app_experimental.py

import os
import gradio as gr
from typing import List, Dict, Callable
import random
import google.generativeai as genai
from anthropic import Anthropic
import openai
from openai import OpenAI  # Add explicit OpenAI import

def get_all_models():
    """Get all available models from the registries."""
    return [
        "SambaNova: Meta-Llama-3.2-1B-Instruct",
        "SambaNova: Meta-Llama-3.2-3B-Instruct",
        "SambaNova: Llama-3.2-11B-Vision-Instruct",
        "SambaNova: Llama-3.2-90B-Vision-Instruct",
        "SambaNova: Meta-Llama-3.1-8B-Instruct",
        "SambaNova: Meta-Llama-3.1-70B-Instruct",
        "SambaNova: Meta-Llama-3.1-405B-Instruct",
        "Hyperbolic: Qwen/Qwen2.5-Coder-32B-Instruct",
        "Hyperbolic: meta-llama/Llama-3.2-3B-Instruct",
        "Hyperbolic: meta-llama/Meta-Llama-3.1-8B-Instruct",
        "Hyperbolic: meta-llama/Meta-Llama-3.1-70B-Instruct",
        "Hyperbolic: meta-llama/Meta-Llama-3-70B-Instruct",
        "Hyperbolic: NousResearch/Hermes-3-Llama-3.1-70B",
        "Hyperbolic: Qwen/Qwen2.5-72B-Instruct",
        "Hyperbolic: deepseek-ai/DeepSeek-V2.5",
        "Hyperbolic: meta-llama/Meta-Llama-3.1-405B-Instruct",
    ]

def generate_discussion_prompt(original_question: str, previous_responses: List[str]) -> str:
    """Generate a prompt for models to discuss and build upon previous responses."""
    prompt = f"""You are participating in a multi-AI discussion about this question: "{original_question}"

Previous responses from other AI models:
{chr(10).join(f"- {response}" for response in previous_responses)}

Please provide your perspective while:
1. Acknowledging key insights from previous responses
2. Adding any missing important points
3. Respectfully noting if you disagree with anything and explaining why
4. Building towards a complete answer

Keep your response focused and concise (max 3-4 paragraphs)."""
    return prompt

def generate_consensus_prompt(original_question: str, discussion_history: List[str]) -> str:
    """Generate a prompt for final consensus building."""
    return f"""Review this multi-AI discussion about: "{original_question}"

Discussion history:
{chr(10).join(discussion_history)}

As a final synthesizer, please:
1. Identify the key points where all models agreed
2. Explain how any disagreements were resolved
3. Present a clear, unified answer that represents our collective best understanding
4. Note any remaining uncertainties or caveats

Keep the final consensus concise but complete."""

def chat_with_openai(model: str, messages: List[Dict], api_key: str) -> str:
    import openai
    client = openai.OpenAI(api_key=api_key)
    response = client.chat.completions.create(
        model=model,
        messages=messages
    )
    return response.choices[0].message.content

def chat_with_anthropic(messages: List[Dict], api_key: str) -> str:
    """Chat with Anthropic's Claude model."""
    client = Anthropic(api_key=api_key)
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        messages=messages,
        max_tokens=1024
    )
    return response.content[0].text

def chat_with_gemini(messages: List[Dict], api_key: str) -> str:
    """Chat with Gemini Pro model."""
    genai.configure(api_key=api_key)
    model = genai.GenerativeModel('gemini-pro')
    
    # Convert messages to Gemini format
    gemini_messages = []
    for msg in messages:
        role = "user" if msg["role"] == "user" else "model"
        gemini_messages.append({"role": role, "parts": [msg["content"]]})
    
    response = model.generate_content([m["parts"][0] for m in gemini_messages])
    return response.text

def chat_with_sambanova(messages: List[Dict], api_key: str, model_name: str = "Llama-3.2-90B-Vision-Instruct") -> str:
    """Chat with SambaNova's models using their OpenAI-compatible API."""
    client = openai.OpenAI(
        api_key=api_key,
        base_url="https://api.sambanova.ai/v1",
    )
    
    response = client.chat.completions.create(
        model=model_name,  # Use the specific model name passed in
        messages=messages,
        temperature=0.1,
        top_p=0.1
    )
    return response.choices[0].message.content

def chat_with_hyperbolic(messages: List[Dict], api_key: str, model_name: str = "Qwen/Qwen2.5-Coder-32B-Instruct") -> str:
    """Chat with Hyperbolic's models using their OpenAI-compatible API."""
    client = OpenAI(
        api_key=api_key,
        base_url="https://api.hyperbolic.xyz/v1"
    )
    
    # Add system message to the start of the messages list
    full_messages = [
        {"role": "system", "content": "You are a helpful assistant. Be descriptive and clear."},
        *messages
    ]
    
    response = client.chat.completions.create(
        model=model_name,  # Use the specific model name passed in
        messages=full_messages,
        temperature=0.7,
        max_tokens=1024,
    )
    return response.choices[0].message.content

def multi_model_consensus(
    question: str, 
    selected_models: List[str], 
    rounds: int = 3,
    progress: gr.Progress = gr.Progress()
) -> tuple[str, List[Dict]]:
    if not selected_models:
        return "Please select at least one model to chat with.", []
    
    chat_history = []
    discussion_history = []
    
