""" AI Debate Arena - A Gradio-based application for hosting AI model debates. This module provides a web interface for conducting debates between two AI models on any given topic, with a third model serving as an impartial analyst to evaluate the debate and determine a winner. The application supports real-time streaming responses and dynamic model selection from Hugging Face Hub. Author: Crony.io Version: 1.0.0 License: MIT Created: 2025-10-22 """ # Third-party library imports import gradio as gr from huggingface_hub import InferenceClient, list_models import time import random # ============================================================================= # CONFIGURATION CONSTANTS # ============================================================================= # Pre-configured model list for initial dropdown population # These models are known to work well for debate scenarios and are optimized # for instruction-following and conversational AI tasks INITIAL_MODELS = [ "NousResearch/Hermes-2-Theta-Llama-3-8B", "Qwen/Qwen2.5-1.5B-Instruct", "Qwen/Qwen2.5-14B-Instruct", "Qwen/Qwen2.5-3B-Instruct", "Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen3-30B-A3B-Instruct-2507", "Qwen/Qwen3-4B-Instruct-2507", "Qwen/Qwen3-Next-80B-A3B-Instruct", "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "google/gemma-2-2b-it", "google/gemma-2-9b-it", "google/gemma-3-1b-it", "meta-llama/Llama-3.1-8B-Instruct", "meta-llama/Llama-3.2-1B-Instruct", "meta-llama/Llama-3.2-3B-Instruct", "meta-llama/Meta-Llama-3-8B-Instruct", "meta-llama/Meta-Llama-3.1-8B-Instruct", "microsoft/Phi-3.5-mini-instruct", "mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mistral-7B-Instruct-v0.3", "openai/gpt-oss-20b", ] # ============================================================================= # GLOBAL STATE MANAGEMENT # ============================================================================= # Global flag to control debate execution # This allows users to stop long-running debates gracefully stop_debate = False # ============================================================================= # CLIENT FACTORY FUNCTIONS # ============================================================================= def get_client(token: str, model_name: str): """ Factory function to create a Hugging Face Inference API client. Args: token (str): Hugging Face API token for authentication model_name (str): Name of the model to use (e.g., "meta-llama/Llama-3.2-3B-Instruct") Returns: InferenceClient: Configured client instance for API calls """ return InferenceClient(token=token, model=model_name) def check_model_readiness(client, model_name: str, max_wait_time: int = 300): """ Check if a model is ready for inference, handling warmup delays. This function attempts to make a simple test request to check if the model is ready. If the model is still warming up (model_pending_deploy error), it will wait with exponential backoff before retrying. Args: client: Hugging Face InferenceClient instance model_name (str): Name of the model being checked max_wait_time (int): Maximum time to wait for model readiness (seconds) Returns: bool: True if model is ready, False if timeout exceeded Raises: Exception: If model fails to become ready within timeout period """ start_time = time.time() attempt = 0 base_delay = 5 # Start with 5 seconds while time.time() - start_time < max_wait_time: try: # Make a simple test request to check if model is ready test_messages = [{"role": "user", "content": "Hello"}] response = client.chat_completion( test_messages, max_tokens=1, temperature=0.1, stream=False ) # If we get here without exception, model is ready print(f"[INFO] Model {model_name} is ready for inference") return True except Exception as e: error_str = str(e) if "model_pending_deploy" in error_str or "model is not ready" in error_str.lower(): # Model is still warming up elapsed = time.time() - start_time remaining = max_wait_time - elapsed if remaining <= 0: raise Exception(f"Model {model_name} failed to become ready within {max_wait_time} seconds") # Calculate delay with exponential backoff and jitter delay = min(base_delay * (2 ** attempt) + random.uniform(0, 