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# from QA_app.components.data_querying import user_query
from chainlit import on_chat_start, on_message, LangchainCallbackHandler
import chainlit as cl
from main_app_deploy.components.data_querying import my_query
import os
os.environ["LITERAL_API_KEY"] = os.getenv("LITERAL_API_KEY")
os.environ["LANGCHAIN_PROJECT"] = "GAME RECOMMENDATION"
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
# user_query
async def user_query_func(user_question):
response = my_query(user_question)
# Replace this with your actual logic for processing the user query
# It could involve interacting with an LLM, searching web documents, etc.
# For illustration purposes, let's just return a simple response
return response
@cl.on_chat_start
def start():
# user_query
print("Chat started!")
@cl.on_message
async def main(message: cl.Message):
# user_query
user_question = message.content
# response = user_query(user_question)
# response = await user_query_func("What happended to the birds")
response = await user_query_func(user_question)
print(user_question, "see")
# user_query = cl.make_async(user_query)
# await user_query("What happended to the birds")
# print(user_question, "see22222222")
# Use LangchainCallbackHandler to capture the final answer
# callback_handler = LangchainCallbackHandler(stream_final_answer=True)
# response = await cl.make_async(user_query)(user_question)
# response = await cl.make_async(user_query)(user_question)
# await message.reply(response)
await cl.Message(content=response).send()
# # Run the Chainlit app
# cl.run()
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