# 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')

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

#data_dir = directories[0]

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")

data_dir = st.selectbox(
    'Select different Date',
    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</a> 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"
}

st.write("Generated on: <b>" + format_dir_date(data_dir) + "</b>", unsafe_allow_html=True)
st.divider()

data_path = './data/' + data_dir

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)]

# Getting the remaining images
remaining_imgs = [img for img in imgs if 'hf_llm_diagram' not in os.path.basename(img)]

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)

st.write("HuggingFace Open LLM leaderboard by Model Size")
st.image(hf_llm_diagrams[0],use_column_width="auto")

print_model_list(hf_llm_diagrams[0],st,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.divider()
st.write("HuggingFace and Other Leaderboards: A Comparative Model Evaluation")
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</a> GPT4 judged evaluation of 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>
        </ul>
    """, unsafe_allow_html=True
)
st.divider()

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

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

st.divider()
st.subheader('About')
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)