2), remaining, 30) attempt += 1 print(f"[INFO] Model {model_name} is warming up... waiting {delay:.1f}s (attempt {attempt})") time.sleep(delay) else: # Different error - model might be ready but this is a different issue print(f"[WARNING] Model {model_name} check failed with different error: {error_str}") return True # Assume ready, let the actual request handle the error raise Exception(f"Model {model_name} failed to become ready within {max_wait_time} seconds") # ============================================================================= # STREAMING AND RESPONSE HANDLING # ============================================================================= def stream_response(client, messages, debate_history, status, bot_id: int, model_name: str, stance_label: str, max_tokens: int, temperature: float, top_p: float, max_retries: int = 2): """ Stream a model response token-by-token with real-time UI updates. This function handles the streaming of AI model responses, providing real-time updates to the debate interface. It includes retry logic for failed requests and proper error handling for network issues or model failures. Args: client: Hugging Face client for API calls messages: Conversation history for the model debate_history: Current debate state (modified in-place) status: Current status message for UI display bot_id (int): Identifier for the bot (1, 2, or 3 for analyst) model_name (str): Name of the model being used stance_label (str): Position label (e.g., "IN FAVOR", "AGAINST") max_tokens (int): Maximum tokens to generate temperature (float): Sampling temperature (0.0 to 2.0) top_p (float): Nucleus sampling parameter (0.0 to 1.0) max_retries (int, optional): Maximum retry attempts. Defaults to 2. Yields: Tuple containing updated debate history, status, and UI state Note: This function modifies the debate_history list in-place and yields updates for real-time UI rendering. """ # Determine bot display title based on ID if bot_id == 1: title = "šŸŸ¦ Bot 1" elif bot_id == 2: title = "šŸŸ„ Bot 2" else: title = "šŸŸ© Analyst" attempt = 0 # Retry loop for handling failed requests while attempt <= max_retries: # Ensure a placeholder message exists for this bot in the debate history if len(debate_history) > 0 and debate_history[-1].get("metadata", {}).get("bot") == bot_id: # Update existing message placeholder debate_history[-1]["content"] = "" debate_history[-1]["metadata"]["title"] = f"{title} ({stance_label})" else: # Create new message entry for this bot debate_history.append({ "role": "assistant", "content": "", "metadata": {"title": f"{title} ({stance_label})", "bot": bot_id} }) try: response_received = False # Stream the response from the model for message in client.chat_completion( messages, max_tokens=int(max_tokens), temperature=float(temperature), top_p=float(top_p), stream=True, ): # Extract token from streaming response token = "" if message.choices and message.choices[0].delta.content: token = message.choices[0].delta.content response_received = True # Append token to current message and yield UI update if token: debate_history[-1]["content"] += token # Yield live updates and keep progress visible yield debate_history, status, gr.update(visible=True) # Success case: valid response received if response_received and debate_history[-1]["content"].strip(): return # Empty response handling - retry if attempts remaining if attempt < max_retries: print(f"[WARNING] Bot {bot_id} returned empty response, retrying... (attempt {attempt + 1}/{max_retries})") debate_history[-1]["content"] = f"[Retrying... {attempt + 1}/{max_retries}]" yield debate_history, status, gr.update(visible=True) except Exception as e: # Error handling with retry logic error_str = str(e) print(f"[ERROR] stream_response failed for Bot{bot_id}:{model_name} (attempt {attempt + 1}/{max_retries}): {repr(e)}") # Check if this is a model warmup error if "model_pending_deploy" in error_str or "model is not ready" in error_str.lower(): if attempt < max_retries: # Wait for model to warm up with exponential backoff delay = min(5 * (2 ** attempt) + random.uniform(0, 2), 30) print(f"[INFO] Model {model_name} is warming up... waiting {delay:.1f}s") debate_history[-1]["content"] = f"[Model warming up... waiting {delay:.1f}s... {attempt + 1}/{max_retries}]" yield debate_history, status, gr.update(visible=True) time.sleep(delay) else: # Final attempt failed - show specific warmup error err_text = f"Error: Model {model_name} is taking too long to warm up. Please try again in a few minutes." debate_history[-1]["content"] = err_text yield debate_history, status, gr.update(visible=False) return else: # Other types of errors if attempt >= max_retries: # Final attempt failed - show error message err_text = f"Error: ({e.