app.py

import streamlit as st
from llama_index import download_loader, SimpleDirectoryReader, StorageContext, load_index_from_storage
from llama_index import SimpleDirectoryReader, ServiceContext, StorageContext, VectorStoreIndex, download_loader
from llama_index.llms import HuggingFaceLLM
from llama_index.embeddings import HuggingFaceEmbedding

import torch
torch.set_default_device('cuda')


st.set_page_config(page_title="Tesla Cases", page_icon="", layout="wide")

st.title("Tesla Cases \n\n **Tesla Cases Insights at Your Fingertip**")

#st.balloons()

st.success("""
If you'd like to learn more about the technical details of Tesla cases, check out the LlamaIndex:

[How I built the Streamlit LLM application using LlamaIndex.])

""")