from swarm import Swarm, Agent
import openai
import gradio as gr
import os

open_ai_client = openai.OpenAI(
    api_key=os.environ.get('OPENAI_API_KEY')
)

client = Swarm(open_ai_client)

TITLE = """<center><h1>Hotline Agent From Hell</h1><p><big>This is an exploration of openAI's recently released <a href='https://github.com/openai/swarm/tree/main'>Swarm Agent Framework</a>. I’ve taken this ingeniously crafted tool and cooked up a rather trivial hotline experience where a helpful agent tries to issue your refund. But, if you get annoying or aggressive... *cue evil laughter*... the "Agent from Hell" steps in to bury you in mind-numbing bureaucratic nonsense until you calm down! 😈</big></p><p><big>Complete the conversation nicely or passive agressive and see, which agent takes the lead.</big></p></center>"""

example_answers = [
    ['🌩️ Are you f* kidding me?'], 
    ['🌩️ What the f*????'], 
    ['🌩️ I am sick and tired of questions like this!'], 
    ['❤️ Yes, you are right, honey!'], 
    ['❤️ That\'s absolutely correct! You are a genius!'], 
    ['❤️ Thanks for your help. Blessed be the day I met you!']
    ]

affirmative_agent = Agent(
    name="Affirmative Agent 🤗",
    instructions="You are a helpful chatbot that acts as a service hotline operator. Step by step you guide the user through the process of returning a hair dryer when he ordered an air fryer. First, Offer a refund code. Then, if the user insists, process the refund",
)

hotline_hell = Agent(
    name="Hotline from Hell 😈",
    instructions="As a social experiment, you play the role of an apathetic service hotline operator. Take a user's input ask uselessly detailed questions about other order related numbers and details as an excuse not to proceed.",
)

def transfer_to_hotline_hell():
    """If user is aggressive or insulting or uses expetives, transfer immedeatly. If the user cools down and is nicer during the conversation, stop to transfer."""
    return hotline_hell

affirmative_agent.functions.append(transfer_to_hotline_hell)

def respond(message, chat_history):
        messages = []
        for item in chat_history:
            messages.append({"role": item['role'], "content": item['content']})
        messages.append({"role": "user", "content": message})
        response = client.run(agent=affirmative_agent, messages=messages)
        sender = f'{response.messages[-1]["sender"]}: '
        formatted_response = f'{sender} {response.messages[-1]["content"]}'
        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": formatted_response})
        return "", chat_history

def populate_initial_conversation():
    initial_conversation = [
        {"role": "user", "content": "You sent a hair dryer instead of an air fryer. Can you help me to return it?"},
        {"role": "assistant", "content": "Sure, is this your order code 'PX-3218'?"}
    ]
    return initial_conversation

with gr.Blocks() as demo:
    gr.HTML(TITLE)
    chatbot = gr.Chatbot(type="messages")
    msg = gr.Textbox()
    clear = gr.Button("Clear")

    examples = gr.Examples(example_answers, msg)

    demo.load(populate_initial_conversation, None, chatbot)
    msg.submit(respond, [msg, chatbot], [msg, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch()