def handle_aws_lambda_event(self, event, func):
orient = event['headers'].get('orient', self.orient)
output_orient = event['headers'].get('output_orient',
self.output_orient)
if event['headers']['Content-Type'] == 'application/json':
df = pd.read_json(event['body'],
orient=orient,
typ=self.typ,
dtype=False)
elif event['headers']['Content-Type'] == 'text/csv':
df = pd.read_csv(event['body'])
else:
return {
"statusCode":
400,
"body":
"Request content-type not supported, only application/json and text/csv"
" are supported"
}
if self.typ == 'frame' and self.input_columns is not None:
check_dataframe_column_contains(self.input_columns, df)
result = func(df)
result = get_output_str(result, event['headers'].get('output', 'json'),
output_orient)
return {"statusCode": 200, "body": result}
def handle_request(self, request, func):
orient = request.headers.get('orient', self.orient)
output_orient = request.headers.get('output_orient',
self.output_orient)
if request.content_type == 'application/json':
df = pd.read_json(request.data.decode('utf-8'),
orient=orient,
typ=self.typ,
dtype=False)
elif request.content_type == 'text/csv':
df = pd.read_csv(request.data.decode('utf-8'))
else:
return make_response(
jsonify(message="Request content-type not supported, "
"only application/json and text/csv are "
"supported"), 400)
if self.typ == 'frame' and self.input_columns is not None:
check_dataframe_column_contains(self.input_columns, df)
result = func(df)
result = get_output_str(result, request.headers.get('output', 'json'),
output_orient)
return Response(response=result,
status=200,
mimetype="application/json")
def handle_aws_lambda_event(self, event, func):
if event["headers"].get("Content-Type", "").startswith("images/"):
# decodebytes introduced at python3.1
try:
image_data = self.imread(base64.decodebytes(event["body"]),
pilmode=self.pilmode)
except AttributeError:
image_data = self.imread(
base64.decodestring(event["body"]), # pylint: disable=W1505
pilmode=self.convert_mode,
)
else:
raise BentoMLException(
"BentoML currently doesn't support Content-Type: {content_type} for "
"AWS Lambda".format(
content_type=event["headers"]["Content-Type"]))
if self.after_open:
image_data = self.after_open(image_data)
image_data = self.fastai_vision.pil2tensor(image_data, np.float32)
if self.div:
image_data = image_data.div_(255)
if self.cls:
image_data = self.cls(image_data)
else:
image_data = self.fastai_vision.Image(image_data)
result = func(image_data)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
def handle_request(self, request, func):
"""Handle http request that has image file/s. It will convert image into a
ndarray for the function to consume.
Args:
request: incoming request object.
func: function that will take ndarray as its arg.
options: configuration for handling request object.
Return:
response object
"""
if request.method != 'POST':
return Response(response="Only accept POST request", status=400)
if not self.accept_multiple_files:
input_file = request.files[self.input_names[0]]
file_name = secure_filename(input_file.filename)
check_file_format(file_name, self.accept_file_extensions)
input_data_string = input_file.read()
input_data = np.fromstring(input_data_string, np.uint8)
else:
return Response(response="Only support single file input",
status=400)
result = func(input_data)
result = get_output_str(result, request.headers.get('output', 'json'))
return Response(response=result,
status=200,
mimetype="application/json")
def handle_request(self, request, func):
"""Handle http request that has image file/s. It will convert image into a
ndarray for the function to consume.
Args:
request: incoming request object.
func: function that will take ndarray as its arg.
options: configuration for handling request object.
