model-ml/ml-api/app/main.py

from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from starlette.staticfiles import StaticFiles
from pathlib import Path

from .translate_pipeline import process_audio_file, AUDIO_DIR
from .train_translate import train_from_local_dataset


app = FastAPI(title="ML API - Speech-to-Speech")

# CORS (peut être ajusté via env WEB_ORIGIN si nécessaire)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Fichiers statiques pour audio
AUDIO_DIR.mkdir(parents=True, exist_ok=True)
app.mount("/audio", StaticFiles(directory=str(AUDIO_DIR)), name="audio")


@app.get("/health")
def health():
    return {"status": "ok"}


@app.post("/translate")
async def translate(file: UploadFile = File(...)):
    data = await file.read()
    result = process_audio_file(data, file.filename)
    return JSONResponse(result)


@app.post("/train")
def train():
    out_dir = train_from_local_dataset()
    return {"status": "ok", "model_path": out_dir}


@app.post("/predict")
async def predict_compat(file: UploadFile = File(None)):
    """
    Endoint de compatibilité: si on reçoit un fichier audio, traite comme /translate.
    Sinon, renvoie un message d’orientation.
    """
    if file is not None:
        data = await file.read()
        result = process_audio_file(data, file.filename)
        return JSONResponse(result)
    return JSONResponse({"detail": "Utilisez /translate pour la traduction vocale."}, status_code=400)