import chainlit as cl
from llama_index.llms import MonsterLLM
from llama_index import VectorStoreIndex,SimpleDirectoryReader, ServiceContext

def indexing(llm,path=None):
    if path==None:
        path="data.txt"
    documents = SimpleDirectoryReader(input_files=[path]).load_data()
    print("loading done")
    service_context = ServiceContext.from_defaults(
        chunk_size=1024, llm=llm, embed_model="local:BAAI/bge-small-en-v1.5"
    )
    print("indexing")
    index = VectorStoreIndex.from_documents(documents, service_context=service_context, use_async=True)
    query_engine = index.as_query_engine()
    print("all done")
    print(query_engine)
    return query_engine

def qa(sp,engine,message):
    message=message.content
    ques=sp+" "+message
    response=engine.query(ques)
    return response

@cl.on_chat_start
async def factory():
    url = await cl.AskUserMessage(author="Beast",content="Enter url").send()
    print(url)
    if url['output'][-1]=="/":
        url['output']=url['output'].replace(".ai/",".ai")
    auth = await cl.AskUserMessage(author="Beast",content="Enter auth token").send()
    print(auth)
    model = 'deploy-llm'
    llm = MonsterLLM(model=model,base_url=url['output'],monster_api_key=auth['output'],temperature=0.75, context_window=1024)
    cl.user_session.set("llm", llm)
    # files = None
    # while files is None:
    #     files = await cl.AskFileMessage(author="Beast",
    #         content="Please upload a PDF file to begin!",
    #         accept=["application/pdf"],
    #         max_size_mb=20,
    #         timeout=180,
    #     ).send()

    # pdf = files[0]
    # print(pdf)
    res = await cl.AskActionMessage(author="Beast",
        content="Do you want to enter system prompt?",
        actions=[
            cl.Action(name="yes", value="yes", label="✅ Yes"),
            cl.Action(name="no", value="no", label="❌ No"),
        ],
    ).send()
    query_engine = await cl.make_async(indexing)(llm)
    if res and res.get("value") == "yes":
        sp = await cl.AskUserMessage(author="Beast",content="Enter system prompt").send()
        await cl.Message(author="Beast",content="Noted. Go ahead as your questions!!").send()
        cl.user_session.set("sp", sp["output"])
    else:
        await cl.Message(author="Beast",content="Okay, then you can start asking your questions!!").send()
    cl.user_session.set("engine", query_engine)
    
    
@cl.on_message
async def main(message: cl.Message):
    engine = cl.user_session.get("engine")
    llm=cl.user_session.get("llm")
    sp=cl.user_session.get("sp")
    if sp==None:
        sp=""
    if not message.elements:
        msg = cl.Message(author="Beast",content=f"Processing...", disable_feedback=False)
        await msg.send()
        response =await cl.make_async(qa)(sp,engine,message)
        print(response)
        msg.content = str(response)
        await msg.update()
    elif message.elements:
        go=True
        for file in message.elements: 
            if "pdf" in file.mime:
                pdf=file
            else:
                await cl.Message(author="Beast",content="We only support PDF for now").send()
                go=False
                break
        if go:
            msg = cl.Message(author="Beast",content=f"Processing `{pdf.name}`...")
            await msg.send()    
            query_engine = await cl.make_async(indexing)(llm,pdf.path)
            msg.content = f"`{pdf.name}` processed."
            await msg.update()
            response =await cl.make_async(qa)(sp,query_engine,message)
            print(response)
            msg.content = str(response)
            msg.author="Beast"
            await msg.send()