app.py

"""A gradio app that renders a static leaderboard. This is used for Hugging Face Space."""
import ast
import argparse
import glob
import pickle

import gradio as gr
import numpy as np
import pandas as pd
import gradio as gr
import pandas as pd
from pathlib import Path
import json
from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS
from init import is_model_on_hub, upload_file, load_all_info_from_dataset_hub
from utils_display import AutoEvalColumn, fields, make_clickable_model, styled_error, styled_message
from datetime import datetime, timezone

LAST_UPDATED = "Feb 27th 2024"

css = """
.markdown-text{font-size: 16pt}
th {
  text-align: center;
}
td {
  font-size: 16px; /* Adjust the font size as needed */
  text-align: center;
}

"""

column_names = {
    "model": "Model",
    "Overall": "All 🎯",    
    "Turn 1": "Turn 1️⃣", 
    "Turn 2": "Turn 2️⃣", 
    }

model_info = {
    "gpt-4": {"hf_name": "https://platform.openai.com/", "pretty_name": "gpt-4"}, 
    "gpt-3.5-turbo": {"hf_name": "https://platform.openai.com/", "pretty_name": "gpt-3.5-turbo"}, 
    "Llama-2-70b-hf": {"hf_name": "meta-llama/Llama-2-70b-hf", "pretty_name": "Llama-2-70B"}, 
    "Llama-2-13b-hf": {"hf_name": "meta-llama/Llama-2-13b-hf", "pretty_name": "Llama-2-13B"}, 
    "Llama-2-7b-hf": {"hf_name": "meta-llama/Llama-2-7b-hf", "pretty_name": "Llama-2-7B"}, 
    "Mixtral-8x7B-v0.1": {"hf_name": "mistralai/Mixtral-8x7B-v0.1", "pretty_name": "Mixtral-8x7B"}, 
    "Mistral-7b-v0.1": {"hf_name": "mistralai/Mistral-7B-v0.1", "pretty_name": "Mistral-7B"}, 
    "Yi-34B": {"hf_name": "01-ai/Yi-34B", "pretty_name": "Yi-34B"}, 
    "Yi-6B": {"hf_name": "01-ai/Yi-6B", "pretty_name": "Yi-6B"}, 
    "gemma-7b": {"hf_name": "google/gemma-7b", "pretty_name": "Gemma-7B"}, 
    "gemma-2b": {"hf_name": "google/gemma-2b", "pretty_name": "Gemma-2B"}, 
    "phi-2": {"hf_name": "microsoft/phi-2", "pretty_name": "Phi-2 (2.7B)"}, 
    "olmo": {"hf_name": "allenai/OLMo-7B", "pretty_name": "OLMo-7B"},  
} 


# Formats the columns
def formatter(x):
    if type(x) is str:
        x = x
    else: 
        x = round(x, 2)
    return x

 

def build_demo(original_df, TYPES):
    with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
        # gr.HTML(BANNER, elem_id="banner")
        gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

        # with gr.Tabs(elem_classes="tab-buttons") as tabs:
        #     with gr.TabItem("🏅 Leaderboard", elem_id="od-benchmark-tab-table", id=0):
        leaderboard_table = gr.components.Dataframe(
            value=original_df,
            datatype=TYPES,
            height=1000,
            wrap=False,
            elem_id="leaderboard-table",
            interactive=False,
            visible=True,
            min_width=60,
            )

            # with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1):
            #     gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text")
 

        gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text")
        
        with gr.Row():
            with gr.Accordion("📙 Citation", open=False):
                gr.Textbox(
                    value=CITATION_TEXT, lines=7,
                    label="Copy the BibTeX to cite URIAL and MT-Bench",
                    elem_id="citation-button",
                    show_copy_button=True)
                # ).style(show_copy_button=True)

    return demo

if __name__ == "__main__": 

    parser = argparse.ArgumentParser()
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--result_file", help="Path to results table", default="leaderboard_data.jsonl")
    args = parser.parse_args()

    bench_results = args.result_file
 
    original_df = pd.read_json(bench_results, lines=True)

    print(original_df.columns)

    for col in original_df.columns:
        if col == "model":
            original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x, model_info)))
        else:
            original_df[col] = original_df[col].apply(formatter) # For numerical values
    
    # Define the first column explicitly, add 'Overall' as the second column, and then append the rest excluding 'Overall'
    new_order = [original_df.columns[0], 'Overall'] + [col for col in original_df.columns if col not in [original_df.columns[0], 'Overall']]

    # Reorder the DataFrame columns using the new order
    reordered_df = original_df[new_order]

    reordered_df.sort_values(by='Overall', inplace=True, ascending=False)

    reordered_df.rename(columns=column_names, inplace=True)

    # COLS = [c.name for c in fields(AutoEvalColumn)]
    # TYPES = [c.type for c in fields(AutoEvalColumn)]

    TYPES = ["markdown", "number"]
    demo = build_demo(reordered_df, TYPES)
    demo.launch(share=args.share)