import colorsys
import json
import os
import random
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass, make_dataclass
from datetime import datetime
from io import BytesIO

import aiohttp
import evaluate
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from huggingface_hub import hf_hub_download, list_repo_files
from pydub import AudioSegment

from constants import WHISPER_OPEN_AI_LINK

# Load the Word Error Rate (WER) metric from the evaluate library
wer_metric = evaluate.load("wer")


def compute_average_wer(results):
    """
    Compute the average Word Error Rate (WER) for a list of transcription results.

    :param results: List of dictionaries, each containing 'reference' and 'prediction' keys
    :return: Average WER as a percentage, rounded to 2 decimal places

    This function calculates the WER for each reference-prediction pair and returns
    the average. If no predictions are provided, it returns 100% WER.
    """
    references = [result["reference"] for result in results]
    predictions = [result["prediction"] for result in results]
    if len(predictions) == 0:
        return 1
    return round(
        wer_metric.compute(references=references, predictions=predictions) * 100.0,
        2,
    )


def read_json_line_by_line(file_path):
    """
    Read a JSON file line by line, parsing each line as a separate JSON object.

    :param file_path: Path to the JSON file
    :return: List of parsed JSON objects

    This function is useful for reading large JSON files that contain one JSON object
    per line. It handles JSON parsing errors gracefully, skipping invalid lines.
    """
    data = []
    with open(file_path, "r") as f:
        for line in f:
            try:
                item = json.loads(line.strip())
                data.append(item)
            except json.JSONDecodeError:
                print(f"Skipping invalid JSON in {file_path}: {line}")
    return data


def group_wer(group):
    """
    Calculate the Word Error Rate (WER) for a group of transcriptions.

    :param group: DataFrame group containing 'normalized_reference' and 'normalized_prediction' columns
    :return: Average WER for the group

    This function is typically used with DataFrame groupby operations to calculate
    WER for specific groups of transcriptions.
    """
    return compute_average_wer(
        group[["normalized_reference", "normalized_prediction"]]
        .rename(
            columns={
                "normalized_reference": "reference",
                "normalized_prediction": "prediction",
            }
        )
        .to_dict("records")
    )


def load_multilingual_results(csv_file):
    """
    Load multilingual results from a CSV file into a pandas DataFrame.

    :param csv_file: Path to the CSV file containing multilingual results
    :return: DataFrame with the loaded results, or None if the file is not found

    This function attempts to load a CSV file using pandas, handling potential
    FileNotFoundError exceptions.
    """
    try:
        df = pd.json_normalize(csv_file)
        return df
    except FileNotFoundError:
        return None


def download_dataset(repo_id, local_dir, remote_dir, path_includes=""):
    """
    Download benchmark result files from a specified Hugging Face repository to a local directory.

    :param repo_id: ID of the Hugging Face repository
    :param local_dir: Local directory where downloaded files will be saved
    :param remote_dir: Remote directory within the repository to download from

    This function uses the Hugging Face Hub API to list and download files from a
    specific directory in a repository. It forces the download to ensure up-to-date files.
    """
    files = list_repo_files(repo_id, repo_type="dataset")
    directory_files = [
        file for file in files if file.startswith(remote_dir) and path_includes in file
    ]
    with ThreadPoolExecutor() as executor:
        executor.map(
            lambda file: hf_hub_download(
                repo_id=repo_id,
                repo_type="dataset",
                filename=file,
                local_dir=local_dir,
                force_download=True,
            ),
            directory_files,
        )


def process_file(file_path):
    """
    Process a file containing JSON objects delimited by new lines.

