def from_http_request(self, req: HTTPRequest) -> InferenceTask[BinaryIO]:
if req.parsed_headers.content_type == 'multipart/form-data':
_, _, files = HTTPRequest.parse_form_data(req)
if len(files) != 1:
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=
f"BentoML#{self.__class__.__name__} requires one and at"
" least one file at a time, if you just upgraded from"
" bentoml 0.7, you may need to use MultiFileAdapter instead",
)
else:
input_file = next(iter(files.values()))
task = InferenceTask(
context=InferenceContext(http_headers=req.parsed_headers),
data=input_file,
)
elif req.body:
task = InferenceTask(
context=InferenceContext(http_headers=req.parsed_headers),
data=io.BytesIO(req.body),
)
else:
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=
f'BentoML#{self.__class__.__name__} unexpected HTTP request'
' format',
)
return task
def from_aws_lambda_event(self, event):
if event["headers"].get("Content-Type", "").startswith("images/"):
img_bytes = base64.b64decode(event["body"])
_, ext = event["headers"]["Content-Type"].split('/')
f = FileLike(bytes_=img_bytes, name=f"default.{ext}")
task = InferenceTask(data=(f, ))
else:
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg="BentoML currently doesn't support Content-Type: "
"{content_type} for AWS Lambda".format(
content_type=event["headers"]["Content-Type"]),
)
return task
def from_http_request(self, req: HTTPRequest) -> MultiImgTask:
if len(self.input_names) == 1:
# broad parsing while single input
if req.parsed_headers.content_type == 'multipart/form-data':
_, _, files = HTTPRequest.parse_form_data(req)
if not any(files):
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=
f"BentoML#{self.__class__.__name__} requires inputs"
f"fields {self.input_names}",
)
else:
f = next(iter(files.values()))
task = InferenceTask(
context=InferenceContext(
http_headers=req.parsed_headers),
data=(f, ),
)
else:
# for images/*
task = InferenceTask(
context=InferenceContext(http_headers=req.parsed_headers),
data=(io.BytesIO(req.body), ),
)
elif req.parsed_headers.content_type == 'multipart/form-data':
_, _, files = HTTPRequest.parse_form_data(req)
files = tuple(files.get(k) for k in self.input_names)
if not any(files):
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=f"BentoML#{self.__class__.__name__} requires inputs "
f"fields {self.input_names}",
)
elif not all(files) and not self.allow_none:
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=f"BentoML#{self.__class__.__name__} requires inputs "
f"fields {self.input_names}",
)
else:
task = InferenceTask(
context=InferenceContext(http_headers=req.parsed_headers),
data=files,
)
else:
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=
f"BentoML#{self.__class__.__name__} with multiple inputs "
"accepts requests with Content-Type: multipart/form-data only",
)
return task
def from_http_request(self, req: HTTPRequest) -> MultiFileTask:
if req.headers.content_type != 'multipart/form-data':
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=
f"BentoML#{self.__class__.__name__} only accepts requests "
"with Content-Type: multipart/form-data",
)
else:
_, _, files = HTTPRequest.parse_form_data(req)
files = tuple(files.get(k) for k in self.input_names)
if not any(files):
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=f"BentoML#{self.__class__.__name__} requires inputs "
f"fields {self.input_names}",
)
elif not all(files) and not self.allow_none:
task = InferenceTask(data=None)
task.discard(
http_status=400,
err_msg=f"BentoML#{self.__class__.__name__} requires inputs "
f"fields {self.input_names}",
)
else:
task = InferenceTask(
http_headers=req.headers,
data=files,
)
return task
def from_http_request(self, req: HTTPRequest) -> InferenceTask[bytes]:
if req.parsed_headers.content_encoding in {"gzip", "x-gzip"}:
# https://tools.ietf.org/html/rfc7230#section-4.2.3
try:
return InferenceTask(
http_headers=req.parsed_headers,
data=gzip.decompress(req.body),
)
except OSError:
task = InferenceTask(data=None)
task.discard(http_status=400,
err_msg="Gzip decompression error")
return task
elif req.parsed_headers.content_encoding in ["", "identity"]:
return InferenceTask(
http_headers=req.parsed_headers,
data=req.body,
)
else:
task = InferenceTask(data=None)
task.discard(http_status=415, err_msg="Unsupported Media Type")
return task
def predict_strict_json(self, input_data, task: InferenceTask = None):
if task.http_headers.content_type != "application/json":
task.discard(http_status=400, err_msg="application/json only")
return
result = self.artifacts.model.predict_json([input_data])[0]
return result