# app.py
import json

import streamlit as st
import glob
import os
from datetime import datetime

st.set_page_config(layout="wide")
st.title('Meta Open LLM leaderboard')
st.write("Combine data from various open LLM leaderboards into one useful visualization page")
st.write("<nbsp/>", unsafe_allow_html=True)

directories = os.listdir("./data")

def format_dir_date(data_dir):
    # Extracting date and time information from the path
    parsed_date = datetime.strptime(data_dir, "%Y%m%d_%H%M")

    # Formatting the parsed date
    return  parsed_date.strftime("%b %d, %Y %H:%M")

def print_model_list(file_name, st, split_into_two=False):
    file_path = file_name[:-4] + '.json'
    # Read the list from the JSON file
    with open(file_path, 'r') as file:
        model_id_list_loaded = json.load(file)
        midpoint = len(model_id_list_loaded) // 2 + (len(model_id_list_loaded) % 2)  # Calculate the midpoint

        # Split the list into two parts
        left_list = model_id_list_loaded[:midpoint]
        right_list = model_id_list_loaded[midpoint:]

        # Generate HTML for the left column
        left_html = ""
        for model_id in left_list:
            model_id_trunc = model_id if len(model_id) <= 35 else '...' + model_id[-35:]
            left_html += f'<li><a href="https://huggingface.co/{model_id}">{model_id_trunc}</a></li>'


        # Generate HTML for the right column
        right_html = ""
        for model_id in right_list:
            model_id_trunc = model_id if len(model_id) <= 35 else '...' + model_id[-35:]
            right_html += f'<li><a href="https://huggingface.co/{model_id}">{model_id_trunc}</a></li>'

        final_html = ""
        if(split_into_two):
            final_html  = "<ul>"
            final_html  += left_html
            final_html  += "</ul>"
            cols = st.columns(2)
            cols[0].write(final_html, unsafe_allow_html=True)
            final_html  = "<ul>"
            final_html  += right_html
            final_html  += "</ul>"
            cols[1].write(final_html, unsafe_allow_html=True)
        else:
            final_html  = "<ul>"
            final_html  += left_html
            final_html  += right_html
            final_html  += "</ul>"
            st.write(final_html, unsafe_allow_html=True)

col1, col2 = st.columns(2)

with col1:
    data_dir = st.selectbox(
        'Select different data generation date',
        directories,
        format_func=format_dir_date,
        index=len(directories)-1,
        )
with col2:
    compare_mode = st.checkbox('Enable compare to different date')
    if compare_mode:
        compare_data_dir = st.selectbox(
            'Select date for comparison',
            directories,
            format_func=format_dir_date,
            index=len(directories)-1,
        )

captions_map = {
    "hg_average_to_agentbench_compare.png": "HF to AgentBench compare",
    "hg_average_to_opencompass_compare.png": "HF to OpenCompass compare",
    "hg_average_to_mt_bench_compare.png": "HF to MT-Bench compare",
    "hg_average_to_mosaic_compare.png": "HF to MosaicML compare",
    "hg_average_to_alpacaeval_compare.png": "HF to AlpacaEval compare"
}

with col1:
    st.write("<div style=\"text-align: center\" >Generated on: <b>" + format_dir_date(data_dir) + "</b></div>", unsafe_allow_html=True)


data_path = './data/' + data_dir

# Adjust the data path loading logic
if compare_mode:

    # Side by side compare:
    compare_data_path = './data/' + compare_data_dir

    # Load images from both directories
    imgs = glob.glob(os.path.join(data_path, '*.png'))
    compare_imgs = glob.glob(os.path.join(compare_data_path, '*.png'))

