text: # Copyright (c) MONAI Consortium
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #     http://www.apache.org/licenses/LICENSE-2.0
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import logging
  import time
  from typing import Dict, Optional
  
  from fastapi import APIRouter
  
  from monailabel.interfaces.app import MONAILabelApp
  from monailabel.interfaces.utils.app import app_instance
  
  logger = logging.getLogger(__name__)
  
  router = APIRouter(
      prefix="/activelearning",
      tags=["ActiveLearning"],
      responses={404: {"description": "Not found"}},
  )
  
  cached_digest: Dict = dict()
  
  
  def sample(strategy: str, params: Optional[dict] = None):
      request = {"strategy": strategy}
  
      instance: MONAILabelApp = app_instance()
      config = instance.info().get("config", {}).get("activelearning", {})
      request.update(config)
  
      params = params if params is not None else {}
      request.update(params)
  
      logger.info(f"Active Learning Request: {request}")
      result = instance.next_sample(request)
      if not result:
          return {}
  
      image_id = result["id"]
      image_info = instance.datastore().get_image_info(image_id)
  
      strategy_info = image_info.get("strategy", {})
      strategy_info[strategy] = {"ts": int(time.time()), "client_id": params.get("client_id")}
      instance.datastore().update_image_info(image_id, {"strategy": strategy_info})
  
      result = {
          "id": image_id,
          **image_info,
      }
      logger.info(f"Next sample: {result}")
      return result
  
  
  @router.post("/{strategy}", summary="Run Active Learning strategy to get next sample")
  async def api_sample(strategy: str, params: Optional[dict] = None):
      return sample(strategy, params)
  