__class__.__name__}) {str(e)}" debate_history[-1]["content"] = err_text yield debate_history, status, gr.update(visible=False) return # Show retry message and continue debate_history[-1]["content"] = f"[Error: {str(e)[:50]}... Retrying... {attempt + 1}/{max_retries}]" yield debate_history, status, gr.update(visible=True) attempt += 1 # All retries exhausted - show failure message if not debate_history[-1]["content"] or debate_history[-1]["content"].startswith("["): debate_history[-1]["content"] = "Sorry, I couldn't generate a response after several attempts. Please try again." yield debate_history, status, gr.update(visible=False) # ============================================================================= # MAIN DEBATE ORCHESTRATION # ============================================================================= def run_debate(topic, num_rounds, model1_name, model2_name, model3_name, favor_bot, start_bot, hf_token: gr.OAuthToken, max_tokens1, temp1, top_p1, max_tokens2, temp2, top_p2, max_tokens3, temp3, top_p3, progress=gr.Progress()): """ Main function to orchestrate the AI debate between two models with analysis. This function manages the entire debate flow including: - Client initialization and authentication - Stance assignment for each bot - Round-by-round debate execution - Real-time streaming responses - Final analysis by a third model Args: topic: The debate topic/statement num_rounds: Number of debate rounds to conduct model1_name: Name of the first debate model model2_name: Name of the second debate model model3_name: Name of the analyst model favor_bot: Which bot should argue in favor ("Bot 1" or "Bot 2") start_bot: Which bot should start the debate hf_token: Hugging Face authentication token max_tokens1, temp1, top_p1: Generation parameters for Bot 1 max_tokens2, temp2, top_p2: Generation parameters for Bot 2 max_tokens3, temp3, top_p3: Generation parameters for Analyst progress: Gradio progress bar for UI updates Yields: Tuple containing debate history, status updates, and UI state """ global stop_debate stop_debate = False # Input validation if not topic or not str(topic).strip(): yield [], "āš ļø Please enter a topic for the debate.", gr.update(visible=False) return # Initialize debate state debate_history = [] status = "šŸŽ­ **Debate started**\n\n" status += f"šŸ“Œ **Topic:** {topic}\n\n" yield debate_history, status, gr.update(visible=True) # Initialize Hugging Face clients try: token = getattr(hf_token, "token", None) if not token: yield [], "āŒ You have not logged in. Please click 'Login with Hugging Face' in the sidebar.", gr.update(visible=False) return client1 = get_client(token, model1_name) client2 = get_client(token, model2_name) client3 = get_client(token, model3_name) print(f"[INFO] Starting debate. Topic='{topic}', rounds={num_rounds}, favor_bot={favor_bot}") print(f"[INFO] Models -> Bot1: {model1_name}, Bot2: {model2_name}, Analyst: {model3_name}") except Exception as e: yield [], f"āŒ Error initializing clients: {str(e)}", gr.update(visible=False) return # Check model readiness before starting debate try: status += "šŸ”„ **Checking model readiness...**\n" yield debate_history, status, gr.update(visible=True) # Check each model with progress updates status += f"šŸ” Checking Bot 1 model ({model1_name.split('/')[-1]})...\n" yield debate_history, status, gr.update(visible=True) check_model_readiness(client1, model1_name) status += f"šŸ” Checking Bot 2 model ({model2_name.split('/')[-1]})...\n" yield debate_history, status, gr.update(visible=True) check_model_readiness(client2, model2_name) status += f"šŸ” Checking Analyst model ({model3_name.split('/')[-1]})...