async def scan(file: UploadFile = File(...)):
content_type = file.content_type
file_name = file.filename
# Get Image
if content_type == 'application/pdf':
# Get Image from 1st page of pdf
pages = convert_from_bytes(file.file.read())
image = pages[0]
file_name = file_name[:-3] + '.png'
elif content_type.startswith('image'):
image = Image.open(file.file)
else:
# Return error if Unknown file
return Response(status_code=422)
# Scan with CUTIE
xlsx_path = predict(image, file_name)
return xlsx_path
def predict_startup():
"""
this function will be invoked by AI SW with CPP
The target of this functoin is used to, including
1. Initialize the Python environment
2. Load trained weight file
3. Completed others, including creating debug files, etc.
"""
#check environment
env.config_env()
pre = predict.predict()
pre.load_model()
scserv = SCServer(config.configured_TCP_server_port, pre)
scserv.start()
def predictImg(path):
result = predict.predict(path)[0]
temp = result[:]
temp = temp.argsort()[-3:][::-1]
return {
'top1': {
'name': dog_breeds[temp[0]],
'percent': "{:.2f}".format(result[temp[0]] * 100),
'detail': description[temp[0]],
},
'top2': {
'name': dog_breeds[temp[1]],
'percent': "{:.2f}".format(result[temp[1]] * 100),
'detail': description[temp[1]],
},
'top3': {
'name': dog_breeds[temp[2]],
'percent': "{:.2f}".format(result[temp[2]] * 100),
'detail': description[temp[2]],
},
}
def heart_predict(request):
val = predict.predict(fin)
return render(request, "prediction/result.html", {'fin': val})