Update app.py

app.py

@@ -25,8 +25,9 @@ docs = splitter.create_documents(str(dataset))
 # Returns a list of documents
 
 embedding_model = HuggingFaceEmbeddings(model_name = "all-MiniLM-L6-v2")
-docs_text = [doc.text for doc in docs]
-data = FAISS.from_texts(docs_text, embedding_model)
+embed = embedding_model.embed_documents(docs)
+#docs_text = [doc.text for doc in docs]
+data = FAISS.from_embeddings(embed, embedding_model)
 #data = FAISS.from_texts(docs, embedding_model)
 
 # Returns a FAISS wrapper vector store. Input is a list of strings. from_documents method used documents to Return VectorStore