Creating description

app.py

@@ -476,7 +476,26 @@ with gr.Blocks(title="Automatic Literacy and Speech Assesmen") as demo:
       text = gr.Textbox()
       phones = gr.Textbox()
         
-  
+  gr.Markdown("""**Reading Difficulty**-  Automatically determining how difficult something is to read is a difficult task as underlying 
+                 semantics are relevant. To efficiently compute text difficulty, a Distil-Bert pre-trained model is fine-tuned for regression 
+                 using The CommonLit Ease of Readability (CLEAR) Corpus. This model scores the text on how difficult it would be for a student
+                 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 
+                  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 "Trees" 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
+              """)
+  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, 
+                  users with poor pronunciation get inaccurate results. This project attempts to score pronunciation by asking a user to read a target excerpt into the 
+                  microphone. We then pass this audio through Wave2Vec to get the inferred intended words. We measure the loss as the Levenshtein distance between the 
+                  target and actual transcripts- the Levenshtein distance between two words is the minimum number of single-character edits required to change one word 
+                  into the other.
+              """)
 
 
   grade.click(reading_difficulty, inputs=in_text, outputs=diff_output)