import asyncio
import json
from websockets.server import serve
import os
from langchain_chroma import Chroma
from langchain_community.embeddings import *
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_huggingface.llms import HuggingFaceEndpoint
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders import DirectoryLoader
from langchain import hub
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain.chains import create_history_aware_retriever
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
from multiprocessing import Process

if not os.path.isdir('database'):
    os.system("unzip database.zip")
retriever = None
conversational_rag_chain = None
loader = DirectoryLoader('./database', glob="./*.txt", loader_cls=TextLoader)

documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(documents)

print()
print("-------")
print("TextSplitter, DirectoryLoader")
print("-------")

persist_directory = 'db'

model_name = "BAAI/bge-large-en"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
embedding = HuggingFaceBgeEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs,
    show_progress=True,
)


async def echo(websocket):
    global retriever, conversational_rag_chain
    async for message in websocket:
        data = json.loads(message)
        if not "message" in message:
            return
        if not "token" in message:
            return
        m = data["message"]
        token = data["token"]
        docs = retriever.get_relevant_documents(m)
        response = conversational_rag_chain.invoke(
            {"input": m},
            config={
                "configurable": {"session_id": token}
            },
        )["answer"]
        await websocket.send(json.dumps({"response": response}))

async def main():
    async with serve(echo, "0.0.0.0", 7860):
        await asyncio.Future()

def f():
    asyncio.run(main())
Process(target=f).start()

vectorstore = Chroma.from_documents(documents=splits, embedding=embedding)

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

retriever = vectorstore.as_retriever()

prompt = hub.pull("rlm/rag-prompt")
llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.3")
rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

### Contextualize question ###
contextualize_q_system_prompt = """Given a chat history and the latest user question \
which might reference context in the chat history, formulate a standalone question \
which can be understood without the chat history. Do NOT answer the question, \
just reformulate it if needed and otherwise return it as is."""
contextualize_q_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", contextualize_q_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}"),
    ]
)
history_aware_retriever = create_history_aware_retriever(
    llm, retriever, contextualize_q_prompt
)


### Answer question ###
qa_system_prompt = """You are an assistant for question-answering tasks. \
Use the following pieces of retrieved context to answer the question. \
If you don't know the answer, just say that you don't know. \
Use three sentences maximum and keep the answer concise.\

{context}"""
qa_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", qa_system_prompt),
        MessagesPlaceholder("chat_history"),
        ("human", "{input}"),
    ]
)
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)

rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)


### Statefully manage chat history ###
store = {}


def get_session_history(session_id: str) -> BaseChatMessageHistory:
    if session_id not in store:
        store[session_id] = ChatMessageHistory()
    return store[session_id]


conversational_rag_chain = RunnableWithMessageHistory(
    rag_chain,
    get_session_history,
    input_messages_key="input",
    history_messages_key="chat_history",
    output_messages_key="answer",
)
"""
websocket
streamlit app ~> backend
{"token": "random", "message": "what is something"} ~> backend
backend ~> {"response": "something is something"}
streamlit app ~> display response
"""