Update app.py

app.py

@@ -35,7 +35,7 @@ h5 {font-family: "Poppins", sans-serif} body {font-size: 16px;} img {margin-bott
 class="w3-padding-large w3-white"> <div class="w3-row-padding-large"> <div class="w3-col"> <h1 
 class="w3-jumbo"><b>Bubble Check-In🐦💭</b></h1> <h1 class="w3-xxxlarge w3-text-blue"><b>Check-in-on someone's Twitter 'bubble'.</b></h1> <p><span class="w3-xlarge">Scroll down to use Bubble Check-In 1.0. ⬇ 
 </span> Bubble Check-In is a tool designed to allow you to check-in-on the type of content someone on Twitter is 
-being exposed to - be that yourself, a friend, loved one, etc. The goal here is to empower us to look out for 
+being exposed to - be that yourself, a friend, loved one, etc. The goal here is to empower users to look out for 
 each-other and identify early if someone is experiencing activity such as hate speech or extremism. We use a queue 
 system, which means <b> you may need to wait your turn to run Bubble Check-In</b>. Bubble Check-In is simple to use simply enter the username of the Twitter account you want to check-in-on and click run!</p> 
 <a href="https://www.jamesstevenson.me/cartographer-labs/"><button class="w3-button w3-light-grey w3-padding-large w3-section 
@@ -373,9 +373,11 @@ def button_pressed(text_box):
     )])
 
     # Comprise text for summary label
-    text = "A total number of {} recent tweets in @{}'s mentions and timeline were reviewed, of which @{} was exposed to {} users via mentions and " \
+    original_text = "A total number of {} recent tweets in @{}'s mentions and timeline were reviewed, of which @{} was exposed to {} users via mentions and " \
            "{} directly via following them.".format(tweets, text_box, text_box, mentions, following)
 
+    text = original_text
+    
     high_identifiers = []
     extreme_identifiers = []
 
@@ -433,6 +435,9 @@ def button_pressed(text_box):
     elif total_average_neg_sentiment > 0.9  and total_average_neg_sentiment > total_average_pos_sentiment:
         text = text + " '{} is experiencing a significantly high amount of negative sentiment content.".format(text_box)
 
+    if len(text) =- len(original_text):
+        text = text + " No excessive hate speech or low sentiment was observed in @{}'s mentions or timeline".format(text_box)
+    
     return [toxicity_plot_fig, sentiment_plot_fig, fig, text]