    :param file_path: Path to the file to be processed
    :return: List of dictionaries, each representing a parsed JSON object

    This function reads the file line by line, parsing each line as a JSON object.
    It handles potential JSON decoding errors, printing error messages for invalid lines.
    """
    data = []
    with open(file_path, "r") as file:
        for line in file:
            line = line.strip()
            if not line:
                continue
            try:
                json_obj = json.loads(line)
                data.append(json_obj)
            except json.JSONDecodeError as e:
                print(f"Error decoding JSON in line: {line}")
                print(f"Error message: {str(e)}")
    return data


def dir_to_json(root_dir, output_file):
    """
    Convert a directory of benchmark result files to a single JSON file.

    :param root_dir: Root directory containing the benchmark result files
    :param output_file: Output file where the JSON data will be saved

    This function walks through the directory structure, processes each file,
    and writes the combined data to a single JSON file. It extracts metadata
    from the file path and includes it in the JSON output.
    """
    with open(output_file, "w") as outfile:
        for subdir, _, files in os.walk(root_dir):
            for file in files:
                file_path = os.path.join(subdir, file)
                # ignore .DS_Store and summary files
                if file_path.endswith(".DS_Store") or "summary" in file_path:
                    continue
                parts = file_path.split(os.sep)
                model_version = parts[2]
                device_name = parts[3].replace("_", " ")
                os_type_version = parts[4]
                dataset_name = parts[5]
                timestamp_commit = parts[6].replace(".json", "")
                timestamp, commit_hash, commit_timestamp = timestamp_commit.split("_")

                data_list = process_file(file_path)
                for data in data_list:
                    original_entry = {
                        "model": model_version.replace("_", "/"),
                        "device": device_name,
                        "os": os_type_version.replace("_", " "),
                        "wer": data["wer"],
                        "dataset_name": dataset_name,
                        "reference_transcription": data["reference_transcription"],
                        "prediction_transcription": data["prediction_transcription"],
                        "difference_transcription": data["difference_transcription"],
                        "audio_file_url": data["audio_file_url"],
                        "timestamp": timestamp.replace("-", ":").replace(":", "-", 2),
                        "commit_hash": commit_hash,
                        "commit_timestamp": commit_timestamp,
                    }

                    outfile.write(json.dumps(original_entry) + "\n")


async def download_audio_to_ndarray(url):
    """
    Downloads an audio file from a URL and converts it to a NumPy array.

    :param url: The URL of the audio file to download
    :return: A tuple containing the sample rate and audio data as a NumPy array

    This asynchronous function uses aiohttp to download the audio file,
    converts it to an AudioSegment, and then to a NumPy array. It handles
    both mono and stereo audio files.
    """
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            if response.status == 200:
                audio_bytes = BytesIO(await response.read())
                audio = AudioSegment.from_file(audio_bytes, format="mp3")
                audio_data = np.array(audio.get_array_of_samples())
                if audio.channels == 2:
                    audio_data = audio_data.reshape((-1, 2))
                return audio.frame_rate, audio_data
            else:
                return None, None


async def play_audio(url):
    """
    Wrapper function for Gradio to play audio from a URL.

    :param url: The URL of the audio file to play
    :return: A tuple of sample rate and audio data, or an error message

    This function uses download_audio_to_ndarray to get the audio data
    and returns it in a format suitable for Gradio's audio player.
    """
    sample_rate, audio_data = await download_audio_to_ndarray(url)
    if audio_data is None:
        return "Error downloading the file"
    else:
        return sample_rate, audio_data


def get_filter_cond(df, model, device, os, dataset, timestamp=None):
    """
    Creates a filter condition for a DataFrame based on specified parameters.

    :param df: DataFrame containing the transcription data
    :param model: String representing the model name
    :param device: String representing the device name
    :param os: String representing the OS name
    :param dataset: String representing the dataset name
    :param timestamp: Optional timestamp for filtering (default: None)
    :return: A boolean mask for filtering the DataFrame

    This function constructs a complex boolean condition for filtering
    the DataFrame based on the provided parameters.
    """
    filter_cond = (
        (df["model"] == model)
        & (df["device"] == device)
        & (df["os"] == os)
        & (df["dataset_name"] == dataset)
    )
    return filter_cond & (df["timestamp"] == timestamp) if timestamp else filter_cond


def get_filtered_transcript(df, model, device, os, dataset, timestamp):
    """
    Retrieves filtered transcription data from a DataFrame.

