app.py

import os 
import json 
import gradio as gr 
import pandas as pd 
import numpy as np 
 
from collections import defaultdict 
 
 
LENGTHS = ["dataset_total_score", "4k", "8k", "16k", "32k", "64k", "128k"] 
datasets_params = json.load(open("datasets_config.json", "r")) 
TASKS = datasets_params.keys() 
 
 
def make_default_md(): 
 leaderboard_md = f""" 
 🏅 LIBRA LeaderBoard 
 | [GitHub](https://github.com/ai-forever/LIBRA) | [Datasets](https://huggingface.co/datasets/ai-forever/LIBRA) | 
 """ 
 return leaderboard_md 
 
 
def make_model_desc_md(): 
 with open("docs/description.md", "r") as f: 
 description = f.read() 
 return description 
 
 
def make_overall_table_by_tasks(files): 
 results = defaultdict(list) 
 
 result_dct = {} 
 for file in files: 
 if not file.endswith("json"): continue 
 path = "results/" + file 
 data = json.load(open(path)) 
 model_name = file.split('/')[-1].split(".json")[0] 
 result_dct[model_name] = {} 
 for dataset in data.keys(): 
 if dataset == "total_score": 
 result_dct[model_name][dataset] = round(data[dataset] * 100, 1) 
 continue 
 result_dct[model_name][dataset] = round(data[dataset]["dataset_total_score"] * 100, 1) 
 
 for file in files: 
 if not file.endswith("json"): continue 
 model_name = file.split('/')[-1].split(".json")[0] 
 results['Model'].append(model_name) 
 for key in result_dct[model_name].keys(): 
 if key == "total_score": 
 results["Total Score"].append(result_dct[model_name][key]) 
 else: 
 results[datasets_params[key]["name"]].append(result_dct[model_name][key]) 
 
 table = pd.DataFrame(results).sort_values(['Total Score'], ascending=False) 
 cols = table.columns.tolist() 
 cols = [cols[0]] + [cols[22]] + cols[1:22] 
 return table[cols] 
 
 
def make_overall_table_by_lengths(files): 
 results = defaultdict(list) 
 
 result_dct = {} 
 for file in files: 
 if not file.endswith("json"): continue 
 path = "results/" + file 
 data = json.load(open(path)) 
 model_name = file.split('/')[-1].split(".json")[0] 
 result_dct[model_name] = {} 
 for dataset in data.keys(): 
 if dataset == "total_score": 
 result_dct[model_name][dataset] = data[dataset] 
 continue 
 for length in data[dataset].keys(): 
 if length == "dataset_total_score": continue 
 if length not in result_dct[model_name]: 
 result_dct[model_name][length] = [] 
 result_dct[model_name][length].append(data[dataset][length]) 
 
 for model_name in result_dct.keys(): 
 for length in result_dct[model_name].keys(): 
 result_dct[model_name][length] = round(np.mean(result_dct[model_name][length]) * 100, 1) 
 
 for file in files: 
 if not file.endswith("json"): continue 
 model_name = file.split('/')[-1].split(".json")[0] 
 results['Model'].append(model_name) 
 for key in result_dct[model_name].keys(): 
 if key == "total_score": 
 results["Total Score"].append(result_dct[model_name][key]) 
 else: 
 results[key].append(result_dct[model_name][key]) 
 
 table = pd.DataFrame(results).sort_values(['Total Score'], ascending=False) 
 cols = table.columns.tolist() 
 cols = [cols[0]] + [cols[7]] + cols[1:7] 
 return table[cols] 
 
 
def load_model(files, tab_name): 
 results = defaultdict(list) 
 
 for file in files: 
 if not file.endswith("json"): continue 
 model_name = file.split('/')[-1].split(".json")[0] 
 results['Model'].append(model_name) 
 result = json.load(open("results/" + file, "r")) 
 for length in LENGTHS: 
 if length in result[tab_name].keys(): 
 if length == "dataset_total_score": 
 results["Dataset Total Score"].append(round(result[tab_name][length] * 100, 1)) 
 continue 
 results[length].append(round(result[tab_name][length] * 100, 1)) 
 else: 
 results[length].append("-") 
 
 return pd.DataFrame(results).sort_values(['Dataset Total Score'], ascending=False) 
 
 
def build_leaderboard_tab(files): 
 default_md = make_default_md() 
 md_1 = gr.Markdown(default_md, elem_id="leaderboard_markdown") 
 
 with gr.Tabs() as tabs: 
 
 with gr.Tab("Results by Lengths", id=0): 
 df = make_overall_table_by_lengths(files) 
 gr.Dataframe( 
 headers=[ 
 "Model", 
 ] + LENGTHS, 
 datatype=[ 
 "markdown", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 ], 
 value=df, 
 elem_id="arena_leaderboard_dataframe", 
 height=700, 
 wrap=True, 
 ) 
 
 with gr.Tab("Results by Tasks", id=1): 
 df = make_overall_table_by_tasks(files) 
 gr.Dataframe( 
 headers=[ 
 "Model", 
 ] + LENGTHS, 
 datatype=[ 
 "markdown", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str" 
 ], 
 value=df, 
 elem_id="arena_leaderboard_dataframe", 
 height=700, 
 wrap=False, 
 ) 
 
 for tab_id, tab_name in enumerate(TASKS): 
 df = load_model(files, tab_name) 
 with gr.Tab(datasets_params[tab_name]["name"], id=tab_id+2): 
 gr.Dataframe( 
 headers=[ 
 "Model", 
 ] + LENGTHS, 
 datatype=[ 
 "markdown", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 "str", 
 ], 
 value=df, 
 elem_id="arena_leaderboard_dataframe", 
 height=700, 
 wrap=True, 
 ) 
 
 with gr.Tab("Description", id=tab_id + 3): 
 desc_md = make_model_desc_md() 
 gr.Markdown(desc_md, elem_id="leaderboard_markdown") 
 
 return [md_1] 
 
 
def build_demo(files): 
 text_size = gr.themes.sizes.text_lg 
 
 with gr.Blocks(title="LIBRA leaderboard", 
 theme=gr.themes.Base(text_size=text_size)) as demo: 
 build_leaderboard_tab(files) 
 return demo 
 
 
if __name__ == "__main__": 
 files = os.listdir("results") 
 demo = build_demo(files) 
 demo.launch(share=False)