chat.py

import requests
from termcolor import colored
from garmin_get_prompt import get_prompt

def get_prompt():
    # Assemble a comprehensive prompt with various components and a detailed report.
    # Note that these variables have to be populated with the data I shared in the article

    return f"""
       {PERSONA_PROMPT}
       ____________

       {CUSTOMER_DATA_PROMPT}
       {PROGRAM_PROMPT}
       {DIET_PROMPT}
       {SUPPLEMENTATION_PROMPT}
       {GENERAL_LIFESTYLE_PROMPT}

       ____________
       {DETAILED_REPORT}
    """
  


# Constants
GPT_TOKEN = "<YOUR_GPT_KEY>"
GPT_MODEL = "gpt-4-0125-preview"
HEADERS = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {GPT_TOKEN}"
}
MAX_TOKENS_CONVERSATION = 300
CONVERSATION_THRESHOLD = 4
SUMMARY_INSERTION_INDEX = 2
INITIAL_MESSAGE = "Hi I am your AI Personal Trainer, I have all your data, please ask me any questions, I'll be happy to answer!"

def generate_conversation_summary(conversation_history):
    """Generate a summary for the conversation history using GPT."""
    summary_prompt = "Summarize the conversation above, using max 200 words. Start your text with `Previous conversations had with the customer were about`"
    # Prepend summary prompt to history
    conversation_history = conversation_history + [{"role": "user", "content": summary_prompt}]
    
    # Send summary request
    summary_response = requests.post(
        "https://api.openai.com/v1/chat/completions",
        headers=HEADERS,
        json={"model": GPT_MODEL, "messages": conversation_history, "max_tokens": MAX_TOKENS_CONVERSATION}
    ).json()
    return summary_response["choices"][0]["message"]["content"]

def chat():
    """Main chat function to interact with the user."""
    conversation = [
        {"role": "system", "content": get_prompt()},
        {"role": "assistant", "content": INITIAL_MESSAGE}
    ]
    user_message_count = 0

    while True:
        user_input = input("Enter your question (or 'END' to finish): ")
        if user_input == "END":
            break

        user_message_count += 1
        conversation.append({"role": "user", "content": user_input})

        # Get response from the model
        response = requests.post(
            "https://api.openai.com/v1/chat/completions",
            headers=HEADERS,
            json={"model": GPT_MODEL, "messages": conversation, "max_tokens": MAX_TOKENS_CONVERSATION}
        ).json()
        response_text = response["choices"][0]["message"]["content"]
        print(colored("AI Response:", 'green'), colored(response_text, 'yellow'))

        conversation.append({"role": "assistant", "content": response_text})

        # Check if the number of user messages has reached the threshold for summarization.
        if user_message_count >= CONVERSATION_THRESHOLD:
            # Generate a summary for the conversation up to this point, excluding the most recent exchange.
            # This is done by passing a slice of the conversation history that excludes the first system message,
            # the initial greeting by the assistant, and the latest questions and responses.
            # The aim is to summarize the meat of the conversation, leaving out the setup and the very latest interactions.
            summary_response = generate_conversation_summary(conversation[2:-2])

            # Print the generated summary
            print(colored("Summary:", 'blue'), colored(summary_response, 'magenta'))
            
            # Insert the summary into the conversation history at a predetermined index.
            # This insertion point is chosen to ensure the summary is logically placed within the conversation,
            # providing a recap of the discussion thus far without interrupting the flow.
            # The index is pre-defined to insert the summary after the initial setup messages.
            conversation.insert(SUMMARY_INSERTION_INDEX, {"role": "system", "content": summary_response})
            
            # Reset the counter of user messages to zero.
            user_message_count = 0

            # This step keeps the initial messages, the inserted summary, and the most recent exchanges,
            # discarding older parts of the conversation. It helps in focusing on recent interactions and the latest summary,
            # preventing the conversation history from becoming overly long and unwieldy.
            conversation = conversation[:SUMMARY_INSERTION_INDEX + 1] + conversation[-2:]

if __name__ == "__main__":
    print(colored('Starting the interactive session. Type your questions or "END" to finish.', 'red'))
    chat()