import io
import re
from collections.abc import Iterable

import pandas as pd
import streamlit as st
from pandas.api.types import is_bool_dtype, is_datetime64_any_dtype, is_numeric_dtype

GITHUB_URL = "https://github.com/msamwelmollel/Swahili_LLM_Leaderboard"
NON_BENCHMARK_COLS = ["Open?", "Publisher"]


def extract_table_and_format_from_markdown_text(markdown_table: str) -> pd.DataFrame:
    """Extracts a table from a markdown text and formats it as a pandas DataFrame.
    Args:
        text (str): Markdown text containing a table.
    Returns:
        pd.DataFrame: Table as pandas DataFrame.
    """
    df = (
        pd.read_table(io.StringIO(markdown_table), sep="|", header=0, index_col=1)
        .dropna(axis=1, how="all")  # drop empty columns
        .iloc[1:]  # drop first row which is the "----" separator of the original markdown table
        .sort_index(ascending=True)
        .apply(lambda x: x.str.strip() if x.dtype == "object" else x)
        .replace("", float("NaN"))
        .astype(float, errors="ignore")
    )

    # remove whitespace from column names and index
    df.columns = df.columns.str.strip()
    df.index = df.index.str.strip()
    df.index.name = df.index.name.strip()

    return df


def extract_markdown_table_from_multiline(multiline: str, table_headline: str, next_headline_start: str = "#") -> str:
    """Extracts the markdown table from a multiline string.
    Args:
        multiline (str): content of README.md file.
        table_headline (str): Headline of the table in the README.md file.
        next_headline_start (str, optional): Start of the next headline. Defaults to "#".
    Returns:
        str: Markdown table.
    Raises:
        ValueError: If the table could not be found.
    """
    # extract everything between the table headline and the next headline
    table = []
    start = False
    for line in multiline.split("\n"):
        if line.startswith(table_headline):
            start = True
        elif line.startswith(next_headline_start):
            start = False
        elif start:
            table.append(line + "\n")

    if len(table) == 0:
        raise ValueError(f"Could not find table with headline '{table_headline}'")

    return "".join(table)


def remove_markdown_links(text: str) -> str:
    """Modifies a markdown text to remove all markdown links.
    Example: [DISPLAY](LINK) to DISPLAY
    First find all markdown links with regex.
    Then replace them with: $1
    Args:
        text (str): Markdown text containing markdown links
    Returns:
        str: Markdown text without markdown links.
    """

    # find all markdown links
    markdown_links = re.findall(r"\[([^\]]+)\]\(([^)]+)\)", text)

    # remove link keep display text
    for display, link in markdown_links:
        text = text.replace(f"[{display}]({link})", display)

    return text


def filter_dataframe_by_row_and_columns(df: pd.DataFrame, ignore_columns: list[str] | None = None) -> pd.DataFrame:
    """
    Filter dataframe by the rows and columns to display.
    This does not select based on the values in the dataframe, but rather on the index and columns.
    Modified from https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
    Args:
        df (pd.DataFrame): Original dataframe
        ignore_columns (list[str], optional): Columns to ignore. Defaults to None.
    Returns:
        pd.DataFrame: Filtered dataframe
    """
    df = df.copy()

    if ignore_columns is None:
        ignore_columns = []

    modification_container = st.container()

    with modification_container:
        to_filter_index = st.multiselect("Filter by model:", sorted(df.index))
        if to_filter_index:
            df = pd.DataFrame(df.loc[to_filter_index])

        to_filter_columns = st.multiselect(
            "Filter by benchmark:", sorted([c for c in df.columns if c not in ignore_columns])
        )
        if to_filter_columns:
            df = pd.DataFrame(df[ignore_columns + to_filter_columns])

    return df


def filter_dataframe_by_column_values(df: pd.DataFrame) -> pd.DataFrame:
    """
    Filter dataframe by the values in the dataframe.
    Modified from https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
    Args:
        df (pd.DataFrame): Original dataframe
    Returns:
        pd.DataFrame: Filtered dataframe
    """
    df = df.copy()

    modification_container = st.container()

    with modification_container:
        to_filter_columns = st.multiselect("Filter results on:", df.columns)
        left, right = st.columns((1, 20))

        for column in to_filter_columns:
            if is_bool_dtype(df[column]):
                user_bool_input = right.checkbox(f"{column}", value=True)
                df = df[df[column] == user_bool_input]

            elif is_numeric_dtype(df[column]):
                _min = float(df[column].min())
                _max = float(df[column].max())

                if (_min != _max) and pd.notna(_min) and pd.notna(_max):
                    step = 0.01
                    user_num_input = right.slider(
                        f"Values for {column}:",
                        min_value=round(_min - step, 2),
                        max_value=round(_max + step, 2),
                        value=(_min, _max),
                        step=step,
                    )
                    df = df[df[column].between(*user_num_input)]

            elif is_datetime64_any_dtype(df[column]):
                user_date_input = right.date_input(
                    f"Values for {column}:",
                    value=(
                        df[column].min(),
                        df[column].max(),
                    ),
                )
                if isinstance(user_date_input, Iterable) and len(user_date_input) == 2:
                    user_date_input_datetime = tuple(map(pd.to_datetime, user_date_input))
                    start_date, end_date = user_date_input_datetime
                    df = df.loc[df[column].between(start_date, end_date)]

            else:
                selected_values = right.multiselect(
                    f"Values for {column}:",
                    sorted(df[column].unique()),
                )

