app.py

import streamlit as st
import json
from typing import Iterable
from moa.agent import MOAgent
from moa.agent.moa import ResponseChunk
from streamlit_ace import st_ace
import copy

# Default configuration
default_config = {
    "main_model": "llama-3.3-70b-versatile",
    "cycles": 3,
    "layer_agent_config": {}
}

layer_agent_config_def = {
    "layer_agent_1": {
        "system_prompt": "Think through your response step by step. {helper_response}",
        "model_name": "llama-3.1-8b-instant"
    },
    "layer_agent_2": {
        "system_prompt": "Respond with a thought and then your response to the question. {helper_response}",
        "model_name": "gemma2-9b-it",
        "temperature": 0.7
    },
    "layer_agent_3": {
        "system_prompt": "You are an expert at logic and reasoning. Always take a logical approach to the answer. {helper_response}",
        "model_name": "llama-3.1-8b-instant"
    },

}

# Recommended Configuration

rec_config = {
    "main_model": "llama-3.3-70b-versatile",
    "cycles": 2,
    "layer_agent_config": {}
}

layer_agent_config_rec = {
    "layer_agent_1": {
        "system_prompt": "Think through your response step by step. {helper_response}",
        "model_name": "llama-3.1-8b-instant",
        "temperature": 0.1
    },
    "layer_agent_2": {
        "system_prompt": "Respond with a thought and then your response to the question. {helper_response}",
        "model_name": "llama-3.1-8b-instant",
        "temperature": 0.2
    },
    "layer_agent_3": {
        "system_prompt": "You are an expert at logic and reasoning. Always take a logical approach to the answer. {helper_response}",
        "model_name": "llama-3.1-8b-instant",
        "temperature": 0.4
    },
    "layer_agent_4": {
        "system_prompt": "You are an expert planner agent. Create a plan for how to answer the human's query. {helper_response}",
        "model_name": "mixtral-8x7b-32768",
        "temperature": 0.5
    },
}


def stream_response(messages: Iterable[ResponseChunk]):
    layer_outputs = {}
    for message in messages:
        if message['response_type'] == 'intermediate':
            layer = message['metadata']['layer']
            if layer not in layer_outputs:
                layer_outputs[layer] = []
            layer_outputs[layer].append(message['delta'])
        else:
            # Display accumulated layer outputs
            for layer, outputs in layer_outputs.items():
                st.write(f"Layer {layer}")
                cols = st.columns(len(outputs))
                for i, output in enumerate(outputs):
                    with cols[i]:
                        st.expander(label=f"Agent {i+1}", expanded=False).write(output)
            
            # Clear layer outputs for the next iteration
            layer_outputs = {}
            
            # Yield the main agent's output
            yield message['delta']




def set_moa_agent(
    main_model: str = default_config['main_model'],
    cycles: int = default_config['cycles'],
    layer_agent_config: dict[dict[str, any]] = copy.deepcopy(layer_agent_config_def),
    main_model_temperature: float = 0.1,
    override: bool = False
):
    if override or ("main_model" not in st.session_state):
        st.session_state.main_model = main_model
    else:
        if "main_model" not in st.session_state: st.session_state.main_model = main_model 

    if override or ("cycles" not in st.session_state):
        st.session_state.cycles = cycles
    else:
        if "cycles" not in st.session_state: st.session_state.cycles = cycles

    if override or ("layer_agent_config" not in st.session_state):
        st.session_state.layer_agent_config = layer_agent_config
    else:
        if "layer_agent_config" not in st.session_state: st.session_state.layer_agent_config = layer_agent_config

    if override or ("main_temp" not in st.session_state):
        st.session_state.main_temp = main_model_temperature
    else:
        if "main_temp" not in st.session_state: st.session_state.main_temp = main_model_temperature

    cls_ly_conf = copy.deepcopy(st.session_state.layer_agent_config)
    
    if override or ("moa_agent" not in st.session_state):
        st.session_state.moa_agent = MOAgent.from_config(
            main_model=st.session_state.main_model,
            cycles=st.session_state.cycles,
            layer_agent_config=cls_ly_conf,
            temperature=st.session_state.main_temp
        )

    del cls_ly_conf
    del layer_agent_config

st.set_page_config(
    page_title="Mixture of Agents",
        menu_items={
        'About': "## Groq Mixture-Of-Agents \n Powered by [Groq](https://groq.com)"
    },
    layout="wide"
)
valid_model_names = [
    'llama-3.1-8b-instant',
    'llama-3.3-70b-versatile',
    'gemma2-9b-it',
    'mixtral-8x7b-32768'
]

