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Runtime error
File size: 2,038 Bytes
635d8d1 | 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 55 56 57 | from fastapi import APIRouter, UploadFile, File, Depends
from PIL import Image
from prediction.domain.domain import SegmentationResult, ClassificationResult, DetectionResult, FullPredictionResult
from auth.service.auth_service import auth
from io import BytesIO
from prediction.service.PetPredictionService import PetPredictionService
from fastapi.security import OAuth2PasswordBearer
from sqlalchemy.orm import Session
from database import get_db
oauth2_scheme = OAuth2PasswordBearer(tokenUrl="login")
router = APIRouter()
@router.post("/predict/full", response_model=FullPredictionResult)
async def predict_full(
file: UploadFile = File(...),
token: str = Depends(oauth2_scheme),
db: Session = Depends(get_db)
):
user = auth(token, db)
contents = await file.read()
image = Image.open(BytesIO(contents)).convert("RGB")
return PetPredictionService().full_pet_prediction(image)
@router.post("/classification", response_model=ClassificationResult)
async def predict_classification(
file: UploadFile = File(...),
token: str = Depends(oauth2_scheme),
db: Session = Depends(get_db)
):
user = auth(token, db)
contents = await file.read()
image = Image.open(BytesIO(contents)).convert("RGB")
return PetPredictionService().classify(image)
@router.post("/detection", response_model=list[DetectionResult])
async def predict_detection(
file: UploadFile = File(...),
token: str = Depends(oauth2_scheme),
db: Session = Depends(get_db)
):
user = auth(token, db)
contents = await file.read()
image = Image.open(BytesIO(contents)).convert("RGB")
return PetPredictionService().detect(image)
@router.post("/segmentation", response_model=SegmentationResult)
async def predict_segmentation(
file: UploadFile = File(...),
token: str = Depends(oauth2_scheme),
db: Session = Depends(get_db)
):
user = auth(token, db)
contents = await file.read()
image = Image.open(BytesIO(contents)).convert("RGB")
return PetPredictionService().segment(image) |