\n" yield debate_history, status, gr.update(visible=True) check_model_readiness(client3, model3_name) status += "āœ… **All models are ready!**\n\n" yield debate_history, status, gr.update(visible=True) except Exception as e: yield [], f"āŒ Model readiness check failed: {str(e)}", gr.update(visible=False) return # Determine debate stances based on user preference bot1_stance = "IN FAVOR" if favor_bot == "Bot 1" else "AGAINST" bot2_stance = "AGAINST" if favor_bot == "Bot 1" else "IN FAVOR" # Configure system prompts for each role system_1 = f"""You are an argumentative and direct debater. Defend the position {bot1_stance} on the topic: "{topic}". Keep your responses short (2-3 sentences). Use logical arguments and be convincing. Respond in English.""" system_2 = f"""You are an argumentative and direct debater. Defend the position {bot2_stance} on the topic: "{topic}". Keep your responses short (2-3 sentences). Use logical arguments and be convincing. Respond in English.""" system_3 = """You are an impartial and objective debate analyst. Your task is: 1. Briefly summarize the debate (2-3 sentences). 2. Analyze the arguments from each side. 3. Decide who won based on logic, evidence, persuasion, and coherence. Structure your response clearly and respond in English.""" # Execute debate rounds total_rounds = int(num_rounds) for round_num in range(total_rounds): # Check for user-initiated stop if stop_debate: status += "\n\nā¹ļø **Debate stopped by user**" yield debate_history, status, gr.update(visible=False) return # Update progress bar progress((round_num + 1) / max(total_rounds, 1), desc=f"Round {round_num + 1}/{total_rounds}") if start_bot == "Bot 1": # Bot 1 starts the round messages_1 = [{"role": "system", "content": system_1}] # Build conversation context from previous messages for msg in debate_history: if msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 2: messages_1.append({"role": "user", "content": f"Opponent says: {msg['content']}"}) elif msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 1: messages_1.append({"role": "assistant", "content": msg["content"]}) # First round gets opening statement, subsequent rounds get response prompt if round_num == 0: inicio_stance = "IN FAVOR" if bot1_stance == "IN FAVOR" else "AGAINST" messages_1.append({"role": "user", "content": f"Start the debate arguing {inicio_stance} on: {topic}"}) else: messages_1.append({"role": "user", "content": "Respond to the opponent's argument."}) status += f"šŸ¤– **Bot 1 ({'In favor' if bot1_stance=='IN FAVOR' else 'Against'} - {model1_name.split('/')[-1]})** thinking...\n" yield debate_history, status, gr.update(visible=True) # Stream Bot 1's response for _ in stream_response(client1, messages_1, debate_history, status, 1, model1_name, ('IN FAVOR' if bot1_stance=='IN FAVOR' else 'AGAINST'), max_tokens1, temp1, top_p1): yield debate_history, status, gr.update(visible=True) # Check for stop after Bot 1's turn if stop_debate: status += "\n\nā¹ļø **Debate stopped by user**" yield debate_history, status, gr.update(visible=False) return # Bot 2 responds messages_2 = [{"role": "system", "content": system_2}] # Build context excluding the most recent message (Bot 1's current response) for msg in debate_history[:-1]: if msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 1: messages_2.append({"role": "user", "content": f"Opponent says: {msg['content']}"}) elif msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 2: messages_2.append({"role": "assistant", "content": msg["content"]}) # Add the most recent message as opponent's statement messages_2.append({"role": "user", "content": f"Opponent says: {debate_history[-1]['content']}. Respond with your counterargument."}) status += f"šŸ¤– **Bot 2 ({'Against' if bot2_stance=='AGAINST' else 'In favor'} - {model2_name.split('/')[-1]})** thinking...