from langchain_community.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import Runnable
from langchain.schema.runnable.config import RunnableConfig
import chainlit as cl
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_core.runnables.passthrough import RunnablePassthrough
from langchain_openai import ChatOpenAI

# Function to read text from a file
def read_text_file(file_path):
    try:
        with open(file_path, 'r') as file:
            content = file.read()
            return content
    except FileNotFoundError:
        return "File not found."
    except Exception as e:
        return f"An error occurred: {e}"

@cl.on_chat_start
async def on_chat_start():
    prompt = read_text_file("prompt")
    # print(prompt)

    #embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
    #vectorstore = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
    #retriever = vectorstore.as_retriever(search_kwargs={"k": 1})

    chat_model = ChatOpenAI(streaming=True, model="gpt-4")

    prompt_template = ChatPromptTemplate.from_messages(
        [
            (
                "system", prompt
            ),
            ("human", "You are looking to create notes for the chapter, {topic} to teach effectively in the class"),
        ]
    )
    parser = StrOutputParser()
    runnable_chain = (
            {
                "topic": RunnablePassthrough(),
            }
            | prompt_template
            | chat_model
            | parser
    )
    cl.user_session.set("runnable", runnable_chain)


@cl.on_message
async def on_message(message: cl.Message):
    runnable = cl.user_session.get("runnable")  # type: Runnable

    msg = cl.Message(content="")

    async for chunk in runnable.astream(
        message.content,
        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
    ):
        await msg.stream_token(chunk)

    await msg.send()