Return:
response object
"""
try:
from imageio import imread
except ImportError:
raise ImportError("imageio package is required to use ImageHandler")
if request.method != "POST":
return Response(response="Only accept POST request", status=400)
if not self.accept_multiple_files:
input_file = request.files.get(self.input_names)
file_name = secure_filename(input_file.filename)
check_file_format(file_name, self.accept_file_extensions)
input_stream = BytesIO()
input_file.save(input_stream)
input_data = imread(input_file, pilmode=self.pilmode)
else:
return Response(response="Only support single file input", status=400)
result = func(input_data)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result, status=200, mimetype="application/json")
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument("-o",
"--output",
default="str",
choices=["str", "json", "yaml"])
parsed_args = parser.parse_args(args)
file_path = parsed_args.input
check_file_format(file_path, self.accept_file_extensions)
if not os.path.isabs(file_path):
file_path = os.path.abspath(file_path)
try:
from fastai.vision import open_image, Image
except ImportError:
raise ImportError("fastai package is required to use")
image_array = open_image(
fn=file_path,
convert_mode=self.convert_mode,
div=self.div,
after_open=self.after_open,
cls=self.cls or Image,
)
result = func(image_array)
result = get_output_str(result, output_format=parsed_args.output)
print(result)
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument("-o",
"--output",
default="str",
choices=["str", "json", "yaml"])
parsed_args = parser.parse_args(args)
file_path = parsed_args.input
check_file_format(file_path, self.accept_file_extensions)
if not os.path.isabs(file_path):
file_path = os.path.abspath(file_path)
try:
from imageio import imread
except ImportError:
raise ImportError(
"imageio package is required to use ImageHandler")
image_array = imread(file_path, pilmode=self.pilmode)
result = func(image_array)
result = get_output_str(result, output_format=parsed_args.output)
print(result)
def handle_aws_lambda_event(self, event, func):
try:
from imageio import imread
except ImportError:
raise ImportError(
"imageio package is required to use ImageHandler")
if event["headers"].get("Content-Type",
None) in ACCEPTED_CONTENT_TYPES:
# decodebytes introduced at python3.1
try:
image = imread(base64.decodebytes(event["body"]),
pilmode=self.pilmode)
except AttributeError:
image = imread(
base64.decodestring(event["body"]), # pylint: disable=W1505
pilmode=self.pilmode,
)
else:
raise BentoMLException(
"BentoML currently doesn't support Content-Type: {content_type} for "
"AWS Lambda".format(
content_type=event["headers"]["Content-Type"]))
result = func(image)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
def handle_aws_lambda_event(self, event, func):
if event["headers"].get("Content-Type", "").startswith("images/"):
image_data = self.imread(
base64.decodebytes(event["body"]), pilmode=self.pilmode
)
else:
raise BadInput(
"BentoML currently doesn't support Content-Type: {content_type} for "
"AWS Lambda".format(content_type=event["headers"]["Content-Type"])
)
if self.after_open:
image_data = self.after_open(image_data)
image_data = self.fastai_vision.pil2tensor(image_data, np.float32)
if self.div:
image_data = image_data.div_(255)
if self.cls:
image_data = self.cls(image_data)
else:
image_data = self.fastai_vision.Image(image_data)
result = func(image_data)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
def handle_request(self, request, func):
orient = request.headers.get("orient", self.orient)
output_orient = request.headers.get("output_orient",
self.output_orient)
if request.content_type == "application/json":
df = pd.read_json(request.data.decode("utf-8"),
orient=orient,
typ=self.typ,
dtype=False)
elif request.content_type == "text/csv":
df = pd.read_csv(request.data.decode("utf-8"))
else:
return make_response(
jsonify(
message=
"Request content-type not supported, only application/json "
"and text/csv are supported"),
400,
)
if self.typ == "frame" and self.input_dtypes is not None:
check_dataframe_column_contains(self.input_dtypes, df)
result = func(df)
result = get_output_str(result, request.headers.get("output", "json"),
output_orient)
return Response(response=result,
status=200,
mimetype="application/json")
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument("-o",
"--output",
default="str",
choices=["str", "json", "yaml"])
parsed_args = parser.parse_args(args)
file_path = parsed_args.input
verify_image_format_or_raise(file_path, self.accept_image_formats)
if not os.path.isabs(file_path):
file_path = os.path.abspath(file_path)
image_array = self.fastai_vision.open_image(
fn=file_path,
convert_mode=self.convert_mode,
div=self.div,
after_open=self.after_open,
cls=self.cls or self.fastai_vision.Image,
)
result = func(image_array)
result = get_output_str(result, output_format=parsed_args.output)
print(result)
def handle_aws_lambda_event(self, event, func):
orient = event["headers"].get("orient", self.orient)
output_orient = event["headers"].get("output_orient",
self.output_orient)
if event["headers"]["Content-Type"] == "application/json":
df = pd.read_json(event["body"],
orient=orient,
typ=self.typ,
dtype=False)
elif event["headers"]["Content-Type"] == "text/csv":
df = pd.read_csv(event["body"])
else:
return {
"statusCode":
400,
"body":
"Request content-type not supported, only application/json and "
"text/csv are supported",
}
if self.typ == "frame" and self.input_dtypes is not None:
check_dataframe_column_contains(self.input_dtypes, df)
result = func(df)
result = get_output_str(result, event["headers"].get("output", "json"),
output_orient)
return {"statusCode": 200, "body": result}
def handle_aws_lambda_event(self, event, func):
if event["headers"]["Content-Type"] == "application/json":
parsed_json = json.loads(event["body"])
else:
return {"statusCode": 400, "body": "Only accept json as content type"}
result = func(parsed_json)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
def handle_request(self, request, func):
"""Handle http request that has image file/s. It will convert image into a
ndarray for the function to consume.
