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	# Import necessary libraries
	import streamlit as st
	from streamlit_chat import message
	import tempfile
	from langchain.document_loaders.csv_loader import CSVLoader
	from langchain.embeddings import HuggingFaceEmbeddings
	from langchain.vectorstores import FAISS
	from langchain.llms import CTransformers
	from langchain.chains import ConversationalRetrievalChain

	import sys
	sys.path.append(r"vectorstore/db_faiss")
	import dataset_utils

	# Define the path for generated embeddings
	DB_FAISS_PATH = 'vectorstore/db_faiss'

	# Load the model of choice
	def load_llm():
	llm = CTransformers(
	model="meta-llama/Llama-2-7b",
	model_type="llama",
	max_new_tokens=512,
	temperature=0.5
	)
	return llm

	# Set the title for the Streamlit app
	st.title("Llama2 Chat CSV - 🦜🦙")

	# Create a file uploader in the sidebar
	uploaded_file = st.sidebar.file_uploader("Upload File", type="csv")

	# Handle file upload
	if uploaded_file:
	with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
	tmp_file.write(uploaded_file.getvalue())
	tmp_file_path = tmp_file.name

	# Load CSV data using CSVLoader
	loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
	data = loader.load()

	# Create embeddings using Sentence Transformers
	embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})

	# Create a FAISS vector store and save embeddings
	db = FAISS.from_documents(data, embeddings)
	db.save_local(DB_FAISS_PATH)

	# Load the language model
	llm = load_llm()

	# Create a conversational chain
	chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())

	# Function for conversational chat
	def conversational_chat(query):
	result = chain({"question": query, "chat_history": st.session_state['history']})
	st.session_state['history'].append((query, result["answer"]))
	return result["answer"]

	# Initialize chat history
	if 'history' not in st.session_state:
	st.session_state['history'] = []

	# Initialize messages
	if 'generated' not in st.session_state:
	st.session_state['generated'] = ["Hello ! Ask me(LLAMA2) about " + uploaded_file.name + " 🤗"]

	if 'past' not in st.session_state:
	st.session_state['past'] = ["Hey ! 👋"]

	# Create containers for chat history and user input
	response_container = st.container()
	container = st.container()

	# User input form
	with container:
	with st.form(key='my_form', clear_on_submit=True):
	user_input = st.text_input("Query:", placeholder="Talk to csv data 👉 (:", key='input')
	submit_button = st.form_submit_button(label='Send')

	if submit_button and user_input:
	output = conversational_chat(user_input)
	st.session_state['past'].append(user_input)
	st.session_state['generated'].append(output)

	# Display chat history
	if st.session_state['generated']:
	with response_container:
	for i in range(len(st.session_state['generated'])):
	message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
	message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")