Simple CLI chat demo

chat.py

from openai import OpenAI
from dotenv import load_dotenv
import os

# The dotenv module provides the load_dotenv() function to load environment
# variables from a .env file into os.environ.  This is particularly useful for
# sensitive information such as API keys, database passwords, etc. that you
# don't want to hard-code into your application.  In this case, we're using it
# to load the .env file.
load_dotenv()

# The os module in Python provides a way of using operating system dependent
# functionality, such as reading or writing environment variables.  Here, we're
# using the os.getenv function to retrieve the value of the OPENAI_API_KEY
# environment variable.  This function returns the value of the environment
# variable key if it exists, or None if it does not.  Storing the API key in an
# environment variable keeps it out of your source code, which is a good
# practice for security.  In this case, we're retrieving the API key for OpenAI
# that's been stored in the environment variable.
api_key = os.getenv('OPENAI_API_KEY')

# Initialize the OpenAI client
client = OpenAI()

def start_chat():
    messages = [
        {"role": "system", "content": "You are a helpful assistant."}
    ]
    return messages

def chat_with_model(messages):
    # Specify the 'stream' parameter here
    response = client.chat.completions.create(
        model="gpt-4-1106-preview",
        messages=messages,
        stream=True
    )
    # This will now be a streaming response
    return response

def main():
    welcome_message = "Welcome to the CLI Chat! Type 'quit' to exit."
    print(welcome_message)

    chat_history = start_chat()

    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            break
        
        # Append the user's message to chat history
        chat_history.append({"role": "user", "content": user_input})
        
        # Get the AI's response
        response_iter = chat_with_model(chat_history)
        
        # Since this is streaming, we iterate through the response
        response = ""
        print("AI: ", end="")
        for response_delta in response_iter:
            ai_text = response_delta.choices[0].delta.content
            if not ai_text:
                ai_text = ""
            response += ai_text
            print(f"{ai_text}", end="")

        # Append the user's message to chat history
        chat_history.append({"role": "assistant", "content": response})

        print()

if __name__ == "__main__":
    main()