from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import Runnable
from langchain.schema.runnable.config import RunnableConfig
from chainlit.input_widget import Select
import chainlit as cl
from typing import Optional

    
@cl.author_rename
def rename(orig_author: str):
    rename_dict = {"LLMMathChain": "Albert Einstein", "Chatbot": "Assistant"}
    return rename_dict.get(orig_author, orig_author)

@cl.on_chat_start
async def on_chat_start():
    
    # Sending an image with the local file path
    # elements = [
    # cl.Image(name="image1", display="inline", path="groq.jpeg")
    # ]
    settings = await cl.ChatSettings(
        [
            Select(
                id="Model",
                label="OpenAI - Model",
                values=["mixtral-8x7b-32768","llama2-70b-4096"],
                initial_index=0,
            )
        ]
    ).send()
     
    value = settings["Model"]

    await cl.Message(content="Hello there, I am Groq. How can I help you ?").send()

    model = ChatGroq(temperature=0,model_name=value,api_key="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")
    prompt = ChatPromptTemplate.from_messages(
        [
            (
                "system",
                "You're a helpful assistant",
            ),
            ("human", "{question}"),
        ]
    )
    runnable = prompt | model | StrOutputParser()
    cl.user_session.set("runnable", runnable)


@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(
        {"question": message.content},
        config=RunnableConfig(callbacks=[cl.LangchainCallbackHandler()]),
    ):
        await msg.stream_token(chunk)

    await msg.send()