Args:
request: incoming request object.
func: function that will take ndarray as its arg.
options: configuration for handling request object.
Return:
response object
"""
if request.method != "POST":
return Response(
response="BentoML#ImageHandler only accept POST request",
status=405)
if len(self.input_names) == 1 and len(request.files) == 1:
# Ignore multipart form input name when ImageHandler is intended to accept
# only one image file at a time
input_files = [file for _, file in iteritems(request.files)]
else:
input_files = [
request.files.get(form_input_name)
for form_input_name in self.input_names
if form_input_name in request.files
]
if input_files:
file_names = [
secure_filename(file.filename) for file in input_files
]
try:
for file_name in file_names:
verify_image_format_or_raise(file_name,
self.accept_image_formats)
except ValueError as e:
return Response(response=str(e), status=400)
input_streams = [
BytesIO(input_file.read()) for input_file in input_files
]
else:
data = request.get_data()
if data:
input_streams = (data, )
else:
return Response(
"BentoML#ImageHandler unexpected HTTP request format",
status=400)
input_data = tuple(
self.imread(input_stream, pilmode=self.pilmode)
for input_stream in input_streams)
result = func(*input_data)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result,
status=200,
mimetype="application/json")
def handle_request(self, request, func):
"""Handle http request that has image file/s. It will convert image into a
ndarray for the function to consume.
Args:
request: incoming request object.
func: function that will take ndarray as its arg.
options: configuration for handling request object.
Return:
response object
"""
try:
from imageio import imread
except ImportError:
raise ImportError("imageio package is required to use ImageHandler")
if request.method != "POST":
return Response(response="Only accept POST request", status=400)
if not self.accept_multiple_files:
input_file = request.files.get(self.input_name)
if input_file:
file_name = secure_filename(input_file.filename)
check_file_format(file_name, self.accept_file_extensions)
input_stream = BytesIO(input_file.read())
elif request.data:
input_stream = request.data
else:
raise ValueError(
"BentoML#ImageHandler unexpected HTTP request: %s" % request
)
input_data = imread(input_stream, pilmode=self.pilmode)
result = func(input_data)
else:
input_files = [request.files.get(filename) for filename in self.input_name]
if input_files:
file_names = [secure_filename(file.filename) for file in input_files]
for file_name in file_names:
check_file_format(file_name, self.accept_file_extensions)
input_streams = [
BytesIO(input_file.read()) for
input_file in input_files
]
else:
raise ValueError(
"BentoML#ImageHandler unexpected HTTP request: %s" % request
)
input_data = tuple(
imread(input_stream, pilmode=self.pilmode)
for input_stream in input_streams
)
result = func(*input_data)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result, status=200, mimetype="application/json")
def handle_aws_lambda_event(self, event, func):
if event['headers']['Content-Type'] == 'application/json':
parsed_json = json.loads(event['body'])
else:
return {"statusCode": 400, "body": 'Only accept json as content type'}
result = func(parsed_json)
result = get_output_str(result, event['headers'].get('output', 'json'))
return {"statusCode": 200, "body": result}
def handle_request(self, request, func):
try:
from fastai.vision import Image, pil2tensor
except ImportError:
raise ImportError(
"fastai package is required to use FastaiImageHandler")
try:
from imageio import imread
except ImportError:
raise ImportError(
"imageio package is required to use FastaiImageHandler")
if request.method != "POST":
return Response(response="Only accept POST request", status=400)
input_streams = []
for filename in self.input_names:
file = request.files.get(filename)
if file is not None:
file_name = secure_filename(file.filename)
check_file_format(file_name, self.accept_file_extensions)
input_streams.append(BytesIO(file.read()))
if len(input_streams) == 0:
if request.data:
input_streams = (request.data, )
else:
raise ValueError(
"BentoML#ImageHandler unexpected HTTP request: %s" %
request)
input_data = []
for input_stream in input_streams:
data = imread(input_stream, pilmode=self.convert_mode)
if self.after_open:
data = self.after_open(data)
data = pil2tensor(data, np.float32)
if self.div:
data = data.div_(255)
if self.cls:
data = self.cls(data)
else:
data = Image(data)
input_data.append(data)
result = func(*input_data)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result,
status=200,
mimetype="application/json")
