Update app.py

app.py

@@ -19,23 +19,26 @@ dataset = load_dataset("torileatherman/sentiment_analysis_training", split='trai
 training_df = pd.DataFrame(dataset)
 random_sample = {}
 
+# Number of articles shown
+n = 5
+
 def article_selection(sentiment):
     if sentiment == "Positive":
         predictions = positive_preds
-        top3 = predictions[0:3]
+        top3 = predictions[0:n]
         top3_result = top3[['Headline_string','Url']]
         top3_result.rename(columns = {'Headline_string':'Headlines', 'Url':'URL'})
         return top3_result
     
     elif sentiment == "Negative":
         predictions = negative_preds
-        top3 = predictions[0:3]
+        top3 = predictions[0:n]
         top3_result = top3[['Headline_string','Url']]
         top3_result.rename(columns = {'Headline_string':'Headlines', 'Url':'URL'})
         return top3_result
     else:
         predictions = neutral_preds
-        top3 = predictions[0:3]
+        top3 = predictions[0:n]
         top3_result = top3[['Headline_string','Url']]
         top3_result.rename(columns = {'Headline_string':'Headlines', 'Url':'URL'})
         return top3_result