# Initialize session state
if "messages" not in st.session_state:
    st.session_state.messages = []

set_moa_agent()

# Sidebar for configuration
# Sidebar for configuration
with st.sidebar:
    st.title("MOA Configuration")
    with st.form("Agent Configuration", border=False):
        if st.form_submit_button("Use Recommended Config"):
            try:
                set_moa_agent(
                    main_model=rec_config['main_model'],
                    cycles=rec_config['cycles'],
                    layer_agent_config=layer_agent_config_rec,
                    override=True
                )
                st.session_state.messages = []
                st.success("Configuration updated successfully!")
            except json.JSONDecodeError:
                st.error("Invalid JSON in Layer Agent Configuration. Please check your input.")
            except Exception as e:
                st.error(f"Error updating configuration: {str(e)}")

        # Main model selection
        new_main_model = st.selectbox(
            "Select Main Model",
            options=valid_model_names,
            index=valid_model_names.index(st.session_state.main_model)
        )

        # Cycles input
        new_cycles = st.number_input(
            "Number of Layers",
            min_value=1,
            max_value=10,
            value=st.session_state.cycles
        )

        # Main Model Temperature
        main_temperature = st.slider(
            "Main Model Temperature",
            min_value=0.0,
            max_value=1.0,
            value=st.session_state.main_temp,
            step=0.05
        )

        # Layer agent configuration
        tooltip = "Agents in the layer agent configuration run in parallel _per cycle_. Each layer agent supports all initialization parameters of [Langchain's ChatGroq](https://api.python.langchain.com/en/latest/chat_models/langchain_groq.chat_models.ChatGroq.html) class as valid dictionary fields."
        st.markdown("Layer Agent Config", help=tooltip)
        new_layer_agent_config = st_ace(
            value=json.dumps(st.session_state.layer_agent_config, indent=2),
            language='json',
            placeholder="Layer Agent Configuration (JSON)",
            show_gutter=False,
            wrap=True,
            auto_update=True
        )


        if st.form_submit_button("Update Configuration"):
            try:
                new_layer_config = json.loads(new_layer_agent_config)
                set_moa_agent(
                    main_model=new_main_model,
                    cycles=new_cycles,
                    layer_agent_config=new_layer_config,
                    main_model_temperature=main_temperature,
                    override=True
                )
                st.session_state.messages = []
                st.success("Configuration updated successfully!")
            except json.JSONDecodeError:
                st.error("Invalid JSON in Layer Agent Configuration. Please check your input.")
            except Exception as e:
                st.error(f"Error updating configuration: {str(e)}")

    st.markdown("---")
    st.markdown("""
    ### Credits
    - MOA: [Together AI](https://www.together.ai/blog/together-moa)
    - LLMs: [Groq](https://groq.com/)
    - Paper: [arXiv:2406.04692](https://arxiv.org/abs/2406.04692)
    """)

# Main app layout
st.header("Mixture of Agents", anchor=False)
st.write("A this project oversees implementation of Mixture of Agents architecture Powered by Groq LLMs.")
# st.image("./static/moa_groq.svg", caption="Mixture of Agents Workflow", width=800)

# Display current configuration
with st.expander("Current MOA Configuration", expanded=False):
    st.markdown(f"**Main Model**: ``{st.session_state.main_model}``")
    st.markdown(f"**Main Model Temperature**: ``{st.session_state.main_temp:.1f}``")
    st.markdown(f"**Layers**: ``{st.session_state.cycles}``")
    st.markdown(f"**Layer Agents Config**:")
    new_layer_agent_config = st_ace(
        value=json.dumps(st.session_state.layer_agent_config, indent=2),
        language='json',
        placeholder="Layer Agent Configuration (JSON)",
        show_gutter=False,
        wrap=True,
        readonly=True,
        auto_update=True
    )

# Chat interface
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

if query := st.chat_input("Ask a question"):
    st.session_state.messages.append({"role": "user", "content": query})
    with st.chat_message("user"):
        st.markdown(query)

    moa_agent: MOAgent = st.session_state.moa_agent
    with st.chat_message("assistant"):
        ast_mess = stream_response(moa_agent.chat(query, output_format="json"))
        response = st.write_stream(ast_mess)

    # Save the final response to session state
    st.session_state.messages.append({"role": "assistant", "content": response})



# Add acknowledgment at the bottom
st.markdown("---")
st.markdown("""
####
This app is based on [Emmanuel M. Ndaliro's work](https://github.com/kram254/Mixture-of-Agents-running-on-Groq/tree/main). 
""")