# coding=utf-8
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------

"""
FILE: sample_conversation_prediction_with_options.py

DESCRIPTION:
    This sample demonstrates how to analyze an utterance using a CLU Conversation project (sync),
    showing how to set additional options such as `verbose=True`.

USAGE:
    python sample_conversation_prediction_with_options.py

REQUIRED ENV VARS (for AAD / DefaultAzureCredential):
    AZURE_CONVERSATIONS_ENDPOINT
    AZURE_CLIENT_ID
    AZURE_TENANT_ID
    AZURE_CLIENT_SECRET
    AZURE_CONVERSATIONS_PROJECT_NAME
    AZURE_CONVERSATIONS_DEPLOYMENT_NAME

NOTE:
    If you want to use AzureKeyCredential instead, set:
      - AZURE_CONVERSATIONS_ENDPOINT
      - AZURE_CONVERSATIONS_KEY
"""

# [START conversation_prediction_with_options]
import os

from azure.identity import DefaultAzureCredential
from azure.ai.language.conversations import ConversationAnalysisClient
from azure.ai.language.conversations.models import (
    ConversationLanguageUnderstandingInput,
    ConversationAnalysisInput,
    TextConversationItem,
    ConversationActionContent,
    StringIndexType,
    ConversationActionResult,
    ConversationPrediction,
    DateTimeResolution,
)


def sample_conversation_prediction_with_options():
    # settings
    endpoint = os.environ["AZURE_CONVERSATIONS_ENDPOINT"]
    project_name = os.environ["AZURE_CONVERSATIONS_PROJECT_NAME"]
    deployment_name = os.environ["AZURE_CONVERSATIONS_DEPLOYMENT_NAME"]

    credential = DefaultAzureCredential()
    client = ConversationAnalysisClient(endpoint, credential=credential)

    # build request with verbose=True
    data = ConversationLanguageUnderstandingInput(
        conversation_input=ConversationAnalysisInput(
            conversation_item=TextConversationItem(
                id="1",
                participant_id="participant1",
                text="Send an email to Carol about tomorrow's demo",
            )
        ),
        action_content=ConversationActionContent(
            project_name=project_name,
            deployment_name=deployment_name,
            string_index_type=StringIndexType.UTF16_CODE_UNIT,
            verbose=True,
        ),
    )

    # call sync API
    response = client.analyze_conversation(data)

    if isinstance(response, ConversationActionResult):
        pred = response.result.prediction
        if isinstance(pred, ConversationPrediction):
            # top intent
            print(f"Top intent: {pred.top_intent}\n")

            # intents
            print("Intents:")
            for intent in pred.intents or []:
                print(f"  Category: {intent.category}")
                print(f"  Confidence: {intent.confidence}")
                print()

            # entities
            print("Entities:")
            for entity in pred.entities or []:
                print(f"  Category: {entity.category}")
                print(f"  Text: {entity.text}")
                print(f"  Offset: {entity.offset}")
                print(f"  Length: {entity.length}")
                print(f"  Confidence: {entity.confidence}")

                for res in entity.resolutions or []:
                    if isinstance(res, DateTimeResolution):
                        print("  DateTime Resolution:")
                        print(f"    Sub Kind: {res.date_time_sub_kind}")
                        print(f"    Timex: {res.timex}")
                        print(f"    Value: {res.value}")
                print()
    else:
        print("Unexpected result type from analyze_conversation.")


# [END conversation_prediction_with_options]


def main():
    sample_conversation_prediction_with_options()


if __name__ == "__main__":
    main()