    # Extracting images that start with specific keywords from both sets
    def extract_images(keyword, img_list):
        return [img for img in img_list if keyword in os.path.basename(img)]

    hf_llm_diagrams = extract_images('hf_llm_diagram', imgs)
    bigcode_diagrams = extract_images('bigcode', imgs)
    mt_bench_diagrams = extract_images('lmsys_leaderboard_mt_bench', imgs)
    arena_diagrams = extract_images('lmsys_leaderboard_arena', imgs)
    opencompass_diagrams = extract_images('opencompass_leaderboard', imgs)

    compare_hf_llm_diagrams = extract_images('hf_llm_diagram', compare_imgs)
    compare_bigcode_diagrams = extract_images('bigcode', compare_imgs)
    compare_mt_bench_diagrams = extract_images('lmsys_leaderboard_mt_bench', compare_imgs)
    compare_arena_diagrams = extract_images('lmsys_leaderboard_arena', compare_imgs)
    compare_opencompass_diagrams = extract_images('opencompass_leaderboard', compare_imgs)

    # Display each category side by side
    def display_side_by_side(diagrams1, diagrams2, title):
        st.subheader(title, divider=True)
        for d1, d2 in zip(diagrams1, diagrams2):
            cols = st.columns(2)
            cols[0].image(d1, use_column_width="auto")
            cols[1].image(d2, use_column_width="auto")

    # Displaying HuggingFace LLM Leaderboard
    display_side_by_side(hf_llm_diagrams, compare_hf_llm_diagrams, "HuggingFace Open LLM leaderboard by Model Size")

    # Displaying Big Code Models Leaderboard
    display_side_by_side(bigcode_diagrams, compare_bigcode_diagrams, "Big Code Models Leaderboard")

    # Displaying MT-Bench Models Leaderboard
    display_side_by_side(mt_bench_diagrams, compare_mt_bench_diagrams, "MT-Bench Models Leaderboard")

    # Displaying Arena Models Leaderboard
    display_side_by_side(arena_diagrams, compare_arena_diagrams, "LMSYS Arena Elo Models Leaderboard")

    # Displaying OpenCompass Models Leaderboard
    display_side_by_side(opencompass_diagrams, compare_opencompass_diagrams, "OpenCompass Models Leaderboard")

    # Extracting remaining images from both sets
    remaining_imgs = list(set(imgs) - set(hf_llm_diagrams) - set(bigcode_diagrams) - set(mt_bench_diagrams) - set(opencompass_diagrams))
    compare_remaining_imgs = list(set(compare_imgs) - set(compare_hf_llm_diagrams) - set(compare_bigcode_diagrams) - set(compare_mt_bench_diagrams) - set(compare_opencompass_diagrams))

    st.subheader("HuggingFace and Other Leaderboards: A Comparative Model Evaluation", divider=True)
    st.caption("Only models evaluated on both leaderboards are included.")

    # Display remaining images side by side
    for img, compare_img in zip(remaining_imgs, compare_remaining_imgs):
        cols = st.columns(2)

        # Extract the filename and caption for the first image
        filename = os.path.basename(img)
        caption = captions_map.get(filename, "")

        # Extract the filename and caption for the comparison image
        compare_filename = os.path.basename(compare_img)
        compare_caption = captions_map.get(compare_filename, "")

        # Display the images with captions
        cols[0].image(img, caption=caption, width=None)
        cols[1].image(compare_img, caption=compare_caption, width=None)

else:
    imgs = glob.glob(os.path.join(data_path, '*.png'))

    # Extracting images that start with "hf_llm_diagram"
    hf_llm_diagrams = [img for img in imgs if 'hf_llm_diagram' in os.path.basename(img)]
    bigcode_diagrams = [img for img in imgs if 'bigcode' in os.path.basename(img)]
    mt_bench_diagrams = [img for img in imgs if 'lmsys_leaderboard_mt_bench' in os.path.basename(img)]
    arena_diagrams = [img for img in imgs if 'lmsys_leaderboard_arena' in os.path.basename(img)]
    opencompass_diagrams = [img for img in imgs if 'opencompass_leaderboard' in os.path.basename(img)]

