Update app.py

app.py

@@ -31,6 +31,15 @@ def display_df_as_table(model,top_k,score='score'):
     
     return df
 
+#load ASR model
+def asr(audio):
+
+    asr_model = whisper.load_model("base")
+    results = loaded_model.transcribe(audio)
+    query = results['text']
+    
+    return query
+    
 #function for generating similarity of query and netflix shows
 def semantic_search(query,top_k):
     '''Encode query and check similarity with embeddings'''
@@ -61,7 +70,7 @@ def semantic_search(query,top_k):
     return bi_df, cross_df
     
     
-title = """<h1 id="title">Netflix Shows Semantic Search</h1>"""
+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. 
@@ -97,7 +106,7 @@ with demo:
     
 
     with gr.Row():
-        query = gr.Textbox(lines=3,label='Describe the Netflix show or movie you would like to watch..')
+        query = gr.Audio(source='microphone',type='filepath',label='Describe the Netflix show or movie you would like to watch..')
             
     with gr.Row():