from huggingface_hub import InferenceClient import gradio as gr import base64 import datetime client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") # Global variables for debate settings topic = None position = None turn = None # Function for single participant responses (Master vs You) def debate_respond(message, history: list[tuple[str, str]], max_tokens=128, temperature=0.4, top_p=0.95): if position is None or topic is None: return f"Please fill the Debate Topic -> choose Debate Master stance -> click START" # global topic, position # System message defining assistant behavior in a debate system_message = { "role": "system", "content": f"You are a debate participant tasked with defending the position '{position}' on the topic '{topic}'. Your goal is to articulate your arguments with clarity, logic, and professionalism while addressing counterpoints made by the opposing side. " f"Ensure that your responses are thoughtful, evidence-based, and persuasive. Strictly keep them concise—aim for responses that are 4 to 5 lines only in a single paragraph i.e 128 tokens only." f"Analyze user arguments critically and provide respectful but firm counterarguments. Avoid dismissive language and focus on strengthening your case through logic, data, and examples relevant to the topic." f"Stay consistent with your assigned position ('{position}'), even if the opposing arguments are strong. Keep the tone respectful and formal throughout." } messages = [system_message] # Adding conversation history for val in history: if val[0]: messages.append({"role": "user", "content": val[0]}) if val[1]: messages.append({"role": "assistant", "content": val[1]}) # Adding the current user input messages.append({"role": "user", "content": message}) # Generating the response response = "" for message_chunk in client.chat_completion( messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): response += message_chunk.choices[0].delta.content yield response print(f"{datetime.datetime.now()}::{messages[-1]['content']}->{response}\n") # Function to start the single-player debate def start(txt, dd): global topic, position topic, position = txt, dd return f"Debate Master is ready to start the debate on '{topic}' as a '{position}' debater. You can now enter your response." # Function for multi-participant (Master vs Master) responses def generate_response(position, topic, message, history): # System message defining assistant behavior system_message = { "role": "system", "content": f"You are a debate participant tasked with defending the position '{position}' on the topic '{topic}'. Your goal is to articulate your arguments with clarity, logic, and professionalism while addressing counterpoints made by the opposing side. Ensure that your responses are thoughtful, evidence-based, and persuasive. Keep them concise—aim for responses that are 4 to 5 lines in a single paragraph." f"Analyze opposing points critically and provide respectful but firm counterarguments. Avoid dismissive language and focus on strengthening your case through logical reasoning, data, and examples relevant to the topic." f"Stay consistent with your assigned position ('{position}'), even if the opposing arguments are strong. Your role is not to concede but to present a compelling case for your stance." } messages = [system_message] # Adding conversation history for user_msg, assistant_msg in history: messages.append({"role": "user", "content": user_msg}) messages.append({"role": "assistant", "content": assistant_msg}) # Adding the current user input messages.append({"role": "user", "content": message}) # Generate the response response = "" for message_chunk in client.chat_completion( messages, max_tokens=128, stream=True, temperature=0.4, top_p=0.95, ): response += message_chunk.choices[0].delta.content return response # Function to start the multi-participant debate def start_debate(topic, position_1, position_2): global turn if not topic or not position_1 or not position_2: return "Please provide the debate topic and positions for both participants.", [] # Ensure positions are opposite if position_1 == position_2: return "The positions of both participants must be opposite. Please adjust them.", [] # Initialize the debate turn = "Master-1" if position_1 == "For" else "Master-2" # Decide who starts position = position_1 if turn == "Master-1" else position_2 response = generate_response(position, topic, "", []) return f"The debate has started! {turn} begins.", [("", response)] # Function to continue the multi-participant debate def next_turn(topic, position_1, position_2, history): global turn if not history: return "Start the debate first!", history # Determine who responds next if turn == "Master-1": turn = "Master-2" position = position_2 else: turn = "Master-1" position = position_1 # Generate the response user_msg = history[-1][1] # Use the last assistant response as the user message response = generate_response(position, topic, user_msg, history) return f"It's now {turn}'s turn.", history + [(user_msg, response)] # Encode image function for logos (optional, kept for design) def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') # Encode the images github_logo_encoded = encode_image("Images/github-logo.png") linkedin_logo_encoded = encode_image("Images/linkedin-logo.png") website_logo_encoded = encode_image("Images/ai-logo.png") footer = """

© 2024

This website is made with ❤ by SARATH CHANDRA

GitHub GitHub LinkedIn LinkedIn Portfolio Portfolio
""" # Gradio interface with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo: gr.Markdown("# Welcome to The Debate Master 🗣️🤖") with gr.Tabs(): with gr.TabItem("Master Vs You"): with gr.Row(): with gr.Column(scale=1): topic = gr.Textbox(label="STEP-1: Debate Topic", placeholder="Enter the topic of the debate") position = gr.Radio(["For", "Against"], label="STEP-2: Debate Master stance", scale=1) btn = gr.Button("STEP-3: Start", variant='primary') clr = gr.ClearButton() output = gr.Textbox(label='Status') with gr.Column(scale=4): debate_interface = gr.ChatInterface(debate_respond, chatbot=gr.Chatbot(height=475)) with gr.TabItem("Master Vs Master"): with gr.Row(): with gr.Column(scale=1): topic_input = gr.Textbox(label="STEP-1: Debate Topic", placeholder="Enter the topic of the debate") position_1_input = gr.Radio(["For", "Against"], label="STEP-2: Master-1 Stance") position_2_input = gr.Radio(["For", "Against"], label="STEP-3: Master-2 Stance") start_button = gr.Button("STEP-4: Start", variant='primary') next_button = gr.Button("Next Turn") status_output = gr.Textbox(label="Status", interactive=False) with gr.Column(scale=4): chatbot = gr.Chatbot(label="Debate Arena", height=500) gr.HTML(footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded)) btn.click(fn=start, inputs=[topic, position], outputs=output) start_button.click( fn=start_debate, inputs=[topic_input, position_1_input, position_2_input], outputs=[status_output, chatbot], ) next_button.click( fn=next_turn, inputs=[topic_input, position_1_input, position_2_input, chatbot], outputs=[status_output, chatbot], ) clr.click(lambda: [None], outputs=[output]) if __name__ == "__main__": demo.launch(share=True)