Update app.py

app.py

@@ -55,8 +55,6 @@ print(dataset[2])
 
 #print(embeddings)
 
-print(updated_dataset[1])
-print(updated_dataset[2])
 print(dataset[1])
 
 embedding_dim = embedding_model.get_sentence_embedding_dimension()
@@ -66,7 +64,7 @@ embedding_dim = embedding_model.get_sentence_embedding_dimension()
 # Returns a FAISS wrapper vector store. Input is a list of strings. from_documents method used documents to Return VectorStore
 # add_embeddings
 #data = dataset["clean_text"]
-data = updated_dataset["text"]
+data = dataset
 
 #print(data)
 d = 384  # vectors dimension