Update app.py

app.py

@@ -123,10 +123,10 @@ def bi_encode(bi_enc,passages):
     
     global bi_encoder
     #We use the Bi-Encoder to encode all passages, so that we can use it with sematic search
-    model = SentenceTransformer(bi_enc)
+    bi_encoder = SentenceTransformer(bi_enc)
     
     #quantize the model
-    bi_encoder = quantize_dynamic(model, {Linear, Embedding})
+    #bi_encoder = quantize_dynamic(model, {Linear, Embedding})
 
     #Compute the embeddings using the multi-process pool
     with st.spinner('Encoding passages into a vector space...'):