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
from typing import List, Optional, Literal, Union, Dict
from constants import (
    LLM_COUNCIL_MEMBERS,
    PROVIDER_TO_AVATAR_MAP,
    AGGREGATORS,
    LLM_TO_UI_NAME_MAP,
)
from prompts import *
from judging_dataclasses import (
    DirectAssessmentJudgingResponse,
    DirectAssessmentCriterionScore,
    DirectAssessmentCriteriaScores,
)
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

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()

client = OpenAI()


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 get_ui_friendly_name(llm):
    if "agg__" in llm:
        return (
            "MoA ("
            + LLM_TO_UI_NAME_MAP.get(llm.split("__")[1], llm.split("__")[1])
            + ")"
        )
    return LLM_TO_UI_NAME_MAP.get(llm, llm)


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


def llm_streamlit_streamer(stream, llm):
    if llm.startswith("anthropic"):
        return anthropic_streamlit_streamer(stream)
    elif llm.startswith("vertex"):
        return google_streamlit_streamer(stream)
    elif llm.startswith("together"):
        return together_streamlit_streamer(stream)


# 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_stream(model_identifier, prompt):
    """Returns a streamlit-friendly stream of response tokens from the LLM."""
    provider, model_name = model_identifier.split("://")
    if provider == "openai":
        return get_openai_response(model_name, prompt)
    elif provider == "anthropic":
        return anthropic_streamlit_streamer(get_anthropic_response(model_name, prompt))
    elif provider == "together":
        return together_streamlit_streamer(get_together_response(model_name, prompt))
    elif provider == "vertex":
        return google_streamlit_streamer(get_google_response(model_name, prompt))
    else:
        return None


def create_dataframe_for_direct_assessment_judging_response(
    response: DirectAssessmentJudgingResponse,
):
    # Initialize empty list to collect data
    data = []

    # Loop through models
    for judging_model in response.judging_models:
        model_name = judging_model.model
        # Loop through criteria_scores
        for criteria_score in judging_model.criteria_scores:
            data.append(
                {
                    "llm_judge_model": model_name,
                    "criteria": criteria_score.criterion,
                    "score": criteria_score.score,
                    "explanation": criteria_score.explanation,
                }
            )

    # Create DataFrame
    return pd.DataFrame(data)


# Streamlit form UI
def render_criteria_form(criteria_num):
    """Render a criteria input form."""
    with st.expander(f"Criteria {criteria_num + 1}"):
        name = st.text_input(
            f"Name for Criteria {criteria_num + 1}", key=f"criteria_name_{criteria_num}"
        )
        description = st.text_area(
            f"Description for Criteria {criteria_num + 1}",
            key=f"criteria_desc_{criteria_num}",
        )
        min_score = st.number_input(
            f"Min Score for Criteria {criteria_num + 1}",
            min_value=0,
            step=1,
            key=f"criteria_min_{criteria_num}",
        )
        max_score = st.number_input(
            f"Max Score for Criteria {criteria_num + 1}",
            min_value=0,
            step=1,
            key=f"criteria_max_{criteria_num}",
        )
    return Criteria(
        name=name, description=description, min_score=min_score, max_score=max_score
    )


def format_likert_comparison_options(options):
    return "\n".join([f"{i + 1}: {option}" for i, option in enumerate(options)])


def format_criteria_list(criteria_list):
    return "\n".join(
        [f"{criteria.name}: {criteria.description}" for criteria in criteria_list]
    )


def get_direct_assessment_prompt(
    direct_assessment_prompt, user_prompt, response, criteria_list, options
):
    return direct_assessment_prompt.format(
        user_prompt=user_prompt,
        response=response,
        criteria_list=f"{format_criteria_list(DEFAULT_DIRECT_ASSESSMENT_CRITERIA_LIST)}",
        options=f"{format_likert_comparison_options(SEVEN_POINT_DIRECT_ASSESSMENT_OPTIONS)}",
    )


def get_default_direct_assessment_prompt(user_prompt):
    return get_direct_assessment_prompt(
        direct_assessment_prompt=DEFAULT_DIRECT_ASSESSMENT_PROMPT,
        user_prompt=user_prompt,
        response="{response}",
        criteria_list=DEFAULT_DIRECT_ASSESSMENT_CRITERIA_LIST,
        options=SEVEN_POINT_DIRECT_ASSESSMENT_OPTIONS,
    )


