app.py

from fastapi import FastAPI, UploadFile, File, Form
from faster_whisper import WhisperModel
import uvicorn
import tempfile
import shutil
import torch
import os
from fastapi.middleware.cors import CORSMiddleware
device = "cuda" if torch.cuda.is_available() else "cpu"
compute_type = "float16" if device == "cuda" else "int8"
origins = [
    "*",  # ton WordPress
]
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)



# === Dictionnaire des modèles dispo ===
AVAILABLE_MODELS = ["tiny", "base", "small", "medium", "large-v2"]

def load_model(model_name: str):
    """Charger un modèle Whisper avec CPU (modifiable si GPU dispo)"""
    return WhisperModel(model_name, device=device, compute_type="int8")


# === Endpoint API REST ===
@app.post("/transcribe")
async def transcribe(
    file: UploadFile = File(...),
    model_name: str = Form("base")  # par défaut "base"
):
    if model_name not in AVAILABLE_MODELS:
        return {"error": f"Modèle non reconnu. Choisissez parmi {AVAILABLE_MODELS}"}

    model = load_model(model_name)

    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
        shutil.copyfileobj(file.file, tmp)
        tmp_path = tmp.name

    segments, info = model.transcribe(tmp_path, beam_size=5)
    text_result = " ".join([segment.text for segment in segments])

    os.remove(tmp_path)

    return {
        "model_used": model_name,
        "language": info.language,
        "probability": info.language_probability,
        "transcription": text_result,
    }

if __name__ == "__main__":
    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=False)