Update app.py

app.py

@@ -20,22 +20,22 @@ dataset = load_dataset("Namitg02/Test", split='train', streaming=False)
 print(dataset)
 # Returns a list of dictionaries, each representing a row in the dataset.
 print(dataset[1])
-splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=25,separators=["\n\n"]) # ["\n\n", "\n", " ", ""])
+#splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=25,separators=["\n\n"]) # ["\n\n", "\n", " ", ""])
 
 
-docs = splitter.create_documents(str(dataset))
+#docs = splitter.create_documents(str(dataset))
 # Returns a list of documents
-print(docs)
+#print(docs)
 embedding_model = HuggingFaceEmbeddings(model_name = "all-MiniLM-L6-v2")
-docs_text = [doc.text for doc in docs]
-embed = embedding_model.embed_documents(docs_text)
+#docs_text = [doc.text for doc in docs]
+#embed = embedding_model.embed_documents(docs_text)
 
-data = FAISS.from_embeddings(embed, embedding_model)
+#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
 
-#data = dataset["train"]
+data = dataset["train"]
 data = data.add_faiss_index("embeddings") 
 # adds a column that has a index of embeddings