Update app.py

app.py

@@ -474,7 +474,7 @@ with gr.Blocks(title="Automatic Literacy and Speech Assesmen") as demo:
       some_val = gr.Label()
       text = gr.Textbox()
       phones = gr.Textbox()
-  gr.Examples(inputs=['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.'], outputs=in_text)  
+  
   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