def get_aggregator_prompt(aggregator_prompt, user_prompt, llms):
    responses_from_other_llms = "\n\n".join(
        [
            f"{get_ui_friendly_name(model)} START\n{st.session_state['responses'][model]}\n\n{get_ui_friendly_name(model)} END\n\n\n"
            for model in llms
        ]
    )
    return aggregator_prompt.format(
        user_prompt=user_prompt,
        responses_from_other_llms=responses_from_other_llms,
    )


def get_default_aggregator_prompt(user_prompt, llms):
    return get_aggregator_prompt(
        DEFAULT_AGGREGATOR_PROMPT,
        user_prompt=user_prompt,
        llms=llms,
    )


def get_parse_judging_response_for_direct_assessment_prompt(
    judging_responses: dict[str, str],
    criteria_list,
    options,
):
    formatted_judging_responses = "\n\n".join(
        [
            f"{get_ui_friendly_name(model)} START\n{judging_responses[model]}\n\n{get_ui_friendly_name(model)} END\n\n\n"
            for model in judging_responses.keys()
        ]
    )
    return PARSE_JUDGING_RESPONSE_FOR_DIRECT_ASSESSMENT_PROMPT.format(
        judging_responses=formatted_judging_responses,
        criteria_list=format_criteria_list(criteria_list),
        options=format_likert_comparison_options(options),
    )


DEBUG_MODE = True


def parse_judging_responses(
    prompt: str, judging_responses: dict[str, str]
) -> DirectAssessmentJudgingResponse:
    if DEBUG_MODE:
        return DirectAssessmentJudgingResponse(
            judging_models=[
                DirectAssessmentCriteriaScores(
                    model="together://meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
                    criteria_scores=[
                        DirectAssessmentCriterionScore(
                            criterion="helpfulness", score=3, explanation="explanation1"
                        ),
                        DirectAssessmentCriterionScore(
                            criterion="conciseness", score=4, explanation="explanation2"
                        ),
                        DirectAssessmentCriterionScore(
                            criterion="relevance", score=5, explanation="explanation3"
                        ),
                    ],
                ),
                DirectAssessmentCriteriaScores(
                    model="together://meta-llama/Llama-3.2-3B-Instruct-Turbo",
                    criteria_scores=[
                        DirectAssessmentCriterionScore(
                            criterion="helpfulness", score=1, explanation="explanation1"
                        ),
                        DirectAssessmentCriterionScore(
                            criterion="conciseness", score=2, explanation="explanation2"
                        ),
                        DirectAssessmentCriterionScore(
                            criterion="relevance", score=3, explanation="explanation3"
                        ),
                    ],
                ),
            ]
        )
    else:
        completion = client.beta.chat.completions.parse(
            model="gpt-4o-mini",
            messages=[
                {
                    "role": "system",
                    "content": "Parse the judging responses into structured data.",
                },
                {"role": "user", "content": prompt},
            ],
            response_format=DirectAssessmentJudgingResponse,
        )
        return completion.choices[0].message.parsed


def plot_criteria_scores(df):
    # Group by criteria and calculate mean and std over all judges.
    grouped = df.groupby(["criteria"]).agg({"score": ["mean", "std"]}).reset_index()

    # Flatten the MultiIndex columns
    grouped.columns = ["criteria", "mean_score", "std_score"]

    # Fill NaN std with zeros (in case there's only one score per group)
    grouped["std_score"] = grouped["std_score"].fillna(0)

    # Set up the plot
    plt.figure(figsize=(8, 5))

    # Create a horizontal bar plot
    ax = sns.barplot(
        data=grouped,
        x="mean_score",
        y="criteria",
        hue="criteria",
        errorbar=None,  # Updated parameter
        orient="h",
    )

    # Add error bars manually
    # Iterate over the bars and add error bars
    for i, (mean, std) in enumerate(zip(grouped["mean_score"], grouped["std_score"])):
        # Get the current bar
        bar = ax.patches[i]
        # Calculate the center of the bar
        center = bar.get_y() + bar.get_height() / 2
        # Add the error bar
        ax.errorbar(x=mean, y=center, xerr=std, ecolor="black", capsize=3, fmt="none")

    # Set labels and title
    ax.set_xlabel("")
    ax.set_ylabel("")
    plt.tight_layout()

    # Display the plot in Streamlit
    st.pyplot(plt.gcf())


def plot_overall_scores(overall_scores_df):
    # Calculate mean and standard deviation
    summary = (
        overall_scores_df.groupby("response_model")
        .agg({"score": ["mean", "std"]})
        .reset_index()
    )
    summary.columns = ["response_model", "mean_score", "std_score"]

