File size: 1,654 Bytes
d52b6cc 9afae16 d52b6cc 9afae16 d52b6cc 9afae16 d52b6cc 9afae16 d52b6cc 9afae16 d52b6cc 9afae16 d52b6cc 9afae16 d52b6cc 9afae16 d52b6cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
import cv2
import numpy as np
from ultralytics import YOLO
app = FastAPI(title="GiziTrace ML API")
model = YOLO("best.onnx", task="detect")
class_names = {0: "plate", 1: "food"}
@app.post("/predict")
async def predict_waste(file: UploadFile = File(...)):
try:
contents = await file.read()
nparr = np.frombuffer(contents, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
results = model(img, conf=0.10)
detections = []
# Proses hasil deteksi
for result in results:
if len(result.boxes) == 0:
continue
clss = result.boxes.cls.cpu().numpy()
confs = result.boxes.conf.cpu().numpy()
for idx in range(len(clss)):
class_id = int(clss[idx])
confidence = float(confs[idx])
# Ambil nama class dari dictionary
label = class_names.get(class_id, "unknown")
# Masukkan ke dalam list hasil
detections.append({
"class": label,
"confidence": round(confidence, 2)
})
return JSONResponse(content={
"status": "success",
"total_objects_detected": len(detections),
"detections": detections
})
except Exception as e:
return JSONResponse(status_code=500, content={"message": str(e)}) |