most Positive Sentence": st.column_config.Column(width=400),
-                               "Least Positive Sentence": st.column_config.Column(width=400), 
-                               "Date": st.column_config.DateColumn(format="DD-MM-YYYY"), 
-                               "URL": st.column_config.LinkColumn("App URL", width=400)
-                })
-    st.markdown(md_notes)
-
 with tab_pred:
-    st.write("This is a sentiment prediction module.\nPlease enter your news link into the textbox.\nSentiment prediction will be returned shortly!.\nExample link: https://www.bbc.com/news/business-68610711")
+    st.write("This is a sentiment prediction module.\nPlease enter your news link into the textbox.\nSentiment prediction will be returned shortly!.\nExample link: https://www.bbc.com/news/technology-68818113")
     newslink = st.chat_input(placeholder="Please input CNN/BBC/CNBC link")
     if newslink:
         placeholder = st.empty()
@@ -245,4 +222,28 @@ with tab_pred:
         
         
         ''')
-        st.markdown(f"{user_neg_sents[2]}")
+        st.markdown(f"{user_neg_sents[2]}")
+
+
+with tab_news:
+    st.markdown(md_sumstats)
+    method_col, range_col = st.columns(2)
+    with method_col: 
+        method_selection = st.selectbox("Select Method", ('Lexicon', 'Transformer'))
+    with range_col:
+        range_selection = st.selectbox("Statistics Range", ('1 day', '3 days'))
+    overall_sentiment, news_count = news_stats(news, method_selection, range_selection)
+    senti_col, count_col = st.columns(2)
+    senti_col.metric("Overall Sentiment", str(round(overall_sentiment, 3)))
+    count_col.metric("Number of News", str(news_count))
+    st.markdown(md_table)
+    date_selection = st.selectbox("Extraction Date", ('Yesterday', '2 Days Ago', '3 Days Ago'))
+    clean_news = news_table(news, date_selection, method_selection)
+    st.dataframe(data=clean_news, 
+                column_config={"Title": st.column_config.Column(width=250),
+                               "Most Positive Sentence": st.column_config.Column(width=400),
+                               "Least Positive Sentence": st.column_config.Column(width=400), 
+                               "Date": st.column_config.DateColumn(format="DD-MM-YYYY"), 
+                               "URL": st.column_config.LinkColumn("App URL", width=400)
+                })
+    st.markdown(md_notes)