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app.py

@@ -94,14 +94,6 @@ with st.sidebar:
 data = get_data()
 corpus, bm25 = get_bm25_model(data)
 
-tokenized_query = preprocess_text(query_text).split()
-sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1]
-indices_hits = get_bm25_search_hits(corpus, sparse_scores, 50)
-
-if use_bm25 == True:
-    indices = indices_hits
-else:
-    indices = None
 
 if ner_choice == "Spacy":
     ner_model = get_spacy_model()
@@ -128,6 +120,16 @@ with col1:
             )
 
 
+tokenized_query = preprocess_text(query_text).split()
+sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1]
+indices_hits = get_bm25_search_hits(corpus, sparse_scores, 50)
+
+if use_bm25 == True:
+    indices = indices_hits
+else:
+    indices = None
+
+
 # Extract keywords from query
 keywords = extract_keywords(query_text, alpaca_model)
 
@@ -383,7 +385,6 @@ with st.sidebar:
         )
 
 
-
 if document_type == "Single-Document":
     if encoder_model in ["Hybrid SGPT - SPLADE", "Hybrid Instructor - SPLADE"]:
         if encoder_model == "Hybrid Instructor - SPLADE":