appDeepseekCoder.py

from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from prompts import maths_assistant_prompt_template
from langchain.memory.buffer import ConversationBufferMemory
from dotenv import load_dotenv
import os
import chainlit as cl
import uvicorn

# Load environment variables from .env file
load_dotenv()

api_key = os.getenv('OPENAI_API_KEY')
print(f"api key is {api_key}")

app = FastAPI()

@app.on_event("startup")
async def startup_event():
    print("Initializing llm...")
    llm = ChatOpenAI(model='gpt-4o-mini', temperature=0.5, api_key=api_key)
    print("llm initialized!")
    conversation_memory = ConversationBufferMemory(memory_key="chat_history", max_len=50, return_messages=True)
    llm_chain = LLMChain(llm=llm, prompt=maths_assistant_prompt_template, memory=conversation_memory)
    
    # Initialize Chainlit context
    cl.init()
    
    cl.user_session.set("llm_chain", llm_chain)

@app.post("/query/")
async def query_llm(request: Request):
    data = await request.json()
    message = data.get("message")
    llm_chain = cl.user_session.get("llm_chain")
    response = await llm_chain.ainvoke({
        "chat_history": llm_chain.memory.load_memory_variables({})["chat_history"],
        "question": message
    }, callbacks=[cl.AsyncLangchainCallbackHandler()])
    return JSONResponse(content={"response": response["text"]})

@cl.on_chat_start
async def on_chat_start():
    actions = [
        cl.Action(name="Probability", value="Probability", description="Select Quiz Topic!"),
        cl.Action(name="Linear Algebra", value="Linear Algebra", description="Select Quiz Topic!"),
        cl.Action(name="Accounts", value="Accounts", description="Select Quiz Topic!"),
        cl.Action(name="Calculus", value="Calculus", description="Select Quiz Topic!")
    ]
    await cl.Message(content="**Pick a Topic and Let the Quiz Adventure Begin!** 🎉📚", actions=actions).send()

@cl.action_callback("Linear Algebra")
@cl.action_callback("Probability")
@cl.action_callback("Accounts")
@cl.action_callback("Calculus")
async def on_action(action: cl.Action):
    llm_chain = cl.user_session.get("llm_chain")
    response = await llm_chain.ainvoke({
        "chat_history": llm_chain.memory.load_memory_variables({})["chat_history"],
        "question": f"Quiz me on the topic {action.value}."
    }, callbacks=[cl.AsyncLangchainCallbackHandler()])
    await cl.Message(response["text"]).send()

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)