Update app.py

app.py

@@ -10,7 +10,7 @@ import os
 #Helps us generate embeddings
 #An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. 
 #Small distances suggest high relatedness and large distances suggest low relatedness.
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.embeddings import HuggingFaceInstructEmbeddings
 
 
 #FAISS is an open-source library developed by Facebook AI Research for efficient similarity search and clustering of large-scale datasets, particularly with high-dimensional vectors. 
@@ -33,7 +33,7 @@ st.header("Hey, Ask me something & I will give out similar things")
 
 #Initialize the OpenAIEmbeddings object
 # embeddings = OpenAIEmbeddings()
-embeddings= HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+embeddings= HuggingFaceInstructEmbeddings(model_name="hku-nlp/instructor-xl")
 
 #The below snippet helps us to import CSV file data for our tasks
 from langchain.document_loaders.csv_loader import CSVLoader