\n" yield debate_history, status, gr.update(visible=True) # Stream Bot 2's response for _ in stream_response(client2, messages_2, debate_history, status, 2, model2_name, ('AGAINST' if bot2_stance=='AGAINST' else 'IN FAVOR'), max_tokens2, temp2, top_p2): yield debate_history, status, gr.update(visible=True) status += f"āœ… **Round {round_num + 1} completed**\n\n" yield debate_history, status, gr.update(visible=True) else: # Bot 2 starts the round (mirror logic of Bot 1 first) messages_2 = [{"role": "system", "content": system_2}] for msg in debate_history: if msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 1: messages_2.append({"role": "user", "content": f"Opponent says: {msg['content']}"}) elif msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 2: messages_2.append({"role": "assistant", "content": msg["content"]}) if round_num == 0: inicio_stance = "IN FAVOR" if bot2_stance == "IN FAVOR" else "AGAINST" messages_2.append({"role": "user", "content": f"Start the debate arguing {inicio_stance} on: {topic}"}) else: messages_2.append({"role": "user", "content": "Respond to the opponent's argument."}) status += f"šŸ¤– **Bot 2 ({'Against' if bot2_stance=='AGAINST' else 'In favor'} - {model2_name.split('/')[-1]})** thinking...\n" yield debate_history, status, gr.update(visible=True) for _ in stream_response(client2, messages_2, debate_history, status, 2, model2_name, ('AGAINST' if bot2_stance=='AGAINST' else 'IN FAVOR'), max_tokens2, temp2, top_p2): yield debate_history, status, gr.update(visible=True) if stop_debate: status += "\n\nā¹ļø **Debate stopped by user**" yield debate_history, status, gr.update(visible=False) return # Bot 1 responds messages_1 = [{"role": "system", "content": system_1}] for msg in debate_history[:-1]: if msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 2: messages_1.append({"role": "user", "content": f"Opponent says: {msg['content']}"}) elif msg["role"] == "assistant" and msg.get("metadata", {}).get("bot") == 1: messages_1.append({"role": "assistant", "content": msg["content"]}) messages_1.append({"role": "user", "content": f"Opponent says: {debate_history[-1]['content']}. Respond with your counterargument."}) status += f"šŸ¤– **Bot 1 ({'In favor' if bot1_stance=='IN FAVOR' else 'Against'} - {model1_name.split('/')[-1]})** thinking...\n" yield debate_history, status, gr.update(visible=True) for _ in stream_response(client1, messages_1, debate_history, status, 1, model1_name, ('IN FAVOR' if bot1_stance=='IN FAVOR' else 'AGAINST'), max_tokens1, temp1, top_p1): yield debate_history, status, gr.update(visible=True) status += f"āœ… **Round {round_num + 1} completed**\n\n" yield debate_history, status, gr.update(visible=True) # Debate rounds completed status += "\n\nšŸ **Debate finished**" yield debate_history, status, gr.update(visible=True) # Conduct final analysis if debate wasn't stopped if not stop_debate: status += "\n\nšŸ” **Analyzing debate with the Analyst...**" yield debate_history, status, gr.update(visible=True) # Prepare debate transcript for analysis messages_3 = [{"role": "system", "content": system_3}] debate_text = f"Topic: {topic}\n\n" for msg in debate_history: debate_text += f"{msg['metadata']['title']}: {msg['content']}\n\n" messages_3.append({"role": "user", "content": f"Analyze this debate:\n{debate_text}"}) # Stream analyst's evaluation for _ in stream_response(client3, messages_3, debate_history, status, 3, model3_name, 'Impartial', max_tokens3, temp3, top_p3): yield debate_history, status, gr.update(visible=True) status += "\n\nšŸ“œ **Analysis completed**" yield debate_history, status, gr.update(visible=False) # ============================================================================= # UI CONTROL FUNCTIONS # ============================================================================= def stop_debate_fn(): """ Stop the currently running debate. Sets the global stop flag to True, which will be checked by the main debate loop to gracefully terminate execution. Returns: Tuple containing status update and UI state change """ global stop_debate stop_debate = True return gr.update(value="ā¹ļø Debate stopped"), gr.update(visible=False) # ============================================================================= # MODEL MANAGEMENT FUNCTIONS # ============================================================================= def refresh_models(): """ Dynamically refresh the available model list from