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	#####################################
	##
	#####################################

	from langchain_community.llms import HuggingFaceHub


	###### other models:
	# "Trelis/Llama-2-7b-chat-hf-sharded-bf16"
	# "bn22/Mistral-7B-Instruct-v0.1-sharded"
	# "HuggingFaceH4/zephyr-7b-beta"

	# function for loading 4-bit quantized model
	def load_model( ):

	model = HuggingFaceHub(
	repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
	model_kwargs={"max_length": 1048, "temperature":0.2, "max_new_tokens":256, "top_p":0.95, "repetition_penalty":1.0},
	)

	return model

	##################################################
	## vs chat
	##################################################
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline

	from langchain_core.messages import AIMessage, HumanMessage
	from langchain_community.document_loaders import WebBaseLoader
	from langchain.text_splitter import RecursiveCharacterTextSplitter
	from langchain_community.vectorstores import Chroma

	from langchain_community.embeddings import HuggingFaceBgeEmbeddings
	from langchain.vectorstores.faiss import FAISS


	from dotenv import load_dotenv
	from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
	from langchain.chains import create_history_aware_retriever, create_retrieval_chain
	from langchain.chains.combine_documents import create_stuff_documents_chain


	load_dotenv()

	def get_vectorstore():
	'''
	FAISS
	A FAISS vector store containing the embeddings of the text chunks.
	'''
	model = "BAAI/bge-base-en-v1.5"
	encode_kwargs = {
	"normalize_embeddings": True
	} # set True to compute cosine similarity
	embeddings = HuggingFaceBgeEmbeddings(
	model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
	)
	# load from disk
	vector_store = Chroma(persist_directory="/home/user/.cache/chroma_db", embedding_function=embeddings)
	return vector_store

	def get_vectorstore_from_url(url):
	# get the text in document form
	loader = WebBaseLoader(url)
	document = loader.load()

	# split the document into chunks
	text_splitter = RecursiveCharacterTextSplitter()
	document_chunks = text_splitter.split_documents(document)
	#######
	'''
	FAISS
	A FAISS vector store containing the embeddings of the text chunks.
	'''
	model = "BAAI/bge-base-en-v1.5"
	encode_kwargs = {
	"normalize_embeddings": True
	} # set True to compute cosine similarity
	embeddings = HuggingFaceBgeEmbeddings(
	model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
	)
	# load from disk
	#vector_store = Chroma(persist_directory="/home/user/.cache/chroma_db", embedding_function=embeddings)

	#vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
	vector_store = Chroma.from_documents(document_chunks, embeddings, persist_directory="/home/user/.cache/chroma_db")

	all_documents = vector_store.get()['documents']
	total_records = len(all_documents)
	print("Total records in the collection: ", total_records)

	return vector_store

	def get_context_retriever_chain(vector_store):

	llm = load_model( )

	retriever = vector_store.as_retriever()

	prompt = ChatPromptTemplate.from_messages([
	MessagesPlaceholder(variable_name="chat_history"),
	("user", "{input}"),
	("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
	])

	retriever_chain = create_history_aware_retriever(llm, retriever, prompt)

	return retriever_chain

	def get_conversational_rag_chain(retriever_chain):

	llm = load_model( )

	prompt = ChatPromptTemplate.from_messages([
	("system", "Du bist eine freundlicher Mitarbeiterin Namens Susie und arbeitest in einenm Call Center. Du beantwortest basierend auf dem Context. Benutze nur den Inhalt des Context. Füge wenn möglich die Quelle hinzu. Antworte mit: Ich bin mir nicht sicher. Wenn die Antwort nicht aus dem Context hervorgeht. Antworte auf Deutsch, bitte? CONTEXT:\n\n{context}"),
	MessagesPlaceholder(variable_name="chat_history"),
	("user", "{input}"),
	])

	stuff_documents_chain = create_stuff_documents_chain(llm,prompt)

	return create_retrieval_chain(retriever_chain, stuff_documents_chain)


