Updating diversity calculation

app.py

@@ -480,12 +480,12 @@ with gr.Blocks(title="Automatic Literacy and Speech Assesmen") as demo:
                  to understand.
               """)
   gr.Markdown("""**Lexical Diversity**-  The lexical diversity score is computed by taking the ratio of unique similar words to total similar words 
-                  squared. The similarity is computed as if the cosine similarity of the word2vec embeddings is greater than .75. It is bad writing/speech 
+                  . The similarity is computed as if the cosine similarity of the word2vec embeddings is greater than .75. It is bad writing/speech 
                   practice to repeat the same words when it's possible not to. Vocabulary diversity is generally computed by taking the ratio of unique 
                   strings/ total strings. This does not give an indication if the person has a large vocabulary or if the topic does not require a diverse 
                   vocabulary to express it. This algorithm only scores the text based on how many times a unique word was chosen for a semantic idea, e.g., 
                   "Forest" and "Woods" are 2 words to represent one semantic idea, so this would receive a 100% lexical diversity score, vs using the word
-                  "Forest" twice would yield you a 25% diversity score, (1 unique word/ 2 total words)^2
+                  "Forest" twice would yield you a 25% diversity score, (1 unique word/ 2 total words)
               """)
   gr.Markdown("""**Speech Pronunciation Scoring-**-  The Wave2Vec 2.0 model is utilized to convert audio into text in real-time. The model predicts words or phonemes
                   (smallest unit of speech distinguishing one word (or word element) from another) from the input audio from the user. Due to the nature of the model,