app.py

import os
import streamlit as st
import dotenv
import openai
from openai import OpenAI
import anthropic
from together import Together
import google.generativeai as genai
import time

dotenv.load_dotenv()

PASSWORD = os.getenv("APP_PASSWORD")

# Load API keys from environment variables
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY")

# Initialize API clients
together_client = Together(api_key=TOGETHER_API_KEY)
genai.configure(api_key=GOOGLE_API_KEY)

# Set up API clients for OpenAI and Anthropic
openai.api_key = OPENAI_API_KEY
openai_client = OpenAI(
    organization="org-kUoRSK0nOw4W2nQYMVGWOt03",
    project="proj_zb6k1DdgnSEbiAEMWxSOVVu4",
)
# anthropic_client = anthropic.Client(api_key=ANTHROPIC_API_KEY)
anthropic_client = anthropic.Anthropic()

LLM_COUNCIL_MEMBERS = {
    "Smalls": [
        "openai://gpt-4o-mini",
        "together://meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
        "vertex://gemini-1.5-flash-001",
        "anthropic://claude-3-haiku-20240307",
    ],
    "Flagships": [
        "openai://gpt-4",
        "together://meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",
        "vertex://gemini-1.5-pro-001",
        "anthropic://claude-3-5-sonnet",
    ],
}

PROVIDER_TO_AVATAR_MAP = {
    "openai://gpt-4o-mini": "data:image/svg+xml;base64,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",
    "anthropic://claude-3-5-sonnet": "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxZW0iIGhlaWdodD0iMWVtIiB2aWV3Qm94PSIwIDAgMjQgMjQiPjxwYXRoIGZpbGw9ImN1cnJlbnRDb2xvciIgZD0iTTE3LjMwNCAzLjU0MWgtMy42NzJsNi42OTYgMTYuOTE4SDI0Wm0tMTAuNjA4IDBMMCAyMC40NTloMy43NDRsMS4zNy0zLjU1M2g3LjAwNWwxLjM2OSAzLjU1M2gzLjc0NEwxMC41MzYgMy41NDFabS0uMzcxIDEwLjIyM0w4LjYxNiA3LjgybDIuMjkxIDUuOTQ1WiIvPjwvc3ZnPg==",
    "vertex://gemini-1.5-flash-001": "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxZW0iIGhlaWdodD0iMWVtIiB2aWV3Qm94PSIwIDAgMjQgMjQiPjxwYXRoIGZpbGw9IiM0MjY4ZmYiIGQ9Ik0yNCAxMi4wMjRjLTYuNDM3LjM4OC0xMS41OSA1LjUzOS0xMS45NzcgMTEuOTc2aC0uMDQ3QzExLjU4OCAxNy41NjMgNi40MzYgMTIuNDEyIDAgMTIuMDI0di0uMDQ3QzYuNDM3IDExLjU4OCAxMS41ODggNi40MzcgMTEuOTc2IDBoLjA0N2MuMzg4IDYuNDM3IDUuNTQgMTEuNTg4IDExLjk3NyAxMS45Nzd6Ii8+PC9zdmc+",
    "together://meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo": "data:image/svg+xml;base64,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",
    "anthropic://claude-3-haiku-20240307": "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxZW0iIGhlaWdodD0iMWVtIiB2aWV3Qm94PSIwIDAgMjQgMjQiPjxwYXRoIGZpbGw9ImN1cnJlbnRDb2xvciIgZD0iTTE3LjMwNCAzLjU0MWgtMy42NzJsNi42OTYgMTYuOTE4SDI0Wm0tMTAuNjA4IDBMMCAyMC40NTloMy43NDRsMS4zNy0zLjU1M2g3LjAwNWwxLjM2OSAzLjU1M2gzLjc0NEwxMC41MzYgMy41NDFabS0uMzcxIDEwLjIyM0w4LjYxNiA3LjgybDIuMjkxIDUuOTQ1WiIvPjwvc3ZnPg==",
}

AGGREGATORS = ["openai://gpt-4", "openai://gpt-3.5-turbo"]


def anthropic_streamlit_streamer(stream):
    """
    Process the Anthropic streaming response and yield content from the deltas.