    # Initial responses
    progress(0, desc="Getting initial responses...")
    initial_responses = []
    for i, model in enumerate(selected_models):
        provider, model_name = model.split(": ", 1)
        
        try:
            if provider == "Anthropic":
                api_key = os.getenv("ANTHROPIC_API_KEY")
                response = chat_with_anthropic(
                    messages=[{"role": "user", "content": question}],
                    api_key=api_key
                )
            elif provider == "SambaNova":
                api_key = os.getenv("SAMBANOVA_API_KEY")
                response = chat_with_sambanova(
                    messages=[
                        {"role": "system", "content": "You are a helpful assistant"},
                        {"role": "user", "content": question}
                    ],
                    api_key=api_key
                )
            elif provider == "Hyperbolic":  # Add Hyperbolic case
                api_key = os.getenv("HYPERBOLIC_API_KEY")
                response = chat_with_hyperbolic(
                    messages=[{"role": "user", "content": question}],
                    api_key=api_key
                )
            else:  # Gemini
                api_key = os.getenv("GEMINI_API_KEY")
                response = chat_with_gemini(
                    messages=[{"role": "user", "content": question}],
                    api_key=api_key
                )
                
            initial_responses.append(f"{model}: {response}")
            discussion_history.append(f"Initial response from {model}:\n{response}")
            chat_history.append((f"Initial response from {model}", response))
        except Exception as e:
            chat_history.append((f"Error from {model}", str(e)))
    
    # Discussion rounds
    for round_num in range(rounds):
        progress((round_num + 1) / (rounds + 2), desc=f"Discussion round {round_num + 1}...")
        round_responses = []
        
        random.shuffle(selected_models)  # Randomize order each round
        for model in selected_models:
            provider, model_name = model.split(": ", 1)
            
            try:
                discussion_prompt = generate_discussion_prompt(question, discussion_history)
                if provider == "Anthropic":
                    api_key = os.getenv("ANTHROPIC_API_KEY")
                    response = chat_with_anthropic(
                        messages=[{"role": "user", "content": discussion_prompt}],
                        api_key=api_key
                    )
                elif provider == "SambaNova":
                    api_key = os.getenv("SAMBANOVA_API_KEY")
                    response = chat_with_sambanova(
                        messages=[
                            {"role": "system", "content": "You are a helpful assistant"},
                            {"role": "user", "content": discussion_prompt}
                        ],
                        api_key=api_key
                    )
                elif provider == "Hyperbolic":  # Add Hyperbolic case
                    api_key = os.getenv("HYPERBOLIC_API_KEY")
                    response = chat_with_hyperbolic(
                        messages=[{"role": "user", "content": discussion_prompt}],
                        api_key=api_key
                    )
                else:  # Gemini
                    api_key = os.getenv("GEMINI_API_KEY")
                    response = chat_with_gemini(
                        messages=[{"role": "user", "content": discussion_prompt}],
                        api_key=api_key
                    )
                    
                round_responses.append(f"{model}: {response}")
                discussion_history.append(f"Round {round_num + 1} - {model}:\n{response}")
                chat_history.append((f"Round {round_num + 1} - {model}", response))
            except Exception as e:
                chat_history.append((f"Error from {model} in round {round_num + 1}", str(e)))
    
    # Final consensus
    progress(0.9, desc="Building final consensus...")
    model = selected_models[0]
    provider, model_name = model.split(": ", 1)
    
    try:
        consensus_prompt = generate_consensus_prompt(question, discussion_history)
        if provider == "Anthropic":
            api_key = os.getenv("ANTHROPIC_API_KEY")
            final_consensus = chat_with_anthropic(
                messages=[{"role": "user", "content": consensus_prompt}],
                api_key=api_key
            )
        elif provider == "SambaNova":
            api_key = os.getenv("SAMBANOVA_API_KEY")
            final_consensus = chat_with_sambanova(
                messages=[
                    {"role": "system", "content": "You are a helpful assistant"},
                    {"role": "user", "content": consensus_prompt}
                ],
                api_key=api_key
            )
        elif provider == "Hyperbolic":  # Add Hyperbolic case
            api_key = os.getenv("HYPERBOLIC_API_KEY")
            final_consensus = chat_with_hyperbolic(
                messages=[{"role": "user", "content": consensus_prompt}],
                api_key=api_key
            )
        else:  # Gemini
            api_key = os.getenv("GEMINI_API_KEY")
            final_consensus = chat_with_gemini(
                messages=[{"role": "user", "content": consensus_prompt}],
                api_key=api_key
            )
    except Exception as e:
        final_consensus = f"Error getting consensus from {model}: {str(e)}"
    
    chat_history.append(("Final Consensus", final_consensus))
    
    progress(1.0, desc="Done!")
    return chat_history

with gr.Blocks() as demo:
    gr.Markdown("# Experimental Multi-Model Consensus Chat")
    gr.Markdown("""Select multiple models to collaborate on answering your question. 
                The models will discuss with each other and attempt to reach a consensus.
                Maximum 3 models can be selected at once.""")
    
    with gr.Row():
        with gr.Column():
            model_selector = gr.Dropdown(
                choices=get_all_models(),
                multiselect=True,
                label="Select Models (max 3)",
                info="Choose up to 3 models to participate in the discussion",
                value=["SambaNova: Llama-3.2-90B-Vision-Instruct", "Hyperbolic: Qwen/Qwen2.5-Coder-32B-Instruct"],
                max_choices=3
            )
            rounds_slider = gr.Slider(
                minimum=1,
                maximum=2,
                value=1,
                step=1,
                label="Discussion Rounds",
                info="Number of rounds of discussion between models"
            )
    
    chatbot = gr.Chatbot(height=600, label="Multi-Model Discussion")
    msg = gr.Textbox(label="Your Question", placeholder="Ask a question for the models to discuss...")
    
    def respond(message, selected_models, rounds):
        chat_history = multi_model_consensus(message, selected_models, rounds)
        return chat_history
    
    msg.submit(
        respond,
        [msg, model_selector, rounds_slider],
        [chatbot],
        api_name="consensus_chat"
    )

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