Update app.py

app.py

@@ -9,6 +9,9 @@ import pathlib
 #Get the netflix dataset
 netflix = load_dataset('hugginglearners/netflix-shows',use_auth_token=True)
 
+#load ASR model
+asr_model = whisper.load_model("small")
+
 #Filter for relevant columns and convert to pandas
 netflix_df = netflix['train'].to_pandas()
 netflix_df = netflix_df[['type','title','country','description','release_year','rating','duration','listed_in','cast']]
@@ -32,15 +35,24 @@ def display_df_as_table(model,top_k,score='score'):
     df.drop('Text',inplace=True,axis=1)
     
     return df
-
-#load ASR model
+ 
+#function for transcribing audio inputs
 def asr(audio):
-
-    asr_model = whisper.load_model("small")
     results = asr_model.transcribe(audio)
     query = results['text']
     
     return query
+
+#load ASR model
+def asr_inputs(audio, upload):
+    
+    if audio:
+        query = asr(audio)
+        
+    elif upload:
+        query = asr(upload)
+    
+    return query
     
 #function for generating similarity of query and netflix shows
 def semantic_search(query,top_k):
@@ -132,7 +144,7 @@ with demo:
         
         sem_but = gr.Button('Search')
          
-        btn.click(asr, inputs=[audio], outputs=[query])        
+        btn.click(asr_inputs, inputs=[audio,audio_file], outputs=[query])        
         sem_but.click(semantic_search,inputs=[query,top_k],outputs=[bi_output,cross_output],queue=True)
     
         gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=nickmuchi-netflix-shows-semantic-search)")