    :param df: DataFrame containing the transcription data
    :param model: String representing the model name
    :param device: String representing the device name
    :param os: String representing the OS name
    :param dataset: String representing the dataset name
    :param timestamp: String representing the timestamp
    :return: A filtered DataFrame with transcription data

    This function applies a filter to the input DataFrame and returns
    relevant columns for transcription analysis.
    """
    filter_cond = get_filter_cond(df, model, device, os, dataset, timestamp)
    df = df[filter_cond][
        [
            "reference_transcription",
            "prediction_transcription",
            "difference_transcription",
            "audio_file_url",
        ]
    ]
    return df


def get_filtered_timestamps(df, model, device, os, dataset):
    """
    Retrieves unique timestamps for a specific model, device, OS, and dataset combination.

    :param df: DataFrame containing the transcription data
    :param model: String representing the model name
    :param device: String representing the device name
    :param os: String representing the OS name
    :param dataset: String representing the dataset name
    :return: A filtered DataFrame containing unique timestamps

    This function is useful for getting a list of available timestamps
    for a specific configuration, which can be used for further analysis or UI elements.
    """
    filter_cond = get_filter_cond(df, model, device, os, dataset)
    df = df[filter_cond][["timestamp"]].drop_duplicates()
    return df


def make_model_name_clickable_link(model):
    """
    Creates an HTML link to the Hugging Face model page.

    :param model: String representing the model name
    :return: An HTML string containing a clickable link to the model page

    This function generates a formatted HTML link that can be used in
    web interfaces to provide direct access to the model's page on Hugging Face.
    """
    return f"""<a style="color: #3B82F6; text-decoration: underline; text-decoration-style: dotted;" href="https://huggingface.co/argmaxinc/whisperkit-coreml/tree/main/{model.replace('/', '_')}" target="_blank">{model}</a>"""


def make_dataset_wer_clickable_link(row, dataset):
    """
    Creates a clickable link for the WER value of a dataset.

    :param row: Row containing the dataset WER value
    :param dataset: String representing the dataset name
    :return: An HTML string containing a clickable link to the dataset's WER details

    This function generates a formatted HTML link that can be used in
    web interfaces to provide access to detailed WER information for a specific dataset.
    """
    dataset_column = f"{dataset}"
    href = WHISPER_OPEN_AI_LINK.format(
        row["Model"].replace("/", "_"),
        dataset,
    )
    return f'<a style="color: #3B82F6; text-decoration: underline; text-decoration-style: dotted;" href="{href}">{row[dataset_column]}</a>'


def make_timestamp_clickable_link(model, dataset, timestamp):
    """
    Creates a clickable link for a timestamp.

    :param model: String representing the model name
    :param dataset: String representing the dataset name
    :param timestamp: Timestamp to be displayed and used in the link
    :return: An HTML string containing a clickable div for the timestamp

    This function generates a formatted HTML div that can be used as a clickable
    element in web interfaces, typically for displaying and interacting with specific timestamps.
    """
    elem_id = (
        f"{dataset}-{model}-{timestamp}".replace(" ", "_")
        .replace('"', "")
        .replace("'", "")
        .replace(",", "")
    )
    onclick = f"onclick=\"document.getElementById('{elem_id}').click();\""
    return f'<div style="color: #3B82F6; text-decoration: underline; text-decoration-style: dotted;" {onclick} href="#">{timestamp}</div>'


def make_multilingual_model_clickable_link(model):
    """
    Creates a clickable link for a multilingual model name.

