# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------

"""
DESCRIPTION:
    This sample demonstrates how to create an AI agent with Microsoft Fabric capabilities
    using the MicrosoftFabricPreviewTool and synchronous Azure AI Projects client. The agent can query
    Fabric data sources and provide responses based on data analysis.

USAGE:
    python sample_agent_fabric.py

    Before running the sample:

    pip install "azure-ai-projects>=2.0.0" python-dotenv

    Set these environment variables with your own values:
    1) FOUNDRY_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
       page of your Microsoft Foundry portal.
    2) FOUNDRY_MODEL_NAME - The deployment name of the AI model, as found under the "Name" column in
       the "Models + endpoints" tab in your Microsoft Foundry project.
    3) FABRIC_PROJECT_CONNECTION_ID - The Fabric project connection ID,
       as found in the "Connections" tab in your Microsoft Foundry project.
    4) FABRIC_USER_INPUT - (Optional) The question to ask. If not set, you will be prompted.
"""

import os
from dotenv import load_dotenv
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import (
    PromptAgentDefinition,
    MicrosoftFabricPreviewTool,
    FabricDataAgentToolParameters,
    ToolProjectConnection,
)

load_dotenv()

endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]

with (
    DefaultAzureCredential() as credential,
    AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
    project_client.get_openai_client() as openai_client,
):
    # [START tool_declaration]
    tool = MicrosoftFabricPreviewTool(
        fabric_dataagent_preview=FabricDataAgentToolParameters(
            project_connections=[
                ToolProjectConnection(project_connection_id=os.environ["FABRIC_PROJECT_CONNECTION_ID"])
            ]
        )
    )
    # [END tool_declaration]

    agent = project_client.agents.create_version(
        agent_name="MyAgent",
        definition=PromptAgentDefinition(
            model=os.environ["FOUNDRY_MODEL_NAME"],
            instructions="You are a helpful assistant.",
            tools=[tool],
        ),
    )
    print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")

    user_input = os.environ.get("FABRIC_USER_INPUT") or input("Enter your question: \n")

    stream_response = openai_client.responses.create(
        tool_choice="required",
        stream=True,
        input=user_input,
        extra_body={"agent_reference": {"name": agent.name, "type": "agent_reference"}},
    )

    for event in stream_response:
        if event.type == "response.created":
            print(f"Follow-up response created with ID: {event.response.id}")
        elif event.type == "response.output_text.delta":
            print(f"Delta: {event.delta}")
        elif event.type == "response.text.done":
            print("\nFollow-up response done!")
        elif event.type == "response.output_item.done":
            if event.item.type == "message":
                item = event.item
                if item.content[-1].type == "output_text":
                    text_content = item.content[-1]
                    for annotation in text_content.annotations:
                        if annotation.type == "url_citation":
                            print(
                                f"URL Citation: {annotation.url}, "
                                f"Start index: {annotation.start_index}, "
                                f"End index: {annotation.end_index}"
                            )
        elif event.type == "response.completed":
            print("\nFollow-up completed!")
            print(f"Full response: {event.response.output_text}")

    print("\nCleaning up...")
    project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
    print("Agent deleted")
