Update app.py

app.py

@@ -35,7 +35,7 @@ embedding_model = HuggingFaceEmbeddings(model_name = "all-MiniLM-L6-v2")
 
 # Returns a FAISS wrapper vector store. Input is a list of strings. from_documents method used documents to Return VectorStore
 
-data = dataset["train"]
+data = dataset["text"]
 data = data.add_faiss_index("embeddings") 
 # adds a column that has a index of embeddings