Hugging Face Hub. Fetches the top 20 most downloaded text-generation models and filters for instruction-tuned models suitable for debate scenarios. Merges with the initial static list and removes duplicates. Returns: Tuple of three gr.update objects for the three model dropdowns Note: Falls back to INITIAL_MODELS if the API call fails """ try: # Fetch top 20 text-generation models from Hugging Face Hub models = list_models(filter="text-generation", sort="downloads", direction=-1, limit=20, inference="warm") # Filter for chat/instruct models that are suitable for debates dynamic = [m.id for m in models if any(kw in m.id.lower() for kw in ["instruct", "chat", "-it", "dialog"])] # Merge with initial models, remove duplicates, and sort alphabetically all_models = sorted(set(INITIAL_MODELS + dynamic)) print(f"[INFO] Refreshed models: found {len(all_models)} models") return gr.update(choices=all_models), gr.update(choices=all_models), gr.update(choices=all_models) except Exception as e: # Fallback to initial models if API call fails print(f"[ERROR] Failed to refresh models: {repr(e)}") fallback = INITIAL_MODELS return gr.update(choices=fallback), gr.update(choices=fallback), gr.update(choices=fallback) def clear_chat(): """ Clear the debate history and reset the UI to initial state. Returns: Tuple containing empty debate history, reset status message, and hidden progress bar """ return [], "### šŸ“Š Status\n\n*Chat cleared. Start a new debate!*", gr.update(visible=False) # ============================================================================= # STYLING AND UI CONFIGURATION # ============================================================================= # Enhanced CSS for improved user experience and mobile responsiveness css = """ /* Mobile-first responsive design */ .container { max-width: 1200px; margin: auto; } @media (max-width: 768px) { .gr-form { flex-direction: column !important; } .gr-button { width: 100% !important; margin: 5px 0 !important; } .gr-chatbot { height: 400px !important; } } /* Custom message bubbles for different bot types */ .bot1 .message { background: #e3f2fd !important; border-left: 5px solid #2196F3 !important; } .bot2 .message { background: #ffebee !important; border-left: 5px solid #f44336 !important; } .analyst .message { background: #e8f5e9 !important; border-left: 5px solid #4caf50 !important; } /* Status display and information boxes */ .status-box { border: 2px solid #4CAF50; border-radius: 12px; padding: 15px; min-height: 120px; overflow-y: auto; font-family: monospace; } .info-box { background: #e3f2fd; border: 1px solid #2196F3; border-radius: 8px; padding: 12px; } """ # ============================================================================= # GRADIO USER INTERFACE DEFINITION # ============================================================================= # Main application interface using Gradio Blocks with gr.Blocks(title="šŸ¤–āš”ļø AI Debate Arena", css=css) as demo: # Authentication sidebar with gr.Sidebar(): login_btn = gr.LoginButton() # Application header and description gr.Markdown(""" # šŸ¤–āš”ļø AI Debate Arena Two AI models will debate on the topic you propose. One will argue in favor of the topic, and the other will argue against it. At the end, a third model will analyze the debate, create a simple summary, and decide who won. You can configure the generation parameters (max_tokens, temperature, top_p) for each model. """) # Information box about Hugging Face authentication with gr.Row(): gr.Markdown("""
ā„¹ļø Note: This app uses the HuggingFace Inference API. Log in with your account (button in the sidebar) to use the models.
