chat.py

import openai
from langchain.llms import OpenAI
from langchain.chains import Continuation
from langchain.conversation import ConversationContext

# Set your OpenAI API key
openai.api_key = 'your-api-key'

# Initialize LangChain with OpenAI's GPT model
llm = OpenAI()

# Initialize a ConversationContext object
conversation_context = ConversationContext()

# Function to interpret the user's service preference
def interpret_service(user_input):
    prompt = f"The user is interested in '{user_input}'. Which service does this refer to: dancing, swimming, or sports?"
    response = llm(prompt).strip().lower()
    if response not in ['dancing', 'swimming', 'sports']:
        return None
    else:
        return response

# Function to interpret the user's time preference
def interpret_time_preference(user_input):
    prompt = f"The user mentioned '{user_input}'. Is this referring to morning, day, or night time?"
    response = llm(prompt).strip().lower()
    if response not in ['morning', 'day', 'night']:
        return None
    else:
        return response

# Function to interpret the user's setting preference
def interpret_setting(user_input):
    prompt = f"The user said '{user_input}'. Are they referring to a personal or group setting?"
    response = llm(prompt).strip().lower()
    if response not in ['personal', 'group']:
        return None
    else:
        return response

# Function to generate prompts for GPT based on the current context
def generate_prompt(conversation_context):
    prompt = "As an intelligent assistant, I'm guiding the user about our services: dancing, swimming, and sports. " \
             "I need to understand their preferences regarding service type, time (morning, day, night), " \
             "and setting (personal or group). Here is our conversation so far:\n\n"
    prompt += conversation_context.get_full_conversation()
    prompt += "\nUser: "
    return prompt

# Function to update the conversation context based on user responses
def update_context(response, conversation_context):
    service_preference = interpret_service(response)
    if service_preference:
        conversation_context.update('service', service_preference)

    time_preference = interpret_time_preference(response)
    if time_preference:
        conversation_context.update('time_preference', time_preference)

    setting_preference = interpret_setting(response)
    if setting_preference:
        conversation_context.update('group_setting', setting_preference)

# Function to manage the conversation with conditional logic
def converse(conversation_context):
    while not conversation_context.all_filled(['service', 'time_preference', 'group_setting']):
        prompt = generate_prompt(conversation_context)
        user_input = input("User: ")
        conversation_context.add_user_input(user_input)

        need_more_info = False

        service_preference = interpret_service(user_input)
        if service_preference is None:
            print("AI: I'm not sure which service you are referring to. Could you specify if it's dancing, swimming, or sports?")
            need_more_info = True
        elif service_preference:
            conversation_context.update('service', service_preference)

        if not need_more_info:
            time_preference = interpret_time_preference(user_input)
            if time_preference is None:
                print("AI: I'm not sure about the time preference you mentioned. Could you specify if it's morning, day, or night?")
                need_more_info = True
            elif time_preference:
                conversation_context.update('time_preference', time_preference)

        if not need_more_info:
            setting_preference = interpret_setting(user_input)
            if setting_preference is None:
                print("AI: Could you clarify if you prefer a personal or group setting?")
                need_more_info = True
            elif setting_preference:
                conversation_context.update('group_setting', setting_preference)

        if not need_more_info:
            continuation = Continuation(llm)
            response = continuation(prompt + user_input)
            conversation_context.add_bot_output(response)
            print("AI: ", response)
            update_context(response, conversation_context)

    print("Collected Information:", conversation_context.context)
    print("We are finding the best services for you based on your preferences.")

# Run the conversation
converse(conversation_context)