Files
jardin/ai-service/main.py

69 lines
2.0 KiB
Python

import asyncio
import io
import os
from typing import List
from fastapi import FastAPI, File, HTTPException, UploadFile
from PIL import Image, UnidentifiedImageError
app = FastAPI(title="AI Plant Detection Service")
_model = None
MODEL_CACHE_DIR = os.environ.get("MODEL_CACHE_DIR", "/models")
MAX_FILE_SIZE = 10 * 1024 * 1024 # 10 MB
def get_model():
global _model
if _model is None:
from ultralytics import YOLO
os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
try:
_model = YOLO("foduucom/plant-leaf-detection-and-classification")
except Exception as e:
raise RuntimeError(f"Impossible de charger le modèle YOLO: {e}") from e
return _model
def _run_inference(img: Image.Image) -> list:
model = get_model()
results = model.predict(img, conf=0.25, iou=0.45, verbose=False)
detections = []
if results and results[0].boxes:
boxes = results[0].boxes
names = model.names
for i in range(min(3, len(boxes))):
cls_id = int(boxes.cls[i].item())
conf = float(boxes.conf[i].item())
detections.append({
"class_name": names[cls_id],
"confidence": round(conf, 3),
})
return detections
@app.get("/health")
def health():
return {"status": "ok", "model_loaded": _model is not None}
@app.post("/detect")
async def detect(file: UploadFile = File(...)):
data = await file.read()
if len(data) > MAX_FILE_SIZE:
raise HTTPException(status_code=413, detail="Fichier trop volumineux (max 10 MB)")
try:
img = Image.open(io.BytesIO(data)).convert("RGB")
except (UnidentifiedImageError, OSError) as e:
raise HTTPException(status_code=400, detail=f"Image invalide: {e}")
try:
loop = asyncio.get_event_loop()
detections = await loop.run_in_executor(None, _run_inference, img)
except RuntimeError as e:
raise HTTPException(status_code=503, detail=str(e))
return detections