    :param stream: Streaming object from Anthropic API
    :return: Yields content (text) from the streaming response.
    """
    for event in stream:
        if hasattr(event, "type"):
            # Handle content blocks
            if event.type == "content_block_delta" and hasattr(event, "delta"):
                # Extract text delta from the event
                text_delta = getattr(event.delta, "text", None)
                if text_delta:
                    yield text_delta

            # Handle message completion events (optional if needed)
            elif event.type == "message_stop":
                break  # End of message, stop streaming


def google_streamlit_streamer(stream):
    for chunk in stream:
        yield chunk.text


def together_streamlit_streamer(stream):
    for chunk in stream:
        yield chunk.choices[0].delta.content


# Helper functions for LLM council and aggregator selection
def llm_council_selector():
    selected_council = st.radio(
        "Choose a council configuration", options=list(LLM_COUNCIL_MEMBERS.keys())
    )
    return LLM_COUNCIL_MEMBERS[selected_council]


def aggregator_selector():
    return st.radio("Choose an aggregator LLM", options=AGGREGATORS)


# API calls for different providers
def get_openai_response(model_name, prompt):
    return openai_client.chat.completions.create(
        model=model_name,
        messages=[{"role": "user", "content": prompt}],
        stream=True,
    )


# https://docs.anthropic.com/en/api/messages-streaming
def get_anthropic_response(model_name, prompt):
    return anthropic_client.messages.create(
        max_tokens=1024,
        messages=[{"role": "user", "content": prompt}],
        model=model_name,
        stream=True,
    )


def get_together_response(model_name, prompt):
    return together_client.chat.completions.create(
        model=model_name,
        messages=[{"role": "user", "content": prompt}],
        stream=True,
    )


# https://ai.google.dev/gemini-api/docs/text-generation?lang=python
def get_google_response(model_name, prompt):
    model = genai.GenerativeModel(model_name)
    return model.generate_content(prompt, stream=True)


def get_llm_response(model_identifier, prompt):
    provider, model_name = model_identifier.split("://")
    if provider == "openai":
        return get_openai_response(model_name, prompt)
    elif provider == "anthropic":
        return get_anthropic_response(model_name, prompt)
    elif provider == "together":
        return get_together_response(model_name, prompt)
    elif provider == "vertex":
        return get_google_response(model_name, prompt)
    else:
        return None


# Main Streamlit App
def main():
    st.set_page_config(
        page_title="Language Model Council Sandbox", page_icon="🏛️", layout="wide"
    )

    # Custom CSS for the chat display
    center_css = """
    <style>
    h1, h2, h3, h6 { text-align: center; }
    .chat-container {
        display: flex;
        align-items: flex-start;
        margin-bottom: 10px;
    }
    .avatar {
        width: 50px;
        margin-right: 10px;
    }
    .message {
        background-color: #f1f1f1;
        padding: 10px;
        border-radius: 10px;
        width: 100%;
    }
    </style>
    """
    st.markdown(center_css, unsafe_allow_html=True)

    # App title and description
    st.title("Language Model Council Sandbox")
    st.markdown("###### Invoke a council of LLMs to generate and judge each other.")
    st.markdown("###### [ArXiv Paper](https://arxiv.org/abs/2406.08598)")

    # Authentication system
    if "authenticated" not in st.session_state:
        st.session_state.authenticated = False

    cols = st.columns([2, 1, 2])
    if not st.session_state.authenticated:
        with cols[1]:
            password = st.text_input("Password", type="password")
            if st.button("Login", use_container_width=True):
                if password == PASSWORD:
                    st.session_state.authenticated = True
                else:
                    st.error("Invalid credentials")

    if st.session_state.authenticated:
        st.success("Logged in successfully!")

