Update app.py

app.py

@@ -118,16 +118,12 @@ def bi_encoder(bi_enc,passages):
     
     #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
     bi_encoder = SentenceTransformer(bi_enc)
-    #Start the multi-process pool on all available CUDA devices
-    pool = bi_encoder.start_multi_process_pool()
 
     #Compute the embeddings using the multi-process pool
     print('encoding passages into a vector space...')
-    corpus_embeddings = bi_encoder.encode_multi_process(passages, pool)
-    print("Embeddings computed. Shape:", corpus_embeddings.shape)
+    corpus_embeddings = bi_encoder.encode(passages, convert_to_tensor=True, show_progress_bar=True)
 
-    #Optional: Stop the proccesses in the pool
-    bi_encoder.stop_multi_process_pool(pool)
+    print("Embeddings computed. Shape:", corpus_embeddings.shape)
     
     return corpus_embeddings