Files
annotations/Azaion.Inference/main.pyx
T
dzaitsev d92da6afa4 Errors sending to UI
notifying client of AI model conversion
2025-05-14 12:43:50 +03:00

92 lines
3.7 KiB
Cython

import queue
import traceback
from queue import Queue
cimport constants
from threading import Thread
import yaml
from api_client cimport ApiClient
from annotation cimport Annotation
from inference cimport Inference
from remote_command cimport RemoteCommand, CommandType
from remote_command_handler cimport RemoteCommandHandler
from credentials cimport Credentials
from file_data cimport FileData
cdef class CommandProcessor:
cdef ApiClient api_client
cdef RemoteCommandHandler remote_handler
cdef object inference_queue
cdef bint running
cdef Inference inference
def __init__(self, int zmq_port, str api_url):
self.api_client = ApiClient(api_url)
self.remote_handler = RemoteCommandHandler(zmq_port, self.on_command)
self.inference_queue = Queue(maxsize=constants.QUEUE_MAXSIZE)
self.remote_handler.start()
self.running = True
self.inference = Inference(self.api_client, self.on_annotation)
def start(self):
while self.running:
try:
command = self.inference_queue.get(timeout=0.5)
self.inference.run_inference(command)
end_inference_command = RemoteCommand(CommandType.INFERENCE_DATA, None, 'DONE')
self.remote_handler.send(command.client_id, end_inference_command.serialize())
except queue.Empty:
continue
except Exception as e:
traceback.print_exc()
print('EXIT!')
cdef on_command(self, RemoteCommand command):
try:
if command.command_type == CommandType.LOGIN:
self.api_client.set_credentials(Credentials.from_msgpack(command.data))
elif command.command_type == CommandType.LOAD:
self.load_file(command)
elif command.command_type == CommandType.INFERENCE:
self.inference_queue.put(command)
elif command.command_type == CommandType.AI_AVAILABILITY_CHECK:
self.build_tensor_engine(command.client_id)
elif command.command_type == CommandType.STOP_INFERENCE:
self.inference.stop()
elif command.command_type == CommandType.EXIT:
t = Thread(target=self.stop) # non-block worker:
t.start()
else:
pass
except Exception as e:
print(f"Error handling client: {e}")
cdef build_tensor_engine(self, client_id):
self.inference.build_tensor_engine(lambda status: self.build_tensor_status_updater(client_id, status))
self.remote_handler.send(client_id, RemoteCommand(CommandType.AI_AVAILABILITY_RESULT, None, 'enabled').serialize())
cdef build_tensor_status_updater(self, bytes client_id, str status):
self.remote_handler.send(client_id, RemoteCommand(CommandType.AI_AVAILABILITY_RESULT, None, status).serialize())
cdef load_file(self, RemoteCommand command):
cdef RemoteCommand response
cdef FileData file_data
cdef bytes file_bytes
try:
file_data = FileData.from_msgpack(command.data)
file_bytes = self.api_client.load_bytes(file_data.filename, file_data.folder)
response = RemoteCommand(CommandType.DATA_BYTES, file_bytes)
except Exception as e:
response = RemoteCommand(CommandType.DATA_BYTES, None, str(e))
self.remote_handler.send(command.client_id, response.serialize())
cdef on_annotation(self, RemoteCommand cmd, Annotation annotation):
cdef RemoteCommand response = RemoteCommand(CommandType.INFERENCE_DATA, annotation.serialize())
self.remote_handler.send(cmd.client_id, response.serialize())
def stop(self):
self.inference.stop()
self.remote_handler.stop()
self.running = False