hf llm

backend.py

@@ -2,7 +2,6 @@ import torch
 import os
 from transformers import AutoModelForCausalLM, GemmaTokenizerFast, TextIteratorStreamer, AutoTokenizer
 #from interface import GemmaLLMInterface
-from llama_index.core.node_parser import SentenceSplitter
 from llama_index.embeddings.instructor import InstructorEmbedding
 import gradio as gr
 from llama_index.core import Settings, ServiceContext, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, PromptTemplate, load_index_from_storage, StorageContext
@@ -78,7 +77,7 @@ Settings.num_output = 512
 Settings.context_window = 3900
 
 
-documents = SimpleDirectoryReader('data/blockchainprova.txt').load_data()
+documents = SimpleDirectoryReader('./data').load_data()
 
 nodes = SentenceSplitter(chunk_size=512, chunk_overlap=20, paragraph_separator="\n\n").get_nodes_from_documents(documents)
 # Build the vector store index from the nodes