Call the pipeline from transformers

app.py

@@ -1,6 +1,7 @@
 import streamlit as st
 import pandas as pd
 import altair as alt
+from transformers import pipeline
 #!pip install -U sentence-transformers
 #from sentence_transformers import SentenceTransformer, util
 #from sentence_transformers import SentenceTransformer
@@ -52,7 +53,7 @@ def main():
     locations = list(df["Location Name"])
 # Query
 # Load the model
-    model = SentenceTransformer('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
+    model = pipeline('sentence-transformers/multi-qa-MiniLM-L6-cos-v1')
 
     query =  st.text_input("Enter your query: ")
 
@@ -61,7 +62,7 @@ def main():
         #age = st.number_input("Age in Years")
 #Encode query and documents
         query_emb = model.encode(query).astype(float)
-
+        
     #Compute dot score between query and all document embeddings
         scores = util.dot_score(query_emb, doc_emb.astype(float))[0].cpu().tolist()