def handle_request(self, request, func):
if request.content_type == 'application/json':
parsed_json = json.loads(request.data.decode('utf-8'))
else:
return make_response(
jsonify(message="Request content-type must be 'application/json'"
"for this BentoService API"), 400)
result = func(parsed_json)
result = get_output_str(result, request.headers.get('output', 'json'))
return Response(response=result, status=200, mimetype="application/json")
def handle_aws_lambda_event(self, event, func):
if event["headers"]["Content-Type"] == "application/json":
parsed_json = json.loads(event["body"])
else:
raise BadInput(
"Request content-type must be 'application/json' for this "
"BentoService API lambda endpoint")
result = func(parsed_json)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
def handle_aws_lambda_event(self, event, func):
if event["headers"].get("Content-Type", "").startswith("images/"):
image = self.imread(base64.decodebytes(event["body"]), pilmode=self.pilmode)
else:
raise BadInput(
"BentoML currently doesn't support Content-Type: {content_type} for "
"AWS Lambda".format(content_type=event["headers"]["Content-Type"])
)
result = func(image)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
def handle_request(self, request, func):
if request.content_type == "application/json":
parsed_json = json.loads(request.data.decode("utf-8"))
else:
raise BadInput(
"Request content-type must be 'application/json' for this "
"BentoService API")
result = func(parsed_json)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result,
status=200,
mimetype="application/json")
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument('--input', required=True)
parser.add_argument('-o', '--output', default="str", choices=['str', 'json', 'yaml'])
parsed_args = parser.parse_args(args)
if os.path.isfile(parsed_args.input):
with open(parsed_args.input, 'r') as content_file:
content = content_file.read()
else:
content = parsed_args.input
input_json = json.loads(content)
result = func(input_json)
result = get_output_str(result, parsed_args.output)
print(result)
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument("-o",
"--output",
default="str",
choices=["str", "json", "yaml"])
parser.add_argument("--orient", default=self.orient)
parser.add_argument("--output_orient", default=self.output_orient)
parsed_args = parser.parse_args(args)
orient = parsed_args.orient
output_orient = parsed_args.output_orient
cli_input = parsed_args.input
if os.path.isfile(cli_input) or is_s3_url(cli_input) or is_url(
cli_input):
if cli_input.endswith(".csv"):
df = pd.read_csv(cli_input)
elif cli_input.endswith(".json"):
df = pd.read_json(cli_input,
orient=orient,
typ=self.typ,
dtype=False)
else:
raise ValueError(
"Input file format not supported, BentoML cli only accepts .json "
"and .csv file")
else:
# Assuming input string is JSON format
try:
df = pd.read_json(cli_input,
orient=orient,
typ=self.typ,
dtype=False)
except ValueError as e:
raise ValueError(
"Unexpected input format, BentoML DataframeHandler expects json "
"string as input: {}".format(e))
if self.typ == "frame" and self.input_dtypes is not None:
check_dataframe_column_contains(self.input_dtypes, df)
result = func(df)
result = get_output_str(result, parsed_args.output, output_orient)
print(result)
def handle_request(self, request, func):
if request.method != "POST":
return Response(response="Only accept POST request", status=400)
input_streams = []
for filename in self.input_names:
file = request.files.get(filename)
if file is not None:
file_name = secure_filename(file.filename)
verify_image_format_or_raise(file_name,
self.accept_image_formats)
input_streams.append(BytesIO(file.read()))
if len(input_streams) == 0:
data = request.get_data()
if data:
input_streams = (data, )
else:
raise ValueError(
"BentoML#ImageHandler unexpected HTTP request: %s" %
request)
input_data = []
for input_stream in input_streams:
data = self.imread(input_stream, pilmode=self.convert_mode)
if self.after_open:
data = self.after_open(data)
data = self.fastai_vision.pil2tensor(data, np.float32)
if self.div:
data = data.div_(255)
if self.cls:
data = self.cls(data)
else:
data = self.fastai_vision.Image(data)
input_data.append(data)
result = func(*input_data)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result,
status=200,
mimetype="application/json")
def handle_request(self, request, func):
"""Handle http request that has image file/s. It will convert image into a
ndarray for the function to consume.
Args:
request: incoming request object.
func: function that will take ndarray as its arg.
options: configuration for handling request object.