    :param model: String representing the model name
    :return: An HTML string containing a clickable div for the model name

    This function generates a formatted HTML div that can be used as a clickable
    element in web interfaces, typically for displaying and interacting with multilingual model names.
    """
    elem_id = (
        f"{model}".replace(" ", "_").replace('"', "").replace("'", "").replace(",", "")
    )
    onclick = f"onclick=\"document.getElementById('{elem_id}').click();console.log('hello');\""
    return f'<div style="color: #3B82F6; text-decoration: underline; text-decoration-style: dotted;" {onclick} href="#">{model}</div>'


def plot_metric(
    df, y_axis_col, y_axis_title, fig_title, filter_input=None, exclude_input=None
):
    """
    Plots a metric for each model-device-OS group in a DataFrame.

    :param df: DataFrame containing the benchmark data
    :param y_axis_col: DataFrame column to use as the y-axis
    :param y_axis_title: Display name for the y-axis
    :param fig_title: Display title for the figure
    :param filter_input: Optional string to filter the model-device-OS combinations
    :param exclude_input: Optional string to exclude model-device-OS combinations
    :return: A Plotly figure object
    """
    with open("dashboard_data/version.json", "r") as f:
        version = json.load(f)
        releases = set(version["releases"])
    
    df = df[df["commit_hash"].isin(releases)]

    grouped = df.groupby(["model", "device", "os"])
    sorted_groups = [
        group.sort_values("commit_timestamp") 
        for _, group in grouped 
    ]
    for i, group in enumerate(sorted_groups):
        print(group["model"], group["device"], group["qoi"])

    if filter_input:
        filters = [f.strip().lower() for f in filter_input.split(";")]
        sorted_groups = [
            group
            for group in sorted_groups
            if any(
                f
                in f"{group['model'].iloc[0]}-{group['device'].iloc[0]}-{group['os'].iloc[0]}".lower()
                for f in filters
            )
        ]

    if exclude_input:
        excludes = [e.strip().lower() for e in exclude_input.split(";")]
        sorted_groups = [
            group
            for group in sorted_groups
            if not any(
                e
                in f"{group['model'].iloc[0]}-{group['device'].iloc[0]}-{group['os'].iloc[0]}".lower()
                for e in excludes
            )
        ]

    base_colors = ["#4542f4", "#0e0c06", "#ccf0a7", "#ff7f4e", "#ffd15a"]
    num_colors = len(sorted_groups)
    random_colors = generate_random_colors(base_colors, num_colors)
    fig = go.Figure()
    for i, group in enumerate(sorted_groups):
        model_device_os = (
            f"{group['model'].iloc[0]}-{group['device'].iloc[0]}-{group['os'].iloc[0]}"
        )
        fig.add_trace(
            go.Scatter(
                x=group["commit_timestamp"].apply(
                    lambda x: datetime.strptime(x, "%Y-%m-%dT%H%M%S").strftime(
                        "%Y-%m-%d %H:%M:%S"
                    )
                ),
                y=group[y_axis_col],
                mode="lines+markers",
                name=model_device_os,
                line=dict(color=random_colors[i % len(random_colors)]),
                marker=dict(color=random_colors[i % len(random_colors)]),
                hovertemplate=(
                    f"<b>{model_device_os}</b><br>"
                    "Timestamp: %{x}<br>"
                    f"{y_axis_title}: %{{y:.2f}}<br>"
                    "<extra></extra>"
                ),
            )
        )
    fig.update_layout(
        title=fig_title,
        xaxis_title="Commit Timestamp",
        yaxis_title=y_axis_title,
        legend_title="Model-Device-OS",
        width=1100,
        height=600,
        plot_bgcolor="rgb(250,249,244)",
    )
    return fig


def fields(raw_class):
    """
    Returns the fields of a dataclass.

    :param raw_class: The dataclass to inspect
    :return: List of fields in the dataclass

    This utility function extracts and returns all the fields defined in a dataclass,
    excluding special methods and attributes.
    """
    return [
        v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"
    ]


def get_os_name_and_version(os_string):
    """
    Extracts the OS name and major version from a string.

