text: # Copyright 2020 - 2021 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 json
  import logging
  import os
  import pathlib
  import shutil
  import tempfile
  from enum import Enum
  from typing import Optional
  
  from fastapi import APIRouter, File, Form, HTTPException, UploadFile
  from fastapi.responses import FileResponse, Response
  from requests_toolbelt import MultipartEncoder
  from starlette.background import BackgroundTasks
  
  from monailabel.interfaces.app import MONAILabelApp
  from monailabel.interfaces.utils.app import app_instance
  from monailabel.utils.others.generic import get_mime_type, remove_file
  
  logger = logging.getLogger(__name__)
  
  router = APIRouter(
      prefix="/infer",
      tags=["Infer"],
      responses={
          404: {"description": "Not found"},
          200: {
              "description": "OK",
              "content": {
                  "multipart/form-data": {
                      "schema": {
                          "type": "object",
                          "properties": {
                              "points": {
                                  "type": "string",
                                  "description": "Reserved for future; Currently it will be empty",
                              },
                              "file": {
                                  "type": "string",
                                  "format": "binary",
                                  "description": "The result NIFTI image which will have segmentation mask",
                              },
                          },
                      },
                      "encoding": {
                          "points": {"contentType": "text/plain"},
                          "file": {"contentType": "application/octet-stream"},
                      },
                  },
                  "application/json": {"schema": {"type": "string", "example": "{}"}},
                  "application/octet-stream": {"schema": {"type": "string", "format": "binary"}},
              },
          },
      },
  )
  
  
  class ResultType(str, Enum):
      image = "image"
      json = "json"
      all = "all"
  
  
  def send_response(datastore, result, output, background_tasks):
      res_img = result.get("label")
      res_tag = result.get("tag")
      res_json = result.get("params")
  
      if res_img:
          if not os.path.exists(res_img):
              res_img = datastore.get_label_uri(res_img, res_tag)
          else:
              background_tasks.add_task(remove_file, res_img)
  
      if output == "json":
          return res_json
  
      m_type = get_mime_type(res_img)
  
      if output == "image":
          return FileResponse(res_img, media_type=m_type, filename=os.path.basename(res_img))
  
      res_fields = dict()
      res_fields["params"] = (None, json.dumps(res_json), "application/json")
      res_fields["image"] = (os.path.basename(res_img), open(res_img, "rb"), m_type)
  
      return_message = MultipartEncoder(fields=res_fields)
      return Response(content=return_message.to_string(), media_type=return_message.content_type)
  
  
  @router.post("/{model}", summary="Run Inference for supported model")
  async def run_inference(
      background_tasks: BackgroundTasks,
      model: str,
      image: str = "",
      params: str = Form("{}"),
      file: UploadFile = File(None),
      label: UploadFile = File(None),
      output: Optional[ResultType] = None,
  ):
      request = {"model": model, "image": image}
  
      if not file and not image:
          raise HTTPException(status_code=500, detail="Neither Image nor File input is provided")
  
      if file:
          file_ext = "".join(pathlib.Path(file.filename).suffixes) if file.filename else ".nii.gz"
          image_file = tempfile.NamedTemporaryFile(suffix=file_ext).name
  
          with open(image_file, "wb") as buffer:
              shutil.copyfileobj(file.file, buffer)
              request["image"] = image_file
              background_tasks.add_task(remove_file, image_file)
  
      if label:
          file_ext = "".join(pathlib.Path(label.filename).suffixes) if label.filename else ".nii.gz"
          label_file = tempfile.NamedTemporaryFile(suffix=file_ext).name
  
          with open(label_file, "wb") as buffer:
              shutil.copyfileobj(label.file, buffer)
              request["label"] = label_file
              background_tasks.add_task(remove_file, label_file)
  
      instance: MONAILabelApp = app_instance()
      config = instance.info().get("config", {}).get("infer", {})
      request.update(config)
  
      p = json.loads(params) if params else {}
      request.update(p)
  
      logger.info(f"Infer Request: {request}")
      result = instance.infer(request)
      if result is None:
          raise HTTPException(status_code=500, detail="Failed to execute infer")
      return send_response(instance.datastore(), result, output, background_tasks)
  