                if selected_values:
                    df = df[df[column].isin(selected_values)]

    return df


def setup_basic():
    title = "🏆 LLM-Leaderboard"

    st.set_page_config(
        page_title=title,
        page_icon="🏆",
        layout="wide",
    )
    st.title(title)

    st.markdown(
        "A joint community effort to create one central leaderboard for LLMs."
        f" Visit [swahili-llm-leaderboard]({GITHUB_URL}) to contribute.  \n"
        'We refer to a model being "open" if it can be locally deployed and used for commercial purposes.'
    )


def setup_leaderboard(readme: str):
    leaderboard_table = extract_markdown_table_from_multiline(readme, table_headline="## Leaderboard")
    leaderboard_table = remove_markdown_links(leaderboard_table)
    df_leaderboard = extract_table_and_format_from_markdown_text(leaderboard_table)
    df_leaderboard["Open?"] = df_leaderboard["Open?"].map({"yes": 1, "no": 0}).astype(bool)

    st.markdown("## Leaderboard")
    modify = st.checkbox("Add filters")
    clear_empty_entries = st.checkbox("Clear empty entries", value=True)

    if modify:
        df_leaderboard = filter_dataframe_by_row_and_columns(df_leaderboard, ignore_columns=NON_BENCHMARK_COLS)
        df_leaderboard = filter_dataframe_by_column_values(df_leaderboard)

    if clear_empty_entries:
        df_leaderboard = df_leaderboard.dropna(axis=1, how="all")
        benchmark_columns = [c for c in df_leaderboard.columns if df_leaderboard[c].dtype == float]
        rows_wo_any_benchmark = df_leaderboard[benchmark_columns].isna().all(axis=1)
        df_leaderboard = df_leaderboard[~rows_wo_any_benchmark]

    st.dataframe(df_leaderboard)

    st.download_button(
        "Download current selection as .html",
        df_leaderboard.to_html().encode("utf-8"),
        "leaderboard.html",
        "text/html",
        key="download-html",
    )

    st.download_button(
        "Download current selection as .csv",
        df_leaderboard.to_csv().encode("utf-8"),
        "leaderboard.csv",
        "text/csv",
        key="download-csv",
    )


def setup_benchmarks(readme: str):
    benchmarks_table = extract_markdown_table_from_multiline(readme, table_headline="## Benchmarks")
    df_benchmarks = extract_table_and_format_from_markdown_text(benchmarks_table)

    st.markdown("## Covered Benchmarks")

    selected_benchmark = st.selectbox("Select a benchmark to learn more:", df_benchmarks.index.unique())
    df_selected = df_benchmarks.loc[selected_benchmark]
    text = [
        f"Name: {selected_benchmark}",
    ]
    for key in df_selected.keys():
        text.append(f"{key}: {df_selected[key]} ")
    st.markdown("  \n".join(text))


def setup_sources():
    st.markdown("## Sources")
    st.markdown(
        "The results of this leaderboard are collected from the individual papers and published results of the model "
        "authors. If you are interested in the sources of each individual reported model value, please visit the "
        f"[llm-leaderboard]({GITHUB_URL}) repository."
    )
    st.markdown(
        """
    Special thanks to the following pages:
    - [MosaicML - Model benchmarks](https://www.mosaicml.com/blog/mpt-7b)
    - [lmsys.org - Chatbot Arena benchmarks](https://lmsys.org/blog/2023-05-03-arena/)
    - [Papers With Code](https://paperswithcode.com/)
    - [Stanford HELM](https://crfm.stanford.edu/helm/latest/)
    - [HF Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
    """
    )

def setup_Contribution():
    st.markdown("## How to Contribute")

    markdown_content = """
    - Model name (don't forget the links):
      - Filling in missing entries
      - Adding a new model as a new row to the leaderboard. Please keep the descending order.
      - Adding a new benchmark as a new column in the leaderboard and adding the benchmark to the benchmarks table. Please keep the descending order.
    - Code work:
      - Improving the existing code
      - Requesting and implementing new features
    """
    st.markdown(markdown_content)
    
def setup_Sponsorship():
    st.markdown("## Sponsorship")
    st.markdown(
        # "The results of this leaderboard are collected from the individual papers and published results of the model "
        # "authors. If you are interested in the sources of each individual reported model value, please visit the "
        # f"[llm-leaderboard]({GITHUB_URL}) repository."
        "The benchmark is English-based, and we need support translating it into Swahili." 
        "We welcome sponsorships to help advance this endeavor."
        "Your sponsorship would facilitate this essential translation effort, bridging language barriers and making the benchmark "
        "accessible to a broader audience. We're grateful for the dedication shown by our collaborators and aim to extend this impact "
        "further with the support of sponsors committed to advancing language technologies."
        "Any support please reach me: msamwelmollel@gmail.com"
    )


def setup_disclaimer():
    st.markdown("## Disclaimer")
    st.markdown(
        "Above information may be wrong. If you want to use a published model for commercial use, please contact a "
        "lawyer."
    )


def setup_footer():
    st.markdown(
        """
        ---
        Made with ❤️ by the awesome open-source community from all over 🌍.
        """
    )


def main():
    setup_basic()

    with open("README.md", "r") as f:
        readme = f.read()
  

    setup_leaderboard(readme)
    # setup_benchmarks(readme)
    # setup_sources()
    # setup_disclaimer()
    # setup_footer()
    setup_Contribution()
    setup_Sponsorship()


if __name__ == "__main__":
    main()