    :param os_string: String representing the OS name and version
    :return: Formatted string with OS name and major version

    This function splits the input string into OS name and version,
    then returns a formatted string with just the major version number.
    """
    os_name, os_version = os_string.split()
    os_version = os_version.split(".")[0]
    return f"{os_name} {os_version}"


def create_initial_quality_column_dict():
    """
    Creates the initial column dictionary for the quality table.

    :return: A list of column dictionaries

    This function defines the basic structure of the quality table,
    including columns for model, average WER, and QoI (Quality of Implementation).
    """
    return [
        [
            "model",
            ColumnContent,
            ColumnContent("Model", "html", True, never_hidden=True),
        ],
        ["average_wer", ColumnContent, ColumnContent("Average WER", "html", True)],
        ["qoi", ColumnContent, ColumnContent("QoI", "html", True)],
    ]


def calculate_parity(m2_ultra_wer, row):
    """
    Calculates the WER parity between M2 Ultra and the current model.

    :param m2_ultra_wer: DataFrame containing WER values for M2 Ultra
    :param row: Current row being processed
    :return: WER difference between M2 Ultra and current model, or None if not applicable

    This function computes the percentage difference in WER between the M2 Ultra model
    and the current model, providing a measure of relative performance.
    """
    if row["Model"] in m2_ultra_wer.index:
        return round(m2_ultra_wer[row["Model"]] - row["Average WER"], 2)
    return None


def create_initial_performance_column_dict():
    """
    Creates the initial column dictionary for the performance table.

    :return: A list of column dictionaries

    This function defines the basic structure of the performance table,
    including columns for model, device, OS, average WER, QoI, speed, and tokens per second.
    """
    return [
        [
            "model",
            ColumnContent,
            ColumnContent("Model", "html", True, never_hidden=True),
        ],
        [
            "device",
            ColumnContent,
            ColumnContent("Device", "html", True, never_hidden=True),
        ],
        ["os", ColumnContent, ColumnContent("OS", "html", True, never_hidden=True)],
        ["english_wer", ColumnContent, ColumnContent("English WER", "html", True)],
        ["multilingual_wer", ColumnContent, ColumnContent("Multilingual WER", "str", True)],
        ["qoi", ColumnContent, ColumnContent("QoI", "html", False)],
        ["speed", ColumnContent, ColumnContent("Speed", "html", False)],
        ["toks", ColumnContent, ColumnContent("Tok / s", "html", False)],
    ]


def add_datasets_to_quality_columns(column_dict, datasets):
    """
    Adds dataset-specific columns to the quality table column dictionary.

    :param column_dict: The initial column dictionary
    :param datasets: List of dataset names to add
    :return: A dictionary containing the updated column dictionary and related metadata

    This function extends the quality table structure with columns for each dataset,
    and creates a dataclass to represent the table structure. It also generates
    metadata about the columns for use in the UI.
    """
    updated_column_dict = column_dict.copy()

    for dataset in datasets:
        field_name = dataset.replace("-", "")
        updated_column_dict.append(
            [field_name, ColumnContent, ColumnContent(dataset, "html", True)]
        )

    AutoEvalColumn = make_dataclass("AutoEvalColumn", updated_column_dict, frozen=True)

    COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
    TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
    ALWAYS_HERE_COLS = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
    TOGGLE_COLS = [c.name for c in fields(AutoEvalColumn) if not c.never_hidden]
    SELECTED_COLS = [
        c.name
        for c in fields(AutoEvalColumn)
        if not c.never_hidden and c.displayed_by_default
    ]

    return {
        "column_dict": updated_column_dict,
        "AutoEvalColumn": AutoEvalColumn,
        "COLS": COLS,
        "TYPES": TYPES,
        "ALWAYS_HERE_COLS": ALWAYS_HERE_COLS,
        "TOGGLE_COLS": TOGGLE_COLS,
        "SELECTED_COLS": SELECTED_COLS,
    }


def add_datasets_to_performance_columns(column_dict, datasets):
    """
    Adds dataset-specific columns to the performance table column dictionary.

