import os
import openai

from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.callbacks.base import CallbackManager
from llama_index import (
    LLMPredictor,
    ServiceContext,
    StorageContext,
    load_index_from_storage,
)
from llama_index.llms import OpenAI
import chainlit as cl


openai.api_key = os.environ.get("OPENAI_API_KEY")

try:
    # rebuild storage context
    storage_context = StorageContext.from_defaults(persist_dir="./storage")
    # load index
    index = load_index_from_storage(storage_context)
except:
    from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader

    documents = SimpleDirectoryReader(input_files=["hitchhikers.pdf"]).load_data()
    index = GPTVectorStoreIndex.from_documents(documents)
    index.storage_context.persist()


@cl.on_chat_start
async def factory():
    llm_predictor = LLMPredictor(
        llm=OpenAI(
            temperature=0,
            model="ft:gpt-3.5-turbo-0613:personal::7sleLdbA",
            streaming=True,
            context_window=2048,
        ),
    )
    service_context = ServiceContext.from_defaults(
        llm_predictor=llm_predictor,
        chunk_size=512,
        callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]),
    )

    query_engine = index.as_query_engine(
        service_context=service_context,
        streaming=True,
    )

    cl.user_session.set("query_engine", query_engine)


@cl.on_message
async def main(message):
    query_engine = cl.user_session.get("query_engine")  # type: RetrieverQueryEngine
    response = await cl.make_async(query_engine.query)(message)

    response_message = cl.Message(content="")

    for token in response.response_gen:
        await response_message.stream_token(token=token)

    if response.response_txt:
        response_message.content = response.response_txt

    await response_message.send()