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""" | |
It provides a leaderboard component. | |
""" | |
from collections import defaultdict | |
import enum | |
import math | |
import firebase_admin | |
from firebase_admin import credentials | |
from firebase_admin import firestore | |
import gradio as gr | |
import pandas as pd | |
from credentials import get_credentials_json | |
# TODO(#21): Fix auto-reload issue related to the initialization of Firebase. | |
firebase_admin.initialize_app(credentials.Certificate(get_credentials_json())) | |
db = firestore.client() | |
class LeaderboardTab(enum.Enum): | |
SUMMARIZATION = "Summarization" | |
TRANSLATION = "Translation" | |
# Ref: https://colab.research.google.com/drive/1RAWb22-PFNI-X1gPVzc927SGUdfr6nsR?usp=sharing#scrollTo=QLGc6DwxyvQc pylint: disable=line-too-long | |
def compute_elo(battles, k=4, scale=400, base=10, initial_rating=1000): | |
rating = defaultdict(lambda: initial_rating) | |
for model_a, model_b, winner in battles[["model_a", "model_b", | |
"winner"]].itertuples(index=False): | |
rating_a = rating[model_a] | |
rating_b = rating[model_b] | |
expected_score_a = 1 / (1 + base**((rating_b - rating_a) / scale)) | |
expected_score_b = 1 / (1 + base**((rating_a - rating_b) / scale)) | |
scored_point_a = 0.5 if winner == "tie" else int(winner == "model_a") | |
rating[model_a] += k * (scored_point_a - expected_score_a) | |
rating[model_b] += k * (1 - scored_point_a - expected_score_b) | |
return rating | |
def get_docs(tab): | |
if tab == LeaderboardTab.SUMMARIZATION: | |
return db.collection("arena-summarizations").order_by("timestamp").stream() | |
if tab == LeaderboardTab.TRANSLATION: | |
return db.collection("arena-translations").order_by("timestamp").stream() | |
def load_elo_ratings(tab): | |
docs = get_docs(tab) | |
battles = [] | |
for doc in docs: | |
data = doc.to_dict() | |
battles.append({ | |
"model_a": data["model_a"], | |
"model_b": data["model_b"], | |
"winner": data["winner"] | |
}) | |
battles = pd.DataFrame(battles) | |
ratings = compute_elo(battles) | |
sorted_ratings = sorted(ratings.items(), key=lambda x: x[1], reverse=True) | |
return [[i + 1, model, math.floor(rating + 0.5)] | |
for i, (model, rating) in enumerate(sorted_ratings)] | |
def load_summarization_elo_ratings(): | |
return load_elo_ratings(LeaderboardTab.SUMMARIZATION) | |
def load_translation_elo_ratings(): | |
return load_elo_ratings(LeaderboardTab.TRANSLATION) | |
LEADERBOARD_UPDATE_INTERVAL = 600 # 10 minutes | |
LEADERBOARD_INFO = "The leaderboard is updated every 10 minutes." | |
def build_leaderboard(): | |
with gr.Tabs(): | |
with gr.Tab(LeaderboardTab.SUMMARIZATION.value): | |
gr.Dataframe(headers=["Rank", "Model", "Elo rating"], | |
datatype=["number", "str", "number"], | |
value=load_summarization_elo_ratings, | |
every=LEADERBOARD_UPDATE_INTERVAL) | |
gr.Markdown(LEADERBOARD_INFO) | |
# TODO(#9): Add language filter options. | |
with gr.Tab(LeaderboardTab.TRANSLATION.value): | |
gr.Dataframe(headers=["Rank", "Model", "Elo rating"], | |
datatype=["number", "str", "number"], | |
value=load_translation_elo_ratings, | |
every=LEADERBOARD_UPDATE_INTERVAL) | |
gr.Markdown(LEADERBOARD_INFO) | |