    # Add UI-friendly names
    summary["ui_friendly_name"] = summary["response_model"].apply(get_ui_friendly_name)

    # Sort the summary dataframe by mean_score in descending order
    summary = summary.sort_values("mean_score", ascending=False)

    # Create the plot
    plt.figure(figsize=(8, 5))

    # Plot bars with rainbow colors
    ax = sns.barplot(
        x="ui_friendly_name",
        y="mean_score",
        hue="ui_friendly_name",  # Add this line
        data=summary,
        palette="prism",
        capsize=0.1,
        legend=False,  # Add this line
    )

    # Add error bars manually
    x_coords = range(len(summary))
    plt.errorbar(
        x=x_coords,
        y=summary["mean_score"],
        yerr=summary["std_score"],
        fmt="none",
        c="black",
        capsize=5,
        zorder=10,  # Ensure error bars are on top
    )

    # Add text annotations
    for i, row in summary.iterrows():
        ax.text(
            i,
            row["mean_score"],
            f"{row['mean_score']:.2f}",
            ha="center",
            va="bottom",
            fontweight="bold",
            color="black",
            bbox=dict(facecolor="white", edgecolor="none", alpha=0.7, pad=0.5),
        )

    # Customize the plot
    plt.xlabel("")
    plt.ylabel("Overall Score")
    plt.xticks(rotation=45, ha="right")
    plt.tight_layout()

    # Display the plot in Streamlit
    st.pyplot(plt.gcf())


def plot_per_judge_overall_scores(df):
    # Find the overall score by finding the overall score for each judge, and then averaging
    # over all judges.
    grouped = df.groupby(["llm_judge_model"]).agg({"score": ["mean"]}).reset_index()
    grouped.columns = ["llm_judge_model", "overall_score"]

    # Create the horizontal bar plot
    plt.figure(figsize=(10, 6))
    ax = sns.barplot(
        data=grouped,
        y="llm_judge_model",
        x="overall_score",
        hue="llm_judge_model",
        orient="h",
    )

    # Customize the plot
    plt.title("Overall Scores by LLM Judge Model")
    plt.xlabel("Overall Score")
    plt.ylabel("LLM Judge Model")

    # Adjust layout and display the plot
    plt.tight_layout()
    st.pyplot(plt)


# 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 judge each other's responses.")
    st.markdown("###### [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:
        # cols[1].success("Logged in successfully!")
        st.markdown("#### LLM Council Member Selection")

        # Council and aggregator selection
        selected_models = llm_council_selector()

        # st.write("Selected Models:", selected_models)

        selected_aggregator = aggregator_selector()

        # Initialize session state for collecting responses.
        if "responses" not in st.session_state:
            st.session_state.responses = {}
        # if "aggregator_response" not in st.session_state:
        # st.session_state.aggregator_response = {}

        # Prompt input
        st.markdown("#### Enter your prompt")
        _, center_column, _ = st.columns([3, 5, 3])
        with center_column:
            user_prompt = st.text_area(
                "Enter your prompt", value="Say 'Hello World'", key="user_prompt"
            )

        if center_column.button("Submit", use_container_width=True):
            st.markdown("#### Responses")

            response_columns = st.columns(3)

            selected_models_to_streamlit_column_map = {
                model: response_columns[i] for i, model in enumerate(selected_models)
            }

            # Fetching and streaming responses from each selected model
            for selected_model in selected_models:
                with selected_models_to_streamlit_column_map[selected_model]:
                    st.write(get_ui_friendly_name(selected_model))
                    with st.chat_message(
                        selected_model,
                        avatar=PROVIDER_TO_AVATAR_MAP[selected_model],
                    ):
                        message_placeholder = st.empty()
                        stream = get_llm_response_stream(selected_model, user_prompt)
                        if stream:
                            st.session_state["responses"][selected_model] = (
                                message_placeholder.write_stream(stream)
                            )

            # Get the aggregator prompt.
            aggregator_prompt = get_default_aggregator_prompt(
                user_prompt=user_prompt, llms=selected_models
            )

            with st.expander("Aggregator Prompt"):
                st.code(aggregator_prompt)

            # Fetching and streaming response from the aggregator
            st.write(f"Mixture-of-Agents ({get_ui_friendly_name(selected_aggregator)})")
            with st.chat_message(
                selected_aggregator,
                avatar="img/council_icon.png",
            ):
                message_placeholder = st.empty()
                aggregator_stream = get_llm_response_stream(
                    selected_aggregator, aggregator_prompt
                )
                if aggregator_stream:
                    st.session_state["responses"]["agg__" + selected_aggregator] = (
                        message_placeholder.write_stream(aggregator_stream)
                    )

