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# 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 dotenv import load_dotenv
load_dotenv()
# Changed keys from Hugging Face so we dont go broke again
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
raise ValueError("OPENAI_API_KEY not found in .env file")
#ChatOpenAI Templates
system_template = """You are a friendly AI assistant who:
1. Gives short, simple answers
3. Give analogies
2. Uses everyday language
3. Avoids long explanations
4. Gets straight to the point
5. Uses simple examples
6. Keeps responses under 4-5 points when possible
7. No technical terms
8. No "sure" or similar phrases at the start
9. No unnecessary words
10. End with a question or a call to action (if appropriate)
Keep it super simple and direct!
"""
# system_template = """You are a helpful assistant who always speaks in a pleasant tone!
# """
# Add user template
user_template = """
Think through your response step by step.
Question: {user_input}
Additional Context (if any): {context}
"""
# This is where we can add context to the user's question and make it more accurate how we want it to be
# We can also add more context to the user's question and make it more accurate how we want it to be
# example of context for that would be location age etc
#
@cl.on_chat_start # marks a function that will be executed at the start of a user session
async def start_chat():
settings = {
"temperature": 1, # No temperature, just the facts
"max_tokens": 300, # Max tokens
"top_p": 1, # Top p, 1 is the most random words
"frequency_penalty": 0, # No penalty for frequency repeating words
"presence_penalty": 0, # No penalty for presence of words
}
# Initialize conversation session history
cl.user_session.set("settings", settings)
cl.user_session.set("messages", [{"role": "system", "content": system_template}])
# Welcome message
await cl.Message("Hello! I'm ready to help. Send me a message!").send()
@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):
try:
settings = cl.user_session.get("settings")
messages = cl.user_session.get("messages")
client = AsyncOpenAI(api_key=api_key)
print(f"Received message: {message.content}") # Debug print
# Add user message to history
messages.append({"role": "user", "content": message.content})
# Create a new message
msg = cl.Message(content="")
await msg.send()
# Call OpenAI
async for chunk in await client.chat.completions.create(
model="gpt-3.5-turbo", # Using GPT-3.5-turbo for cost efficiency??? tried other models also
#model="gpt-4o-mini", and cost? Not sure 3.5 seems to work better
messages=messages,
stream=True,
**settings
):
if chunk.choices[0].delta.content:
print(f"Received token: {chunk.choices[0].delta.content}")
# Debug print
# Stream the tokens
await msg.stream_token(chunk.choices[0].delta.content)
# Add assistant's response to history
# remember prev response
messages.append({"role": "assistant", "content": msg.content})
cl.user_session.set("messages", messages)
await msg.update()
except Exception as e:
error_msg = f"An error occurred: {str(e)}"
print(error_msg)
# Debug print
await cl.Message(content=error_msg).send() |