Update app.py

app.py

@@ -18,8 +18,6 @@ model = SentenceTransformer('all-mpnet-base-v2')
 flix_ds = load_dataset("nickmuchi/netflix-shows-mpnet-embeddings", use_auth_token=True)
 dataset_embeddings = torch.from_numpy(flix_ds["train"].to_pandas().to_numpy()).to(torch.float)
 
-embed = {'embedding': dataset_embeddings}
-
 #load cross-encoder for reranking
 cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
 
@@ -33,11 +31,11 @@ def display_df_as_table(model,top_k,score='score'):
     return df
 
 #function for generating similarity of query and netflix shows
-def semantic_search(query,embeddings,top_k):
+def semantic_search(query,top_k):
     '''Encode query and check similarity with embeddings'''
     
     question_embedding = model.encode(query, convert_to_tensor=True).cpu()
-    hits = util.semantic_search(question_embedding, embeddings['embedding'], top_k=top_k)
+    hits = util.semantic_search(question_embedding, dataset_embeddings, top_k=top_k)
     hits = hits[0]
     
     ##### Re-Ranking #####
@@ -115,7 +113,7 @@ with demo:
     sem_but = gr.Button('Search')
      
             
-    sem_but.click(semantic_search,inputs=[query,embed,top_k],outputs=[bi_output,cross_output],queue=True)
+    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)")