        # st.write("Responses (in session state):")
        # st.write(st.session_state["responses"])

        # Judging.
        st.markdown("#### Judging Configuration")

        # Choose the type of assessment
        assessment_type = st.radio(
            "Select the type of assessment",
            options=["Direct Assessment", "Pairwise Comparison"],
        )

        _, center_column, _ = st.columns([3, 5, 3])

        # Depending on the assessment type, render different forms
        if assessment_type == "Direct Assessment":

            # Initialize session state for direct assessment.
            if "direct_assessment_overall_score" not in st.session_state:
                st.session_state["direct_assessment_overall_score"] = {}
            if "direct_assessment_judging_df" not in st.session_state:
                st.session_state["direct_assessment_judging_df"] = {}
                for response_model in selected_models:
                    st.session_state["direct_assessment_judging_df"][
                        response_model
                    ] = {}
                # aggregator model
                st.session_state["direct_assessment_judging_df"][
                    "agg__" + selected_aggregator
                ] = {}
            if "direct_assessment_judging_responses" not in st.session_state:
                st.session_state["direct_assessment_judging_responses"] = {}
                for response_model in selected_models:
                    st.session_state["direct_assessment_judging_responses"][
                        response_model
                    ] = {}
                # aggregator model
                st.session_state["direct_assessment_judging_responses"][
                    "agg__" + selected_aggregator
                ] = {}
            if "direct_assessment_overall_scores" not in st.session_state:
                st.session_state["direct_assessment_overall_scores"] = {}
                for response_model in selected_models:
                    st.session_state["direct_assessment_overall_scores"][
                        response_model
                    ] = {}
                st.session_state["direct_assessment_overall_scores"][
                    "agg__" + selected_aggregator
                ] = {}
            if "judging_status" not in st.session_state:
                st.session_state["judging_status"] = "incomplete"

            # Direct assessment prompt.
            with center_column.expander("Direct Assessment Prompt"):
                direct_assessment_prompt = st.text_area(
                    "Prompt for the Direct Assessment",
                    value=get_default_direct_assessment_prompt(user_prompt=user_prompt),
                    height=500,
                    key="direct_assessment_prompt",
                )

            # TODO: Add option to edit criteria list with a basic text field.
            criteria_list = DEFAULT_DIRECT_ASSESSMENT_CRITERIA_LIST

            # Create DirectAssessment object when form is submitted
            if center_column.button(
                "Submit Direct Assessment", use_container_width=True
            ):

                # Submit direct asssessment.
                responses_for_judging = st.session_state["responses"]

                # st.write("Responses for judging (in session state):")
                # st.write(responses_for_judging)

                response_judging_columns = st.columns(3)

                responses_for_judging_to_streamlit_column_map = {
                    model: response_judging_columns[i % 3]
                    for i, model in enumerate(responses_for_judging.keys())
                }

                # Get judging responses.
                for response_model, response in responses_for_judging.items():

                    st_column = responses_for_judging_to_streamlit_column_map[
                        response_model
                    ]

                    with st_column:
                        if "agg__" in response_model:
                            judging_model_header = "Mixture-of-Agents Response"
                        else:
                            judging_model_header = get_ui_friendly_name(response_model)
                        st.write(f"Judging for {judging_model_header}")
                        # st.write("Response being judged: ")
                        # st.write(response)
                        judging_prompt = get_direct_assessment_prompt(
                            direct_assessment_prompt=direct_assessment_prompt,
                            user_prompt=user_prompt,
                            response=response,
                            criteria_list=criteria_list,
                            options=SEVEN_POINT_DIRECT_ASSESSMENT_OPTIONS,
                        )

                        with st.expander("Final Judging Prompt"):
                            st.code(judging_prompt)

                        for judging_model in selected_models:
                            with st.expander(
                                get_ui_friendly_name(judging_model), expanded=False
                            ):
                                with st.chat_message(
                                    judging_model,
                                    avatar=PROVIDER_TO_AVATAR_MAP[judging_model],
                                ):
                                    message_placeholder = st.empty()
                                    judging_stream = get_llm_response_stream(
                                        judging_model, judging_prompt
                                    )
                                    # if judging_stream:
                                    st.session_state[
                                        "direct_assessment_judging_responses"
                                    ][response_model][
                                        judging_model
                                    ] = message_placeholder.write_stream(
                                        judging_stream
                                    )
                        # When all of the judging is finished for the given response, get the actual
                        # values, parsed (use gpt-4o-mini for now) with json mode.
                        # TODO.
                        judging_responses = st.session_state[
                            "direct_assessment_judging_responses"
                        ][response_model]