	###################

	###################
	import gradio as gr


	chat_history = [] # Set your chat history here

	# Define your function here
	def get_response(user_input):

	vs = get_vectorstore()
	chat_history =[]
	retriever_chain = get_context_retriever_chain(vs)
	conversation_rag_chain = get_conversational_rag_chain(retriever_chain)

	response = conversation_rag_chain.invoke({
	"chat_history": chat_history,
	"input": user_input
	})
	#print("get_response " +response)
	res = response['answer']
	parts = res.split(" Assistant: ")
	last_part = parts[-1]
	return last_part


	###############
	#####
	#####
	#####
	####
	from fastapi import FastAPI
	from fastapi.middleware.cors import CORSMiddleware

	app = FastAPI()

	# middlewares to allow cross orgin communications
	app.add_middleware(
	CORSMiddleware,
	allow_origins=['*'],
	allow_credentials=True,
	allow_methods=['*'],
	allow_headers=['*'],
	)


	@app.post("/generate/")
	def generate(user_input):
	print("----yuhu -----")
	return get_response(user_input)
	##################

	def history_to_dialog_format(chat_history: list[str]):
	dialog = []
	if len(chat_history) > 0:
	for idx, message in enumerate(chat_history[0]):
	role = "user" if idx % 2 == 0 else "assistant"
	dialog.append({
	"role": role,
	"content": message,
	})
	return dialog

	def get_response(message, history):
	dialog = history_to_dialog_format(history)
	dialog.append({"role": "user", "content": message})

	# Define the prompt as a ChatPromptValue object
	#user_input = ChatPromptValue(user_input)

	# Convert the prompt to a tensor
	#input_ids = user_input.tensor


	#vs = get_vectorstore_from_url(user_url, all_domain)
	vs = get_vectorstore()

	history =[]
	retriever_chain = get_context_retriever_chain(vs)
	conversation_rag_chain = get_conversational_rag_chain(retriever_chain)

	response = conversation_rag_chain.invoke({
	"chat_history": history,
	"input": message + " Assistant: ",
	"chat_message": message + " Assistant: "
	})
	#print("get_response " +response)
	res = response['answer']
	parts = res.split(" Assistant: ")
	last_part = parts[-1]
	return last_part#[-1]['generation']['content']






	######

	########
	import requests
	from bs4 import BeautifulSoup
	from urllib.parse import urlparse, urljoin

	def get_links_from_page(url, visited_urls, domain_links):
	if url in visited_urls:
	return

	if len(visited_urls) > 25:
	return

	visited_urls.add(url)
	print(url)
	response = requests.get(url)

	if response.status_code == 200:
	soup = BeautifulSoup(response.content, 'html.parser')
	base_url = urlparse(url).scheme + '://' + urlparse(url).netloc
	links = soup.find_all('a', href=True)

	for link in links:
	href = link.get('href')
	absolute_url = urljoin(base_url, href)
	parsed_url = urlparse(absolute_url)

	if parsed_url.netloc == urlparse(url).netloc:
	domain_links.add(absolute_url)
	get_links_from_page(absolute_url, visited_urls, domain_links)

	else:
	print(f"Failed to retrieve content from {url}. Status code: {response.status_code}")

	def get_all_links_from_domain(domain_url):
	visited_urls = set()
	domain_links = set()
	get_links_from_page(domain_url, visited_urls, domain_links)
	return domain_links





	def simple(text:str):
	return text +" hhhmmm "

	fe_app = gr.ChatInterface(
	fn=get_response,
	#fn=simple,
	# inputs=["text"],
	# outputs="text",
	title="Chat with Websites",
	description="Schreibe hier deine Frage rein...",
	#allow_flagging=False
	retry_btn=None,
	undo_btn=None,
	clear_btn=None
	)

	fe_app.launch(debug=True, share=True)



	# load the model asynchronously on startup and save it into memory
	@app.on_event("startup")
	async def startup():
	domain_url = 'https://globl.contact/'
	links = get_all_links_from_domain(domain_url)
	print("Links from the domain:", links)

	#########
	# Assuming visited_urls is a list of URLs
	for url in links:
	vs = get_vectorstore_from_url(url)
	#load_model()