    :param column_dict: The initial column dictionary
    :param datasets: List of dataset names to add
    :return: A dictionary containing the updated column dictionary and related metadata

    This function extends the performance table structure with columns for each dataset,
    adding both speed and tokens per second metrics. It also creates a dataclass to
    represent the table structure and generates metadata about the columns for use in the UI.
    """
    updated_column_dict = column_dict.copy()

    for dataset in datasets:
        field_name = dataset.replace("-", "")
        updated_column_dict.append(
            [
                f"{field_name}_speed",
                ColumnContent,
                ColumnContent(
                    f"{'Short-Form' if dataset == 'librispeech-10mins' else 'Long-Form'} Speed",
                    "html",
                    True,
                ),
            ]
        )
        updated_column_dict.append(
            [
                f"{field_name}_toks",
                ColumnContent,
                ColumnContent(
                    f"{'Short-Form' if dataset == 'librispeech-10mins' else 'Long-Form'} Tok/s",
                    "html",
                    True,
                ),
            ]
        )

    AutoEvalColumn = make_dataclass("AutoEvalColumn", updated_column_dict, frozen=True)

    COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
    TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
    ALWAYS_HERE_COLS = [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
    TOGGLE_COLS = [c.name for c in fields(AutoEvalColumn) if not c.never_hidden]
    SELECTED_COLS = [
        c.name
        for c in fields(AutoEvalColumn)
        if not c.never_hidden and c.displayed_by_default
    ]

    return {
        "column_dict": updated_column_dict,
        "AutoEvalColumn": AutoEvalColumn,
        "COLS": COLS,
        "TYPES": TYPES,
        "ALWAYS_HERE_COLS": ALWAYS_HERE_COLS,
        "TOGGLE_COLS": TOGGLE_COLS,
        "SELECTED_COLS": SELECTED_COLS,
    }


def create_confusion_matrix_plot(matrix, labels, is_forced):
    """
    Creates a confusion matrix plot for language detection.

    :param matrix: 2D numpy array representing the confusion matrix
    :param labels: List of language labels
    :param is_forced: Boolean indicating whether language hint was used
    :return: A Plotly figure object representing the confusion matrix

    This function generates a heatmap visualization of the confusion matrix
    for language detection, with customized layout and hover information.
    """
    fig = go.Figure(
        data=go.Heatmap(
            z=matrix,
            x=labels,
            y=labels,
            colorscale=[
                [0, "rgb(250,249,244)"],
                [0.5, "rgb(69,66,244)"],
                [1.0, "rgb(14,12,6)"],
            ],
            hoverongaps=False,
            hovertemplate="True: %{y}<br>Predicted: %{x}<br>Value: %{z}<extra></extra>",
        )
    )
    fig.update_layout(
        title=f'Language Detection Confusion Matrix with {"Language Hint" if is_forced else "Language Prediction by Model"}',
        xaxis_title="Predicted Language",
        yaxis_title="True Language",
        xaxis=dict(tickangle=-45),
        width=600,
        height=600,
        margin=dict(l=50, r=50, t=50, b=50),
    )
    return fig


def hex_to_rgb(hex_color):
    """
    Converts a hexadecimal color code to RGB values.

    :param hex_color: String representing a color in hexadecimal format
    :return: Tuple of three integers representing RGB values

    This function takes a hex color code and returns the corresponding
    RGB values as a tuple of integers.
    """
    hex_color = hex_color.lstrip("#")
    return tuple(int(hex_color[i : i + 2], 16) for i in (0, 2, 4))


def rgb_to_hex(rgb):
    """
    Converts RGB values to a hexadecimal color code.

