text: # coding=utf-8
  # ------------------------------------
  # Copyright (c) Microsoft Corporation.
  # Licensed under the MIT License.
  # ------------------------------------
  
  """
  FILE: sample_analyze_orchestration_app_qna_response.py
  
  DESCRIPTION:
      This sample demonstrates how to analyze user query using an orchestration project.
      In this sample, orchestration project's top intent will map to a Qna project.
  
      For more info about how to setup a CLU orchestration project, see the README.
  
  USAGE:
      python sample_analyze_orchestration_app_qna_response.py
  
      Set the environment variables with your own values before running the sample:
      1) AZURE_CONVERSATIONS_ENDPOINT - the endpoint to your CLU resource.
      2) AZURE_CONVERSATIONS_KEY - your CLU API key.
      3) AZURE_CONVERSATIONS_WORKFLOW_PROJECT - the name of your CLU orchestration project.
  """
  
  def sample_analyze_orchestration_app_qna_response():
      # [START analyze_orchestration_app_qna_response]
      # import libraries
      import os
      from azure.core.credentials import AzureKeyCredential
  
      from azure.ai.language.conversations import ConversationAnalysisClient
      from azure.ai.language.conversations.models import ConversationAnalysisOptions
  
      # get secrets
      conv_endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
      conv_key = os.environ["AZURE_CONVERSATIONS_KEY"]
      orchestration_project = os.environ["AZURE_CONVERSATIONS_WORKFLOW_PROJECT"]
  
      # prepare data
      query = "How do you make sushi rice?",
      input = ConversationAnalysisOptions(
          query=query
      )
  
      # analyze query
      client = ConversationAnalysisClient(conv_endpoint, AzureKeyCredential(conv_key))
      with client:
          result = client.analyze_conversations(
              input,
              project_name=orchestration_project,
              deployment_name='production',
          )
  
      # view result
      print("query: {}".format(result.query))
      print("project kind: {}\n".format(result.prediction.project_kind))
  
      print("view top intent:")
      top_intent = result.prediction.top_intent
      print("\ttop intent: {}".format(top_intent))
  
      top_intent_object = result.prediction.intents[0]
      print("\tconfidence score: {}\n".format(top_intent_object.confidence_score))
  
      print("view qna result:")
      qna_result = result.prediction.intents[0]
      print("\tanswer: {}\n".format(qna_result))
      # [END analyze_orchestration_app_qna_response]
  
  if __name__ == '__main__':
      sample_analyze_orchestration_app_qna_response()