""") # Topic input section with example topics with gr.Row(): topic_input = gr.Textbox(label="šŸŽÆ Debate Topic", placeholder="e.g., AI will replace jobs", lines=2) gr.Examples( examples=[ ["Ai will improve society"], ["Artificial intelligence will replace most human jobs"], ["Social media does more harm than good"], ["Renewable energy is the only path to sustainability"], ["Universal basic income is essential for future economies"], ["Cryptocurrency will become the dominant form of money"], ["Space colonization should be humanity's top priority"], ["Genetic editing of humans is ethically justifiable"], ["Privacy in the digital age is impossible to maintain"], ["Veganism is the most sustainable diet for the planet"], ["Democracy is the best form of government"], ["Electric vehicles will solve climate change"], ["Remote work is better than office-based work"], ["Video games have more positive than negative effects on youth"], ], inputs=topic_input ) # Model selection interface with gr.Row(): refresh_btn = gr.Button("šŸ”„ Refresh Models", variant="secondary", size="sm") with gr.Row(): # Three model dropdowns for the two debaters and analyst model1_dd = gr.Dropdown(choices=INITIAL_MODELS, value=INITIAL_MODELS[0], label="šŸ¤– Bot 1", allow_custom_value=True, scale=1) model2_dd = gr.Dropdown(choices=INITIAL_MODELS, value=INITIAL_MODELS[-1], label="šŸ¤– Bot 2", allow_custom_value=True, scale=1) model3_dd = gr.Dropdown(choices=INITIAL_MODELS, value="Qwen/Qwen2.5-7B-Instruct", label="šŸ¤– Analyst", allow_custom_value=True, scale=1) # Debate configuration controls with gr.Row(): favor_dd = gr.Radio(choices=["Bot 1", "Bot 2"], value="Bot 1", label="In Favor") start_dd = gr.Radio(choices=["Bot 1", "Bot 2"], value="Bot 1", label="Starts") rounds_input = gr.Slider(1, 10, 3, step=1, label="Rounds") # Advanced generation parameters in collapsible tabs with gr.Accordion("āš™ļø Generation Parameters", open=False): with gr.Tabs(): # Bot 1 parameters with gr.Tab("Bot 1"): max_tokens1 = gr.Slider(50, 2000, 250, step=50, label="Max Tokens") temp1 = gr.Slider(0, 2, 0.7, step=0.1, label="Temperature") top_p1 = gr.Slider(0, 1, 0.95, step=0.05, label="Top P") # Bot 2 parameters with gr.Tab("Bot 2"): max_tokens2 = gr.Slider(50, 2000, 250, step=50, label="Max Tokens") temp2 = gr.Slider(0, 2, 0.7, step=0.1, label="Temperature") top_p2 = gr.Slider(0, 1, 0.95, step=0.05, label="Top P") # Analyst parameters with gr.Tab("Analyst"): max_tokens3 = gr.Slider(50, 2000, 500, step=50, label="Max Tokens") temp3 = gr.Slider(0, 2, 0.7, step=0.1, label="Temperature") top_p3 = gr.Slider(0, 1, 0.95, step=0.05, label="Top P") # Main action buttons with gr.Row(): start_btn = gr.Button("ā–¶ļø Start Debate", variant="primary") stop_btn = gr.Button("ā¹ļø Stop", variant="stop") clear_btn = gr.Button("šŸ—‘ļø Clear Chat", variant="secondary") # Debate display and status monitoring chatbot = gr.Chatbot(label="Live Debate", height=500, type="messages", avatar_images=(None, None)) with gr.Row(): status_box = gr.Markdown("### šŸ“Š Status\n*Ready*", elem_classes="status-box") progress_bar = gr.Markdown(visible=False) # ============================================================================= # EVENT HANDLERS AND INTERACTIONS # ============================================================================= # Model refresh functionality refresh_btn.click( fn=refresh_models, outputs=[model1_dd, model2_dd, model3_dd] ) # Main debate start button with initialization and execution start_btn.click( # Initialize UI state before starting debate fn=lambda: ([], "šŸ”„ Initializing...", gr.update(visible=True)), outputs=[chatbot, status_box, progress_bar] ).then( # Execute the main debate function with all parameters fn=run_debate, inputs=[topic_input, rounds_input, model1_dd, model2_dd, model3_dd, favor_dd, start_dd, max_tokens1, temp1, top_p1, max_tokens2, temp2, top_p2, max_tokens3, temp3, top_p3], outputs=[chatbot, status_box, progress_bar] ) # Emergency stop functionality stop_btn.click( fn=stop_debate_fn, outputs=[status_box, progress_bar] ) # Clear chat and reset interface clear_btn.click( fn=clear_chat, outputs=[chatbot, status_box, progress_bar] ) # Application footer with usage tips gr.Markdown(""" --- ### šŸ’” Tips - Refresh models to see trending instruct models. - Type any HF model ID (e.g., `meta-llama/Meta-Llama-3-70B-Instruct`) thanks to `allow_custom_value`. - Mobile-friendly layout with colored bubbles. """) # ============================================================================= # APPLICATION ENTRY POINT # ============================================================================= if __name__ == "__main__": # Launch the Gradio application demo.launch()