    :param rgb: Tuple of three integers representing RGB values
    :return: String representing the color in hexadecimal format

    This function takes RGB values as a tuple and returns the corresponding
    hex color code as a string.
    """
    return "#{:02x}{:02x}{:02x}".format(*rgb)


def interpolate_colors(color1, color2, factor):
    """
    Interpolates between two colors in HSV space.

    :param color1: First color in hexadecimal format
    :param color2: Second color in hexadecimal format
    :param factor: Float between 0 and 1, representing the interpolation factor
    :return: Interpolated color in hexadecimal format

    This function performs color interpolation in HSV color space, which can
    produce more visually pleasing results than simple RGB interpolation.
    """
    rgb1 = hex_to_rgb(color1)
    rgb2 = hex_to_rgb(color2)

    hsv1 = colorsys.rgb_to_hsv(*[x / 255.0 for x in rgb1])
    hsv2 = colorsys.rgb_to_hsv(*[x / 255.0 for x in rgb2])

    h = (hsv1[0] + factor * (hsv2[0] - hsv1[0])) % 1.0
    s = hsv1[1] + factor * (hsv2[1] - hsv1[1])
    v = hsv1[2] + factor * (hsv2[2] - hsv1[2])

    rgb = colorsys.hsv_to_rgb(h, s, v)
    return rgb_to_hex(tuple(int(x * 255) for x in rgb))


def color_distance(color1, color2):
    """
    Calculates the Euclidean distance between two colors in RGB space.

    :param color1: First color in hexadecimal format
    :param color2: Second color in hexadecimal format
    :return: Float representing the distance between the two colors

    This function computes the Euclidean distance between two colors in RGB space,
    which can be used as a measure of color similarity.
    """
    rgb1 = hex_to_rgb(color1)
    rgb2 = hex_to_rgb(color2)
    return sum((a - b) ** 2 for a, b in zip(rgb1, rgb2)) ** 0.5


def generate_random_colors(base_colors, num_colors, min_distance=30):
    """
    Generates a list of random colors based on a set of base colors.

    :param base_colors: List of base colors in hexadecimal format
    :param num_colors: Number of colors to generate
    :param min_distance: Minimum distance between generated colors (default: 30)
    :return: List of generated colors in hexadecimal format

    This function creates a list of random colors by interpolating between
    the provided base colors. It attempts to maintain a minimum distance
    between colors to ensure visual distinctiveness.
    """
    generated_colors = []
    attempts = 0
    max_attempts = 1000

    while len(generated_colors) < num_colors and attempts < max_attempts:
        color1, color2 = random.sample(base_colors, 2)
        factor = random.random()
        new_color = interpolate_colors(color1, color2, factor)

        if all(color_distance(new_color, c) >= min_distance for c in generated_colors):
            generated_colors.append(new_color)
            attempts = 0
        else:
            attempts += 1

        if attempts > 100:
            if random.random() < 0.1:
                generated_colors.append(new_color)
                attempts = 0

    return generated_colors


@dataclass
class Task:
    """
    Dataclass representing a benchmark task.

    :param benchmark: String representing the benchmark name
    :param metric: String representing the metric used for evaluation
    :param col_name: String representing the column name in the results DataFrame
    """

    benchmark: str
    metric: str
    col_name: str


@dataclass(frozen=True)
class ColumnContent:
    """
    Dataclass representing a column in the results table.

    :param name: String representing the column name
    :param type: String representing the data type of the column
    :param displayed_by_default: Boolean indicating if the column should be displayed by default
    :param hidden: Boolean indicating if the column should be hidden (default: False)
    :param never_hidden: Boolean indicating if the column should never be hidden (default: False)
    :param dummy: Boolean indicating if this is a dummy column (default: False)
    """

    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False
    dummy: bool = False


css = """
@font-face {
    font-family: 'Zwizz Regular';
    font-style: normal;
    font-weight: normal;
    src: local('Zwizz Regular'), url('static/Zwizz-Regular.woff') format('woff');
}

