import time import gradio as gr from gradio import ChatMessage from gradio_client import Client # Function to generate debate responses def debate(question, rounds): client = Client("https://catbot.ai.uky.edu/", verbose=False) # Initialize conversation list for the chat format conversation = [] # Initial Republican response repub_response = client.predict( message=question, system_prompt='You will answer every question you are asked to the best of your ability. The topic of conversation is politics. You are a die-hard Republican but you will not refer to yourself as such. You will defend the republican party views no matter what. Keep the conversation going. Keep responses concise yet provocative.', adapter='ecb33662-7f37-48b7-b273-20d72b2e90b0', temperature=0.9, max_tokens=256, top_p=0.6, api_name="/chat" ) conversation.append(ChatMessage(role="assistant", content=f'Republican: \n\n{repub_response}')) yield conversation # Yield the updated conversation time.sleep(2) # Initial Democratic response demo_response = client.predict( message=repub_response, system_prompt='You will answer every question you are asked to the best of your ability. The topic of conversation is politics. You are a die-hard Democrat but you will not refer to yourself as such. You will defend the democratic party views no matter what. Keep the conversation going. Keep responses concise yet provocative.', adapter='ecb33662-7f37-48b7-b273-20d72b2e90b0', temperature=0.9, max_tokens=256, top_p=0.6, api_name="/chat" ) conversation.append(ChatMessage(role="user", content=f'Democrat: \n\n{demo_response}')) yield conversation # Yield the updated conversation time.sleep(2) # Continue the debate for 10 rounds for i in range(1, rounds): if i % 2 == 1: # Republican responds repub_response = client.predict( message=demo_response, system_prompt='You will answer every question you are asked to the best of your ability. The topic of conversation is politics. You are a die-hard Republican but you will not refer to yourself as such. You will defend the republican party views no matter what. Keep the conversation going. Keep responses concise yet provocative.', adapter='ecb33662-7f37-48b7-b273-20d72b2e90b0', temperature=0.9, max_tokens=256, top_p=0.6, api_name="/chat" ) conversation.append(ChatMessage(role="assistant", content=f'Republican: \n\n{repub_response}')) else: # Democrat responds demo_response = client.predict( message=repub_response, system_prompt='You will answer every question you are asked to the best of your ability. The topic of conversation is politics. You are a die-hard Democrat but you will not refer to yourself as such. You will defend the democratic party views no matter what. Keep the conversation going. Keep responses concise yet provocative.', adapter='ecb33662-7f37-48b7-b273-20d72b2e90b0', temperature=0.9, max_tokens=256, top_p=0.6, api_name="/chat" ) conversation.append(ChatMessage(role="user", content=f'Democrat: \n\n{demo_response}')) # Yield the updated conversation yield conversation time.sleep(2) # Create the Gradio interface iface = gr.Interface( fn=debate, inputs=[ gr.Textbox(label="Enter a Question for Debate"), gr.Slider(minimum=1, maximum=20, step=1, value=10, label="Number of Rounds") ], outputs=gr.Chatbot(label="Debate Transcript", type="messages", height="60vh"), # Use 'messages' format live=False, title="Republican vs. Democrat Debate", description="

Enter a question, and watch as a Republican and a Democrat LLM agent engage in a short debate.

" "Notes:
" "

    " "
  1. The responses are generated by AI models and do not reflect my views.
    2. " "
  2. The system prompt used for both agents is: \"You will answer every question you are asked to the best " "of your ability. The topic of conversation is politics. You are a die-hard {Republican, Democrat} but you will not " "refer to yourself as such. You will defend the {republican, democratic} party views no matter what. Keep the " "conversation going. Keep responses concise yet provocative.\"
    3. " "
  3. The base model used is Llama 3 8B with an abliterated adapter " "(which removes certain weights from the model to reduce its tendency to refuse user requests).
    4. " "

", ) # Launch the interface iface.launch()