Update app.py

app.py

@@ -87,7 +87,9 @@ def semantic_search(query,top_k):
 title = """<h1 id="title">Voice Activated Netflix Shows Semantic Search</h1>"""
 
 description = """
-Semantic Search is a way to generate search results based on the actual meaning of the query instead of a standard keyword search. I believe this way of searching provides more meaning results when trying to find a good show to watch on Netflix. For example, one could search for "Success, rags to riches story" as provided in the example below to generate shows or movies with a description that is semantically similar to the query. 
+Semantic Search is a way to generate search results based on the actual meaning of the query instead of a standard keyword search. I believe this way of searching provides more meaning results when trying to find a good show to watch on Netflix. For example, one could say "Success, rags to riches story" as provided in the example below to generate shows or movies with a description that is semantically similar to the query. 
+
+The app uses OpenAI's SOTA ASR model, [Whisper](https://huggingface.co/spaces/openai/whisper), to convert speech to text. 
 
 - The App generates embeddings using [All-Mpnet-Base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) model from Sentence Transformers. 
 - The model encodes the query and the discerption field from the [Netflix-Shows](https://huggingface.co/datasets/hugginglearners/netflix-shows) dataset which contains 8800 shows and movies currently on Netflix scraped from the web using Selenium. 
@@ -117,7 +119,7 @@ with demo:
         gr.Markdown(description)
         gr.Markdown(twitter_link)
         
-        top_k = gr.Slider(minimum=3,maximum=10,value=5,step=1,label='Number of Suggestions to Generate')
+        top_k = gr.Slider(minimum=3,maximum=10,value=3,step=1,label='Number of Suggestions to Generate')
         
     
         with gr.Row():