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228 | @app.post("/invocations")
def predict_image(image: UploadFile = File(...), examination_id: str = Header(None)):
"""
# POST: /invocations
Endpoint to process an image and send it to the inference server.
Args:
image (UploadFile): Image file to process (in the request body).
Headers:
*Content-Type*: Type of the image. Can be "image/jpeg", "image/png", "image/tiff", "image/bmp", "image/jpg".
*Examination-Id*: ID of the examination. Used to track the request.
Returns:
JSON
Raises:
Response: Error response if the content type is not supported.
"""
client = None
request_uuid = str(uuid.uuid4())
try:
if image.content_type not in config.available_content_types:
log_event(
logger,
LogEventDataclass(
event_message="Rejected unsupported content type.",
level=LoggingLevel.WARNING,
model_name=config.model_name,
status=Status.FAILED,
pipeline_step="invocations_validate_request",
examination_id=examination_id,
local_filename=image.filename,
request_uuid=request_uuid,
),
)
return Response(
status_code=415,
content="Cannot decode image data. Is content_type correct?",
media_type="text/plain",
)
client = grpcclient.InferenceServerClient(
url=config.grcp_model_server_address,
verbose=False,
channel_args=(("grpc.lb_policy_name", "round_robin"),),
)
model_config = client.get_model_config(
model_name=config.model_name,
model_version=config.model_version,
as_json=True,
)["config"]
contents = image.file.read()
image_bytes = np.frombuffer(contents, dtype=np.uint8)
img = cv2.imdecode(image_bytes, cv2.IMREAD_COLOR)
if img is None:
raise ValueError("Cannot decode image data.")
log_event(
logger,
LogEventDataclass(
event_message="Received image for inference.",
level=LoggingLevel.INFO,
model_name=config.model_name,
status=Status.SUCCESS,
pipeline_step="invocations_read_image",
examination_id=examination_id,
local_filename=image.filename,
request_uuid=request_uuid,
),
)
run_params = _build_run_params()
inputs = [
grpcclient.InferInput(
"IMAGE",
[1, img.shape[0], img.shape[1], img.shape[2]],
np_to_triton_dtype(img.dtype),
),
grpcclient.InferInput("INPUT_JSON_PARAMS", (1, 1), "BYTES"),
]
inputs[0].set_data_from_numpy(img[np.newaxis, ...])
inputs[1].set_data_from_numpy(np.array([[run_params]], dtype=np.object_))
outputs = [
grpcclient.InferRequestedOutput(model_config["output"][i]["name"])
for i in range(len(model_config["output"]))
]
start = time.time()
result = client.infer(
model_name=config.model_name,
model_version=config.model_version,
inputs=inputs,
outputs=outputs,
)
output_data = _parse_inference_output(model_config, result)
elapsed = time.time() - start
log_event(
logger,
LogEventDataclass(
event_message="Completed inference request.",
level=LoggingLevel.INFO,
model_name=config.model_name,
status=Status.SUCCESS,
pipeline_step="invocations_infer_image",
examination_id=examination_id,
local_filename=image.filename,
request_uuid=request_uuid,
elapsed_time_s=elapsed,
),
)
return JSONResponse(
content={
"filename": image.filename,
"status": "COMPLETED",
"request_uuid": request_uuid,
"examination_id": examination_id,
"output": output_data,
},
status_code=200,
)
except Exception as e:
log_event(
logger,
LogEventDataclass(
event_message="Inference request failed.",
level=LoggingLevel.ERROR,
model_name=config.model_name,
status=Status.ERROR,
pipeline_step="invocations_infer_image",
examination_id=examination_id,
local_filename=image.filename,
request_uuid=request_uuid,
error=type(e).__name__,
error_message=str(e),
),
)
return JSONResponse(
content={
"message": str(e),
"status": "error",
"request_uuid": request_uuid,
"examination_id": examination_id,
},
status_code=400,
)
finally:
if client is not None:
client.close()
|