app.py

# You can find this code for Chainlit python streaming here (https://docs.chainlit.io/concepts/streaming/python)

# OpenAI Chat completion
import os
from openai import AsyncOpenAI  # importing openai for API usage
import chainlit as cl  # importing chainlit for our app
from chainlit.prompt import Prompt, PromptMessage  # importing prompt tools
from chainlit.playground.providers import ChatOpenAI  # importing ChatOpenAI tools
from dotenv import load_dotenv
load_dotenv()

# ChatOpenAI Templates
system_template = """\
###Instruction###
You are an expert assistant answering technical questions on machine learning and deep learning subject.
Ensure that your response is unbiased and generic, you will be 'AWARDED' for giving really good clarity and correct answers.
"""

user_template = """{input} + 
Think and only give code along with response, if you think giving code is necessary for any given question.
"""


@cl.on_chat_start  # marks a function that will be executed at the start of a user session
async def start_chat():
    settings = {
        "model": "gpt-3.5-turbo",
        "temperature": 0,
        "max_tokens": 500,
        "top_p": 1,
        "frequency_penalty": 0,
        "presence_penalty": 0,
    }

    cl.user_session.set("settings", settings)

@cl.on_message  # marks a function that should be run each time the chatbot receives a message from a user
async def main(message: cl.Message):
    settings = cl.user_session.get("settings")

    client = AsyncOpenAI()

    print(message.content)

    prompt = Prompt(
        provider=ChatOpenAI.id,
        messages=[
            PromptMessage(
                role="system",
                template=system_template,
                formatted=system_template,
            ),
            PromptMessage(
                role="user",
                template=user_template,
                formatted=user_template.format(input=message.content),
            ),
        ],
        inputs={"input": message.content},
        settings=settings,
    )

    print([m.to_openai() for m in prompt.messages])

    msg = cl.Message(content="")
    await msg.send()
    

    # Call OpenAI
    async for stream_resp in await client.chat.completions.create(
        messages=[m.to_openai() for m in prompt.messages], stream=True, **settings
    ):
        token = stream_resp.choices[0].delta.content
        if not token:
            token = ""
        await msg.stream_token(token)

    # Update the prompt object with the completion
    prompt.completion = msg.content
    msg.prompt = prompt

    # Send and close the message stream
    await msg.send()