import streamlit as st
import pandas as pd
from huggingface_hub import HfApi, ModelCard
from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
import re
from io import StringIO
from yall import create_yall
import plotly.graph_objs as go

def calculate_pages(df, items_per_page):
    """Calculate the number of pages needed for pagination."""
    return -(-len(df) // items_per_page)  # Equivalent to math.ceil(len(df) / items_per_page)

@st.cache_data
def cached_model_info(_api, model):
    """Fetch model information from the Hugging Face API and cache the result."""
    try:
        return _api.model_info(repo_id=str(model))
    except (RepositoryNotFoundError, RevisionNotFoundError):
        return None

@st.cache_data
def get_model_info(df):
    """Get model information and update the DataFrame with likes and tags."""
    api = HfApi()
    with st.spinner("Fetching model information..."):
        for index, row in df.iterrows():
            model_info = cached_model_info(api, row['Model'].strip())
            if model_info:
                df.loc[index, 'Likes'] = model_info.likes
                df.loc[index, 'Tags'] = ', '.join(model_info.tags)
            else:
                df.loc[index, 'Likes'] = -1
                df.loc[index, 'Tags'] = ''
    return df

def convert_markdown_table_to_dataframe(md_content):
    """Convert a markdown table to a pandas DataFrame."""
    cleaned_content = re.sub(r'\|\s*$', '', re.sub(r'^\|\s*', '', md_content, flags=re.MULTILINE), flags=re.MULTILINE)
    df = pd.read_csv(StringIO(cleaned_content), sep="\|", engine='python')
    df = df.drop(0, axis=0)
    df.columns = df.columns.str.strip()
    model_link_pattern = r'\[(.*?)\]\((.*?)\)\s*\[.*?\]\(.*?\)'
    df['URL'] = df['Model'].apply(lambda x: re.search(model_link_pattern, x).group(2) if re.search(model_link_pattern, x) else None)
    df['Model'] = df['Model'].apply(lambda x: re.sub(model_link_pattern, r'\1', x))
    return df

def create_bar_chart(df, category):
    """Create a horizontal bar chart for the specified category."""
    st.write(f"### {category} Scores")
    sorted_df = df[['Model', category]].sort_values(by=category, ascending=True)
    fig = go.Figure(go.Bar(
        x=sorted_df[category],
        y=sorted_df['Model'],
        orientation='h',
        marker=dict(color=sorted_df[category], colorscale='Viridis'),
        hoverinfo='x+y',
        text=sorted_df[category],
        textposition='auto'
    ))
    fig.update_layout(
        margin=dict(l=20, r=20, t=20, b=20),
        title=f"Leaderboard for {category} Scores"
    )
    st.plotly_chart(fig, use_container_width=True, height=len(df) * 35)

def fetch_merge_configs(df):
    """Fetch and save merge configurations for the top models."""
    df_sorted = df.sort_values(by='Average', ascending=False)
    try:
        with open('/tmp/configurations.txt', 'a') as file:
            for index, row in df_sorted.head(20).iterrows():
                model_name = row['Model'].rstrip()
                try:
                    card = ModelCard.load(model_name)
                    file.write(f'Model Name: {model_name}\n')
                    file.write(f'Scores: {row["Average"]}\n')
                    file.write(f'AGIEval: {row["AGIEval"]}\n')
                    file.write(f'GPT4All: {row["GPT4All"]}\n')
                    file.write(f'TruthfulQA: {row["TruthfulQA"]}\n')
                    file.write(f'Bigbench: {row["Bigbench"]}\n')
                    file.write(f'Model Card: {card}\n')
                except Exception as e:
                    st.error(f"Error loading model card for {model_name}: {str(e)}")
        with open('/tmp/configurations.txt', 'r') as file:
            content = file.read()
            matches = re.findall(r'yaml(.*?)```', content, re.DOTALL)
        with open('/tmp/configurations2.txt', 'w') as file:
            for row, match in zip(df_sorted[['Model', 'Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']].head(20).values, matches):
                file.write(f'Model Name: {row[0]}\n')
                file.write(f'Scores: {row[1]}\n')
                file.write(f'AGIEval: {row[2]}\n')
                file.write(f'GPT4All: {row[3]}\n')
                file.write(f'TruthfulQA: {row[4]}\n')
                file.write(f'Bigbench: {row[5]}\n')
                file.write('yaml' + match + '```\n')
    except Exception as e:
        st.error(f"Error while fetching merge configs: {str(e)}")

def main():
    """Main function to set up the Streamlit app and display the leaderboard."""
    st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
    st.title("🏆 YALL - Yet Another LLM Leaderboard")
    st.markdown("Leaderboard made with 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co./NousResearch) benchmark suite.")
    content = create_yall()
    tab1, tab2 = st.tabs(["🏆 Leaderboard", "📝 About"])

    with tab1:
        if content:
            try:
                score_columns = ['Average', 'AGIEval', 'GPT4All', 'TruthfulQA', 'Bigbench']
                full_df = convert_markdown_table_to_dataframe(content)

                for col in score_columns:
                    full_df[col] = pd.to_numeric(full_df[col].str.strip(), errors='coerce')

                full_df = get_model_info(full_df)
                full_df['Tags'] = full_df['Tags'].fillna('')
                df = pd.DataFrame(columns=full_df.columns)

                show_phi = st.checkbox("Phi (2.8B)", value=True)
                show_mistral = st.checkbox("Mistral (7B)", value=True)
                show_other = st.checkbox("Other", value=True)