                        # st.write("Judging responses (in session state):")
                        # st.write(judging_responses)

                        if not judging_responses:
                            st.error(f"No judging responses for {response_model}")
                            quit()
                        parse_judging_response_prompt = (
                            get_parse_judging_response_for_direct_assessment_prompt(
                                judging_responses,
                                criteria_list,
                                SEVEN_POINT_DIRECT_ASSESSMENT_OPTIONS,
                            )
                        )
                        with st.expander("Parse Judging Response Prompt"):
                            st.code(parse_judging_response_prompt)
                        # Issue the prompt to openai mini with structured outputs
                        parsed_judging_responses = parse_judging_responses(
                            parse_judging_response_prompt, judging_responses
                        )

                        st.session_state["direct_assessment_judging_df"][
                            response_model
                        ] = create_dataframe_for_direct_assessment_judging_response(
                            parsed_judging_responses
                        )
                        st.write(
                            st.session_state["direct_assessment_judging_df"][
                                response_model
                            ]
                        )

                        plot_criteria_scores(
                            st.session_state["direct_assessment_judging_df"][
                                response_model
                            ]
                        )

                        # Find the overall score by finding the overall score for each judge, and then averaging
                        # over all judges.
                        plot_per_judge_overall_scores(
                            st.session_state["direct_assessment_judging_df"][
                                response_model
                            ]
                        )

                        grouped = (
                            st.session_state["direct_assessment_judging_df"][
                                response_model
                            ]
                            .groupby(["llm_judge_model"])
                            .agg({"score": ["mean"]})
                            .reset_index()
                        )
                        grouped.columns = ["llm_judge_model", "overall_score"]

                        # st.write(
                        #     "Extracting overall scores from this grouped dataframe:"
                        # )
                        # st.write(grouped)

                        # Save the overall scores to the session state.
                        for record in grouped.to_dict(orient="records"):
                            st.session_state["direct_assessment_overall_scores"][
                                response_model
                            ][record["llm_judge_model"]] = record["overall_score"]

                        overall_score = grouped["overall_score"].mean()
                        controversy = grouped["overall_score"].std()
                        st.write(f"Overall Score: {overall_score:.2f}")
                        st.write(f"Controversy: {controversy:.2f}")

                st.session_state["judging_status"] = "complete"

            # Judging is complete.
            st.write("#### Results")
            # The session state now contains the overall scores for each response from each judge.
            if st.session_state["judging_status"] == "complete":
                overall_scores_df_raw = pd.DataFrame(
                    st.session_state["direct_assessment_overall_scores"]
                ).reset_index()

                overall_scores_df = pd.melt(
                    overall_scores_df_raw,
                    id_vars=["index"],
                    var_name="response_model",
                    value_name="score",
                ).rename(columns={"index": "judging_model"})

                # Print the overall winner.
                overall_winner = overall_scores_df.loc[
                    overall_scores_df["score"].idxmax()
                ]

                st.write(
                    f"**Overall Winner:** {get_ui_friendly_name(overall_winner['response_model'])}"
                )
                # Find how much the standard deviation overlaps with other models.
                # Calculate separability.
                # TODO.
                st.write(f"**Confidence:** {overall_winner['score']:.2f}")

                left_column, right_column = st.columns([1, 1])
                with left_column:
                    plot_overall_scores(overall_scores_df)

                with right_column:
                    st.dataframe(overall_scores_df)

        elif assessment_type == "Pairwise Comparison":
            pass
            # pairwise_comparison_prompt = st.text_area(
            #     "Prompt for the Pairwise Comparison"
            # )
            # granularity = st.selectbox("Granularity", ["coarse", "fine", "super fine"])
            # ties_allowed = st.checkbox("Are ties allowed?")
            # position_swapping = st.checkbox("Enable position swapping?")
            # reference_model = st.text_input("Reference Model")

            # # Create PairwiseComparison object when form is submitted
            # if st.button("Submit Pairwise Comparison"):
            #     pairwise_comparison_config = PairwiseComparison(
            #         type="pairwise_comparison",
            #         granularity=granularity,
            #         ties_allowed=ties_allowed,
            #         position_swapping=position_swapping,
            #         reference_model=reference_model,
            #         prompt=prompt,
            #     )
            #     st.success(f"Pairwise Comparison Created: {pairwise_comparison_config}")
            #     # Submit pairwise comparison.
            #     responses_for_judging = st.session_state["responses"]

    else:
        with cols[1]:
            st.warning("Please log in to access this app.")


if __name__ == "__main__":
    main()