@font-face {
    font-family: 'Zwizz Medium';
    font-style: normal;
    font-weight: normal;
    src: local('Zwizz Medium'), url('static/Zwizz-Medium.woff') format('woff');
}

@font-face {
    font-family: 'Zwizz SemiBold';
    font-style: normal;
    font-weight: normal;
    src: local('Zwizz SemiBold'), url('static/Zwizz-SemiBold.woff') format('woff');
}
    
@import url('https://fonts.googleapis.com/css2?family=Noto+Color+Emoji&display=swap');
@import url('https://fonts.googleapis.com/css2?family=Sora:wght@300..400&display=swap');

/* Typography Scale */
h1, .h1 {
    font-family: 'Sora', sans-serif;
    font-weight: 300;
    font-size: 2em;
    letter-spacing: -0.05em;
}

h2, .h2 {
    font-family: 'Sora', sans-serif;
    font-weight: 400;
    letter-spacing: -0.05em;
}

h3, h4, h5, .h3, .h4, .h5 {
    font-family: 'Sora', sans-serif;
    font-weight: 400;
    letter-spacing: -0.05em;
}

h6, .h6, pre, code, .monospace {
    font-family: 'IBM Plex Mono', monospace;
    font-weight: 400;
    letter-spacing: 0.01em;
}

/* Add strong tag styling */
strong, b {
    font-family: 'Zwizz SemiBold', -apple-system, BlinkMacSystemFont, system-ui, sans-serif;
    letter-spacing: -0.02em;
}

/* Global Zwizz styles */
:root {
    --zwizz-spacing: -0.02em;
}

/* All Gradio elements should have Zwizz spacing */
.gradio-container * {
    letter-spacing: var(--zwizz-spacing);
    line-height: 1.7;
}

/* UI Elements */
.tab-buttons button, #models-to-add-text, .gradio-button {
    font-family: 'Sora', sans-serif;
    font-weight: 400;
    letter-spacing: -0.05em;
}

/* Specific Table Styling */
table, .table, th, td {
    font-family: 'IBM Plex Mono', 'Noto Color Emoji', sans-serif, monospace !important;
    font-weight: 400;
    letter-spacing: 0.01em;
}

/* Technical/Code Elements */
.code-block, .technical-text {
    font-family: 'IBM Plex Mono', monospace;
    font-weight: 400;
    letter-spacing: 0.01em;
}

/* Additional Elements */
#methodology-text p, #methodology-text li, .markdown-text {
    font-family: 'Zwizz Regular', -apple-system, BlinkMacSystemFont, system-ui, sans-serif;
    font-size: 16px !important;
    letter-spacing: var(--zwizz-spacing);
    line-height: 1.7;
}

/* Font weight utilities */
.zwizz-medium {
    font-family: 'Zwizz Medium', -apple-system, BlinkMacSystemFont, system-ui, sans-serif;
}

.zwizz-semibold {
    font-family: 'Zwizz SemiBold', -apple-system, BlinkMacSystemFont, system-ui, sans-serif;
}

/* Maintaining Original Layout Rules */
.gradio-container {
    max-width: 95% !important;
}

/* Table Layouts */
.large-table, 
.large-table .table-wrap, 
#multilingual-model-table .table-wrap, 
#lookup-table .table-wrap {
    height: 35em !important;
    overflow-y: scroll !important;
}

/* SVG Container Rules */
.svg-container,
.main-svg {
    width: 100% !important;
}

.large-table, .large-table .table-wrap, #multilingual-model-table .table-wrap, #lookup-table .table-wrap {
    height: 35em !important;
    overflow-y: scroll !important;
}

.left-side-table .table-wrap {
    height: 15em !important;
    overflow-y: scroll !important;
}

#average-wer-table .table-wrap {
    height: 8em !important;
    overflow-y: scroll !important;
}

#general-wer-table .table-wrap {
    height: 35em !important;
    overflow-y: scroll !important;
}
"""