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app.py

@@ -1,6 +1,6 @@
 import numpy as np
 import streamlit_scrollable_textbox as stx
-
+from ast import literal_eval
 import pinecone
 import streamlit as st
 
@@ -89,11 +89,11 @@ sparse_scores = np.argsort(bm25.get_scores(tokenized_query), axis=0)[::-1]
 indices = get_bm25_search_hits(corpus, sparse_scores, 50)
 
 
-dense_embedding = instructor_model.predict(
+dense_embedding = literal_eval(instructor_model.predict(
     query_embedding_instruction,
     query_text,
     api_name="/predict",
-)
+))
 
 
 text_embedding_instructions_choice = [