                dfs_to_concat = []
                if show_phi:
                    dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('phi,|phi-msft,')])
                if show_mistral:
                    dfs_to_concat.append(full_df[full_df['Tags'].str.lower().str.contains('mistral,')])
                if show_other:
                    other_df = full_df[~full_df['Tags'].str.lower().str.contains('phi,|phi-msft,|mistral,')]
                    dfs_to_concat.append(other_df)

                if dfs_to_concat:
                    df = pd.concat(dfs_to_concat, ignore_index=True)

                search_query = st.text_input("Search models", "")
                if search_query:
                    df = df[df['Model'].str.contains(search_query, case=False)]

                items_per_page = 50
                pages = calculate_pages(df, items_per_page)
                page = st.selectbox("Page", list(range(1, pages + 1)))

                df = df.sort_values(by='Average', ascending=False)
                start = (page - 1) * items_per_page
                end = start + items_per_page
                df = df[start:end]

                selected_benchmarks = st.multiselect('Select benchmarks to include in the average', score_columns, default=score_columns)

                if selected_benchmarks:
                    df['Filtered Average'] = df[selected_benchmarks].mean(axis=1)
                    df = df.sort_values(by='Filtered Average', ascending=False)
                    st.dataframe(
                        df[['Model'] + selected_benchmarks + ['Filtered Average', 'Likes', 'URL']],
                        use_container_width=True,
                        column_config={
                            "Likes": st.column_config.NumberColumn(
                                "Likes",
                                help="Number of likes on Hugging Face",
                                format="%d ❤️",
                            ),
                            "URL": st.column_config.LinkColumn("URL"),
                        },
                        hide_index=True,
                        height=len(df) * 37,
                    )

                selected_models = st.multiselect('Select models to compare', df['Model'].unique())
                comparison_df = df[df['Model'].isin(selected_models)]
                st.dataframe(comparison_df)

                if st.button("Export to CSV"):
                    csv_data = df.to_csv(index=False)
                    st.download_button(
                        label="Download CSV",
                        data=csv_data,
                        file_name="leaderboard.csv",
                        key="download-csv",
                        help="Click to download the CSV file",
                    )
                if st.button("Fetch Merge-Configs"):
                    fetch_merge_configs(full_df)
                    st.success("Merge configurations have been fetched and saved.")

                create_bar_chart(df, 'Filtered Average')

                col1, col2 = st.columns(2)
                with col1:
                    create_bar_chart(df, score_columns[1])
                with col2:
                    create_bar_chart(df, score_columns[2])

                col3, col4 = st.columns(2)
                with col3:
                    create_bar_chart(df, score_columns[3])
                with col4:
                    create_bar_chart(df, score_columns[4])

            except Exception as e:
                st.error("An error occurred while processing the markdown table.")
                st.error(str(e))
        else:
            st.error("Failed to download the content from the URL provided.")
            
    with tab2:
        st.markdown('''
            ### Nous benchmark suite
            Popularized by [Teknium](https://huggingface.co./teknium) and [NousResearch](https://huggingface.co./NousResearch), this benchmark suite aggregates four benchmarks:
            * [**AGIEval**](https://arxiv.org/abs/2304.06364) (0-shot): `agieval_aqua_rat,agieval_logiqa_en,agieval_lsat_ar,agieval_lsat_lr,agieval_lsat_rc,agieval_sat_en,agieval_sat_en_without_passage,agieval_sat_math`
            * **GPT4ALL** (0-shot): `hellaswag,openbookqa,winogrande,arc_easy,arc_challenge,boolq,piqa`
            * [**TruthfulQA**](https://arxiv.org/abs/2109.07958) (0-shot): `truthfulqa_mc`
            * [**Bigbench**](https://arxiv.org/abs/2206.04615) (0-shot): `bigbench_causal_judgement,bigbench_date_understanding,bigbench_disambiguation_qa,bigbench_geometric_shapes,bigbench_logical_deduction_five_objects,bigbench_logical_deduction_seven_objects,bigbench_logical_deduction_three_objects,bigbench_movie_recommendation,bigbench_navigate,bigbench_reasoning_about_colored_objects,bigbench_ruin_names,bigbench_salient_translation_error_detection,bigbench_snarks,bigbench_sports_understanding,bigbench_temporal_sequences,bigbench_tracking_shuffled_objects_five_objects,bigbench_tracking_shuffled_objects_seven_objects,bigbench_tracking_shuffled_objects_three_objects`
            ### Reproducibility
            You can easily reproduce these results using 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval/tree/master), a colab notebook that automates the evaluation process (benchmark: `nous`). This will upload the results to GitHub as gists. You can find the entire table with the links to the detailed results [here](https://gist.github.com/mlabonne/90294929a2dbcb8877f9696f28105fdf).
            ### Clone this space
            You can create your own leaderboard with your LLM AutoEval results on GitHub Gist. You just need to clone this space and specify two variables:
            * Change the `gist_id` in [yall.py](https://huggingface.co./spaces/mlabonne/Yet_Another_LLM_Leaderboard/blob/main/yall.py#L126).
            * Create "New Secret" in Settings > Variables and secrets (name: "github", value: [your GitHub token](https://github.com/settings/tokens))
            A special thanks to [gblazex](https://huggingface.co./gblazex) for providing many evaluations.
        ''')

if __name__ == "__main__":
    main()