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app.py

@@ -89,11 +89,13 @@ sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1]
 indices = get_bm25_search_hits(corpus, sparse_scores, 50)
 
 
-dense_embedding = literal_eval(instructor_model.predict(
-    query_embedding_instruction,
-    query_text,
-    api_name="/predict",
-))
+dense_embedding = literal_eval(
+    instructor_model.predict(
+        query_embedding_instruction,
+        query_text,
+        api_name="/predict",
+    )
+)
 
 
 text_embedding_instructions_choice = [
@@ -136,7 +138,7 @@ with col2:
             )
             context = format_query(matches)
             output_text = format_context(context)
-        
+
             st.subheader("Retrieved Text:")
             for output in output_text:
                 output = f"""{output}"""
@@ -148,16 +150,6 @@ with col2:
 tab1 = st.tabs(["View transcript"])
 
 
-with col2:
-    st.subheader("Retrieved Text:")
-    for output in output_text:
-        output = f"""{output}"""
-        st.write(
-            f"<ul><li><p>{output}</p></li></ul>",
-            unsafe_allow_html=True,
-        )
-
-
 with tab1:
     file_text = retrieve_transcript()
     with st.expander("See Transcript"):