    # Getting the remaining images
    remaining_imgs = list(set(imgs) - set(hf_llm_diagrams) - set(bigcode_diagrams) - set(mt_bench_diagrams) - set(arena_diagrams) - set(opencompass_diagrams))

    st.subheader("HuggingFace Open LLM leaderboard by Model Size", divider=True)
    cols = st.columns(2)

    cols[0].image(hf_llm_diagrams[0], caption="Main chart using all the models", use_column_width="auto")

    print_model_list(hf_llm_diagrams[0],st, True)
    st.write("<nbsp/>", unsafe_allow_html=True)

    cols = st.columns(2)

    cols[0].image(hf_llm_diagrams[1],caption="Other or commercially permissive licenses only", use_column_width="auto")
    print_model_list(hf_llm_diagrams[1],cols[0])

    cols[1].image(hf_llm_diagrams[2],caption="Commercially permissive license only", use_column_width="auto")
    print_model_list(hf_llm_diagrams[2],cols[1])

    st.write("<nbsp/>", unsafe_allow_html=True)

    cols = st.columns(2)
    cols[0].image(hf_llm_diagrams[3],caption="TruthfulQA at 10% for HuggingFace Open LLM leaderboard by Model Size", use_column_width="auto")
    print_model_list(hf_llm_diagrams[3],cols[0],False)

    cols[1].image(hf_llm_diagrams[4],caption="ARC at 50% and MMLU at 50% for HuggingFace Open LLM leaderboard by Model Size", use_column_width="auto")
    print_model_list(hf_llm_diagrams[4],cols[1],False)


    st.subheader("Big Code Models Leaderboard", divider=True)
    cols = st.columns(2)
    cols[0].image(bigcode_diagrams[0], use_column_width="auto")


    print_model_list(bigcode_diagrams[0],st,True)

    st.subheader("MT-Bench Models Leaderboard", divider=True)
    cols = st.columns(2)
    cols[0].image(mt_bench_diagrams[0], use_column_width="auto")

    print_model_list(mt_bench_diagrams[0],st,True)

    st.subheader("LMSYS Arena Elo Models Leaderboard", divider=True)
    cols = st.columns(2)
    cols[0].image(arena_diagrams[0], use_column_width="auto")

    print_model_list(arena_diagrams[0],st,True)

    st.subheader("OpenCompass Models Leaderboard", divider=True)
    cols = st.columns(2)
    cols[0].image(opencompass_diagrams[0], use_column_width="auto")
    print_model_list(opencompass_diagrams[0],st,True)

    st.subheader("HuggingFace and Other Leaderboards: A Comparative Model Evaluation", divider=True)
    st.caption("Only models evaluated on both leaderboards are included.")

    cols = st.columns(2)

    for i, img in enumerate(remaining_imgs):
        # Extract the filename from the full image path
        filename = os.path.basename(img)

        # Get the caption from the captions_map dictionary
        caption = captions_map.get(filename, "")  # If no caption is found, it will default to an empty string

        # Display the image with the caption
        cols[i % 2].image(img, caption=caption, width=None)

st.write(
    """
    <p>Leaderboards tracked:</p>
     <ul>
        <li><a href="https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard">Hugging Face Open LLM</a></li>
        <li><a href="https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard">MT-Bench and Arena Elo</a>MT-Bench is GPT4 judged evaluation of models, Arena Elo is users ranking outputs between models.</li>
        <li><a href="https://tatsu-lab.github.io/alpaca_eval/">AlpacaEval</a> GPT4 judged evaluation of models</li>
        <li><a href="https://www.mosaicml.com/llm-evaluation">MosaicML</a> Balanced set of static benchmarks</li>
        <li><a href="https://opencompass.org.cn/leaderboard-llm">OpenCompass</a> Balanced set of static benchmarks</li>
        <li><a href="https://llmbench.ai/data">AgentBench</a> Benchmark evaluating Agent abilities</li>
        <li><a href="https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard">BigCode</a>  Compare performance of base multilingual code generation models</li>
        </ul>
        <sub>HuggingFace models that have been flagged as contaminated or do not provide any model card information are excluded.</sub>
    """, unsafe_allow_html=True
)


st.subheader('About', divider=True)
st.write('This meta leaderboard is built and maintained by Felix Zaslavskiy. For feedback, correction, suggestions please reach out on X at <a href="https://twitter.com/FZaslavskiy" >@FZaslavskiy</a> or here via community discussions.', unsafe_allow_html=True)