import langchain 
from langchain.llms import DeepSparse
from langchain.document_loaders import TextLoader, DirectoryLoader
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
import chainlit as cl
import os

MODEL_PATH = "hf:neuralmagic/mpt-7b-chat-pruned50-quant"
generation_config = {"max_new_tokens":200}
llm = DeepSparse(model=MODEL_PATH, generation_config=generation_config)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={'device': 'cpu'})
text_splitter = RecursiveCharacterTextSplitter(chunk_size=600, chunk_overlap=100)
DATA_PATH = "documents"

@cl.on_chat_start
async def init():
    loader = DirectoryLoader(DATA_PATH,
                             glob='*.txt',
                             loader_cls=TextLoader)
    documents = loader.load()
    texts = text_splitter.split_documents(documents)
    docsearch = Chroma.from_documents(texts, embeddings)
    chain = RetrievalQA.from_chain_type(
        llm,
        chain_type="stuff",
        return_source_documents=True,
        retriever=docsearch.as_retriever(search_kwargs={"k": 1}),
    )
    # Save the metadata and texts in the user session
    metadatas = [{"source": f"{i}-pl"} for i in range(len(texts))]
    cl.user_session.set("metadatas", metadatas)
    cl.user_session.set("texts", texts)
    cl.user_session.set("chain", chain)

@cl.on_message
async def main(message: cl.Message):
    chain = cl.user_session.get("chain")  
    cb = cl.AsyncLangchainCallbackHandler(
        stream_final_answer=True, answer_prefix_tokens=["HELPFUL", "ANSWER"]
    )
    cb.answer_reached = True
    res = await chain.acall(message.content, callbacks=[cb])

    answer = res["result"]
    source_documents = res["source_documents"]
    source_elements = []

    # Get the metadata and texts from the user session
    metadatas = cl.user_session.get("metadatas")
    all_sources = [m["source"] for m in metadatas]
    texts = cl.user_session.get("texts")

    if source_documents:
        found_sources = []

        # Add the sources to the message
        for source_idx, source in enumerate(source_documents):
            # Get the index of the source
            source_name = f"source_{source_idx}"
            found_sources.append(source_name)
            # Create the text element referenced in the message
            source_elements.append(cl.Text(content=str(source.page_content).strip(), name=source_name))

        if found_sources:
            answer += f"\nSources: {', '.join(found_sources)}"
        else:
            answer += "\nNo sources found"
            
    if cb.has_streamed_final_answer:
        cb.final_stream.content = answer
        cb.final_stream.elements = source_elements
        await cb.final_stream.update()
    else:
        await cl.Message(content=answer,
                         elements=source_elements
                        ).send()