Return:
response object
"""
if request.method != "POST":
return Response(response="Only accept POST request", status=400)
input_files = [
request.files.get(filename) for filename in self.input_names
]
if len(input_files) == 1 and input_files[0] is None:
data = request.get_data()
if data:
input_streams = (data, )
else:
raise ValueError(
"BentoML#ImageHandler unexpected HTTP request: %s" %
request)
else:
file_names = [
secure_filename(file.filename) for file in input_files
]
for file_name in file_names:
verify_image_format_or_raise(file_name,
self.accept_image_formats)
input_streams = [
BytesIO(input_file.read()) for input_file in input_files
]
input_data = tuple(
imread(input_stream, pilmode=self.pilmode)
for input_stream in input_streams)
result = func(*input_data)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result,
status=200,
mimetype="application/json")
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument(
"-o", "--output", default="str", choices=["str", "json", "yaml"]
)
parsed_args = parser.parse_args(args)
file_path = parsed_args.input
verify_image_format_or_raise(file_path, self.accept_image_formats)
if not os.path.isabs(file_path):
file_path = os.path.abspath(file_path)
image_array = self.imread(file_path, pilmode=self.pilmode)
result = func(image_array)
result = get_output_str(result, output_format=parsed_args.output)
print(result)
def handle_aws_lambda_event(self, event, func):
try:
from imageio import imread
except ImportError:
raise ImportError(
"imageio package is required to use ImageHandler")
if event["headers"].get("Content-Type",
None) in ACCEPTED_CONTENT_TYPES:
# decodebytes introduced at python3.1
try:
image_data = imread(base64.decodebytes(event["body"]),
pilmode=self.pilmode)
except AttributeError:
image_data = imread(
base64.decodestring(event["body"]), # pylint: disable=W1505
pilmode=self.convert_mode,
)
else:
raise BentoMLException(
"BentoML currently doesn't support Content-Type: {content_type} for "
"AWS Lambda".format(
content_type=event["headers"]["Content-Type"]))
try:
from fastai.vision import pil2tensor, Image
import numpy as np
except ImportError:
raise ImportError("fastai package is required")
if self.after_open:
image_data = self.after_open(image_data)
image_data = pil2tensor(image_data, np.float32)
if self.div:
image_data = image_data.div_(255)
if self.cls:
image_data = self.cls(image_data)
else:
image_data = Image(image_data)
result = func(image_data)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
def handle_request(self, request, func):
try:
from fastai.vision import Image, pil2tensor
import numpy as np
except ImportError:
raise ImportError(
"fastai package is required to use FastaiImageHandler")
try:
from imageio import imread
except ImportError:
raise ImportError(
"imageio package is required to use FastaiImageHandler")
return
if request.method != "POST":
return Response(response="Only accept POST request", status=400)
input_file = request.files.get(self.input_name)
file_name = secure_filename(input_file.filename)
check_file_format(file_name, self.accept_file_extensions)
input_stream = BytesIO(input_file.read())
input_data = imread(input_stream, pilmode=self.convert_mode)
if self.after_open:
input_data = self.after_open(input_data)
input_data = pil2tensor(input_data, np.float32)
if self.div:
input_data = input_data.div_(255)
if self.cls:
input_data = self.cls(input_data)
else:
input_data = Image(input_data)
result = func(input_data)
result = get_output_str(result, request.headers.get("output", "json"))
return Response(response=result,
status=200,
mimetype="application/json")
def handle_aws_lambda_event(self, event, func):
try:
import cv2
except ImportError:
raise ImportError(
"opencv-python package is required to use ImageHandler")
if event['headers'].get('Content-Type',
None) in ACCEPTED_CONTENT_TYPES:
nparr = np.fromstring(base64.b64decode(event['body']), np.uint8)
image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
else:
raise BentoMLException(
"BentoML currently doesn't support Content-Type: {content_type} for AWS Lambda"
.format(content_type=event['headers']['Content-Type']))
result = func(image)
result = get_output_str(result, event['headers'].get('output', 'json'))
return {'statusCode': 200, 'body': result}
def handle_aws_lambda_event(self, event, func):
if event["headers"].get("Content-Type", "").startswith("images/"):
# decodebytes introduced at python3.1
try:
image = imread(base64.decodebytes(event["body"]),
pilmode=self.pilmode)
except AttributeError:
image = imread(
base64.decodestring(event["body"]), # pylint: disable=W1505
pilmode=self.pilmode,
)
else:
raise BentoMLException(
"BentoML currently doesn't support Content-Type: {content_type} for "
"AWS Lambda".format(
content_type=event["headers"]["Content-Type"]))
result = func(image)
result = get_output_str(result, event["headers"].get("output", "json"))
return {"statusCode": 200, "body": result}
