app.py

import openai
import gradio as gr
import os

openai.api_key = os.getenv('secret_token')

message_history = [{"role": "user", "content": f"You are a contrarian chatbot trained in the rhetorical exercise of dissoi logoi. I will upload an argument and you will reply only with the opposing argument. Reply only with an opposing argument to further input. If you understand, say OK."},
                   {"role": "assistant", "content": f"OK"}]

def predict(input):
    # tokenize the new input sentence
    message_history.append({"role": "user", "content": f"{input}"})

    completion = openai.ChatCompletion.create(
      model="gpt-3.5-turbo", #10x cheaper than davinci, and better. $0.002 per 1k tokens
      messages=message_history
    )
    #Just the reply:
    reply_content = completion.choices[0].message.content#.replace('```python', '<pre>').replace('```', '</pre>')

    print(reply_content)
    message_history.append({"role": "assistant", "content": f"{reply_content}"}) 
    
    # get pairs of msg["content"] from message history, skipping the pre-prompt:              here.
    response = [(message_history[i]["content"], message_history[i+1]["content"]) for i in range(2, len(message_history)-1, 2)]  # convert to tuples of list
    return response

# creates a new Blocks app and assigns it to the variable demo.
with gr.Blocks() as demo: 
    gr.Markdown(
    """
    # Dissoi Bot
    Write an argument and I will respond with a counterargument. Shorter arguments (a paragraph or less) tend to work better.
        """
    )
    # creates a new Chatbot instance and assigns it to the variable chatbot.
    chatbot = gr.Chatbot() 

    # creates a new Row component, which is a container for other components.
    with gr.Row(): 
        '''creates a new Textbox component, which is used to collect user input. 
        The show_label parameter is set to False to hide the label, 
        and the placeholder parameter is set'''
        txt = gr.Textbox(show_label=False, placeholder="Enter argument and press enter").style(container=False)
    '''
    sets the submit action of the Textbox to the predict function, 
    which takes the input from the Textbox, the chatbot instance, 
    and the state instance as arguments. 
    This function processes the input and generates a response from the chatbot, 
    which is displayed in the output area.'''
    txt.submit(predict, txt, chatbot) # submit(function, input, output)
    #txt.submit(lambda :"", None, txt)  #Sets submit action to lambda function that returns empty string 

    '''
    sets the submit action of the Textbox to a JavaScript function that returns an empty string. 
    This line is equivalent to the commented out line above, but uses a different implementation. 
    The _js parameter is used to pass a JavaScript function to the submit method.'''
    txt.submit(None, None, txt, _js="() => {''}") # No function, no input to that function, submit action to textbox is a js function that returns empty string, so it clears immediately.

demo.launch()