evals/backend.py

import json
import os

import numpy as np
import pandas as pd
import uvicorn
from countries import make_country_table
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.middleware.gzip import GZipMiddleware
from fastapi.responses import JSONResponse
from fastapi.staticfiles import StaticFiles

with open("results.json", "r") as f:
    results = json.load(f)
scores = pd.DataFrame(results["scores"])
languages = pd.DataFrame(results["languages"])
models = pd.DataFrame(results["models"])


def mean(lst):
    return sum(lst) / len(lst) if lst else None


task_metrics = ["translation_from_bleu", "translation_to_bleu", "classification_accuracy", "mmlu_accuracy"]


def make_model_table(df, models):
    df = (
        df.groupby(["model", "task", "metric"])
        .agg({"score": "mean", "bcp_47": "nunique"})
        .reset_index()
    )
    df["task_metric"] = df["task"] + "_" + df["metric"]
    df = df.drop(columns=["task", "metric"])
    df = df.pivot(index="model", columns="task_metric", values="score").fillna(0)
    df["average"] = df[task_metrics].mean(axis=1)
    df = df.sort_values(by="average", ascending=False).reset_index()
    df = pd.merge(df, models, left_on="model", right_on="id", how="left")
    df["rank"] = df.index + 1
    df = df[
        [
            "rank",
            "model",
            "name",
            "provider_name",
            "hf_id",
            "creation_date",
            "size",
            "type",
            "license",
            "cost",
            "average",
            *task_metrics,
        ]
    ]
    return df


def make_language_table(df, languages):
    df = (
        df.groupby(["bcp_47", "task", "metric"])
        .agg({"score": "mean", "model": "nunique"})
        .reset_index()
    )
    df["task_metric"] = df["task"] + "_" + df["metric"]
    df = df.drop(columns=["task", "metric"])
    df = (
        df.pivot(index="bcp_47", columns="task_metric", values="score")
        .fillna(0)
        .reset_index()
    )
    df["average"] = df[task_metrics].mean(axis=1)
    df = pd.merge(languages, df, on="bcp_47", how="outer")
    df = df.sort_values(by="speakers", ascending=False)
    df = df[
        [
            "bcp_47",
            "language_name",
            "autonym",
            "speakers",
            "family",
            "average",
            "in_benchmark",
            *task_metrics,
        ]
    ]
    return df


app = FastAPI()

app.add_middleware(CORSMiddleware, allow_origins=["*"])
app.add_middleware(GZipMiddleware, minimum_size=1000)


def serialize(df):
    return df.replace({np.nan: None}).to_dict(orient="records")


@app.post("/api/data")
async def data(request: Request):
    body = await request.body()
    data = json.loads(body)
    selected_languages = data.get("selectedLanguages", {})
    df = scores.groupby(["model", "bcp_47", "task", "metric"]).mean().reset_index()
    # lang_results = pd.merge(languages, lang_results, on="bcp_47", how="outer")
    language_table = make_language_table(df, languages)
    datasets_df = pd.read_json("datasets.json")
    if selected_languages:
        # the filtering is only applied for the model table and the country data
        df = df[df["bcp_47"].isin(lang["bcp_47"] for lang in selected_languages)]
    if len(df) == 0:
        model_table = pd.DataFrame()
        countries = pd.DataFrame()
    else:
        model_table = make_model_table(df, models)
        countries = make_country_table(make_language_table(df, languages))
    all_tables = {
        "model_table": serialize(model_table),
        "language_table": serialize(language_table),
        "dataset_table": serialize(datasets_df),
        "countries": serialize(countries),
    }
    return JSONResponse(content=all_tables)


app.mount("/", StaticFiles(directory="frontend/build", html=True), name="frontend")

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 8000)))