from langchain_community.llms import HuggingFaceEndpoint
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
import chainlit as cl
import os
repo_id = "tiiuae/falcon-7b-instruct"

# Set the token as an environment variable
os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN

llm = HuggingFaceEndpoint(
    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN
)


template = """ You're a general chatbot that can answer any thing related to enterprises.{question}
"""


@cl.on_chat_start
def main():
    # Instantiate the chain for that user session
    prompt = PromptTemplate(template=template, input_variables=["question"])
    llm_chain = LLMChain(prompt=prompt, llm=llm, verbose=True)

    # Store the chain in the user session
    cl.user_session.set("llm_chain", llm_chain)


@cl.on_message
async def main(message: cl.Message):
    # Retrieve the chain from the user session
    llm_chain = cl.user_session.get("llm_chain")  # type: LLMChain

    # Call the chain asynchronously
    res = await llm_chain.acall(message.content, callbacks=[cl.AsyncLangchainCallbackHandler()])
    await cl.Message(content=res["text"]).send()