        # Council and aggregator selection
        selected_models = llm_council_selector()
        st.write("Selected Models:", selected_models)
        selected_aggregator = aggregator_selector()
        st.write("Selected Aggregator:", selected_aggregator)

        # Prompt input
        prompt = st.text_area("Enter your prompt:")

        if st.button("Submit"):
            st.write("Responses:")

            # Fetching and streaming responses from each selected model
            for model in selected_models:
                # with st.chat_message(model):
                with st.chat_message(
                    model,
                    avatar=PROVIDER_TO_AVATAR_MAP[model],
                ):
                    message_placeholder = st.empty()
                    stream = get_llm_response(model, prompt)
                    if stream:
                        if model.startswith("anthropic"):
                            stream = anthropic_streamlit_streamer(stream)
                        elif model.startswith("vertex"):
                            stream = google_streamlit_streamer(stream)
                        elif model.startswith("together"):
                            stream = together_streamlit_streamer(stream)
                        message_placeholder.write_stream(stream)

            # Constructing the aggregator prompt
            aggregator_prompt = f"User prompt: {prompt}\n\n"
            aggregator_prompt += "Responses from other LLMs:\n"
            aggregator_prompt += "\n".join(
                [
                    f"{model}: {st.session_state.get(model, '')}"
                    for model in selected_models
                ]
            )
            aggregator_prompt += "\n\nPlease provide an aggregated response."

            # Fetching and streaming response from the aggregator
            st.write(f"Aggregated response from {selected_aggregator}:")
            with st.chat_message(selected_aggregator):
                message_placeholder = st.empty()
                aggregator_stream = get_llm_response(
                    selected_aggregator, aggregator_prompt
                )
                if aggregator_stream:
                    message_placeholder.write_stream(aggregator_stream)
    else:
        with cols[1]:
            st.warning("Please log in to access this app.")


if __name__ == "__main__":
    main()


# import streamlit as st
# from components import llm_council_selector

# st.title("LLM Council Selector")

# selected_models = llm_council_selector()

# if selected_models is not None:
#     st.write("Selected Models:", selected_models)
# else:
#     st.write("No models selected or component didn't return a value.")


# Choose your council.
# Pre-selected.
#    Smalls: GPT-4o-mini, llama-3.1-70b, qwen-2.0-70b
#    Flagships: GPT-4o, llama-3.1-405b, qwen-2.0-110b, gemini, claude-3.5-sonnet
#    Best: chatgpt-4o-latest, gemini-1.5-pro-exp-0827, grok-2-2024-08-13, claude-3-5-sonnet-20240620, llama-3.1-405b-instruct
# Custom:
#    Choose from a list of available models.
# All:
#    All available models.

# Choose aggregator.
# Aggregators are models proficient in synthesizing responses from other models into a single, highquality output. An effective aggregator should maintain or enhance output quality even when
# integrating inputs that are of lesser quality than its own.
# Choices:
#   - 4o-latest
#   - gemini-1.5
#   - grok-2
#   - claude-3.5-sonnet
#   - llama-3.1-405b-instruct

# Provide a prompt. (Or pre-canned prompts.)
# Paste chat history.

# Checkbox, enable judging.
#
# If checked, Judging config:
# Single sided
#   Provide criteria. (or default).
# If pairwise, choose granularity (or default).
#   Choose criteria. (or default).
#   Enable position swapping?

# Go button.
# Sections.
# 1. Model outputs.
# 2. Aggregated output.
# 3. Judging underneath each output.
#  Highlight in green, the output that was best, as determined by council.
#  Show graph breakdown of scores and justifications. (by criteria, # wins and # losses)
#  Show final overall score.
#  Highlight in red, the output that was worst, as determined by council.
# Judging section.
#   Show agreement matrix.
#   Show bar graph of self-bias.
#   Plot contrarianism vs. conviction (scatter plot)
#   Show per-judge scores.

# Calculate total cost.
# Show total tokens used.