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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)))
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