def handle_aws_lambda_event(self, event, func):
if event["headers"].get("Content-Type", None) == "text/csv":
df = pd.read_csv(event["body"])
else:
# Optimistically assuming Content-Type to be "application/json"
try:
df = pd.read_json(event["body"], orient=self.orient, typ=self.typ)
except ValueError:
raise BadInput(
"Failed parsing request data, only Content-Type application/json "
"and text/csv are supported in BentoML DataframeInput"
)
if self.typ == "frame" and self.input_dtypes is not None:
check_dataframe_column_contains(self.input_dtypes, df)
result = func(df)
return self.output_adapter.to_aws_lambda_event(result, event)
def handle_cli(self, args, func):
parser = argparse.ArgumentParser()
parser.add_argument("--input", required=True)
parser.add_argument(
"--orient",
default=self.orient,
choices=PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS,
)
parsed_args, unknown_args = parser.parse_known_args(args)
orient = parsed_args.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 BadInput(
"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 BadInput(
"Unexpected input format, BentoML DataframeInput 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)
self.output_adapter.to_cli(result, unknown_args)
def handle_request(self, request: flask.Request, func):
if request.content_type == "text/csv":
csv_string = StringIO(request.get_data(as_text=True))
df = pd.read_csv(csv_string)
else:
# Optimistically assuming Content-Type to be "application/json"
try:
df = pd.read_json(
request.get_data(as_text=True), orient=self.orient, typ=self.typ,
)
except ValueError:
raise BadInput(
"Failed parsing request data, only Content-Type application/json "
"and text/csv are supported in BentoML DataframeInput"
)
if self.typ == "frame" and self.input_dtypes is not None:
check_dataframe_column_contains(self.input_dtypes, df)
result = func(df)
return self.output_adapter.to_response(result, request)
