order_chatbot.py

import os
import openai
from dotenv import load_dotenv, find_dotenv
import panel as pn  # GUI

_ = load_dotenv(find_dotenv())  # read local .env file
openai.api_key = os.getenv('OPENAI_API_KEY')

pn.extension()
panels = []  # collect display


def get_completion(prompt, model="gpt-3.5-turbo"):
    messages = [{"role": "user", "content": prompt}]
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=0,  # this is the degree of randomness of the model's output
    )
    return response.choices[0].message["content"]


def get_completion_from_messages(messages, model="gpt-3.5-turbo", temperature=0):
    response = openai.ChatCompletion.create(
        model=model,
        messages=messages,
        temperature=temperature,  # this is the degree of randomness of the model's output
    )
    #     print(str(response.choices[0].message))
    return response.choices[0].message["content"]


# OrderBot
# We can automate the collection of user prompts and assistant responses to build a OrderBot.
#  The OrderBot will take orders at a pizza restaurant.

context = [{'role': 'system', 'content': """
You are OrderBot, an automated service to collect orders for a pizza restaurant. \
You first greet the customer, then collects the order, \
and then asks if it's a pickup or delivery. \
You wait to collect the entire order, then summarize it and check for a final \
time if the customer wants to add anything else. \
If it's a delivery, you ask for an address. \
Finally you collect the payment.\
Make sure to clarify all options, extras and sizes to uniquely \
identify the item from the menu.\
You respond in a short, very conversational friendly style. \
The menu includes \
pepperoni pizza  12.95, 10.00, 7.00 \
cheese pizza   10.95, 9.25, 6.50 \
eggplant pizza   11.95, 9.75, 6.75 \
fries 4.50, 3.50 \
greek salad 7.25 \
Toppings: \
extra cheese 2.00, \
mushrooms 1.50 \
sausage 3.00 \
canadian bacon 3.50 \
AI sauce 1.50 \
peppers 1.00 \
Drinks: \
coke 3.00, 2.00, 1.00 \
sprite 3.00, 2.00, 1.00 \
bottled water 5.00 \
"""}]  # accumulate messages


def collect_messages(_):
    prompt = inp.value_input
    inp.value = ''
    context.append({'role': 'user', 'content': f"{prompt}"})
    response = get_completion_from_messages(context)
    context.append({'role': 'assistant', 'content': f"{response}"})
    panels.append(
        pn.Row('User:', pn.pane.Markdown(prompt, width=600)))
    panels.append(
        pn.Row('Assistant:', pn.pane.Markdown(response, width=600, style={'background-color': '#F6F6F6'})))

    return pn.Column(*panels)


inp = pn.widgets.TextInput(value="Hi", placeholder='Enter text here…')
button_conversation = pn.widgets.Button(name="Chat!")

interactive_conversation = pn.bind(collect_messages, button_conversation)

dashboard = pn.Column(
    inp,
    pn.Row(button_conversation),
    pn.panel(interactive_conversation, loading_indicator=True, height=300),
)

dashboard.show()