#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

"""Example: async function call with the OpenAI Realtime LLM service.

The ``get_current_weather`` tool is registered with
``cancel_on_interruption=False`` and simulates a slow API call (10s sleep).
While the call is in flight the conversation continues; the result arrives
later via the async-tool mechanism and is forwarded to OpenAI Realtime as a
``function_call_output`` so the model can integrate it naturally into its
next turn.
"""

import asyncio
import os
import random
from datetime import datetime

from dotenv import load_dotenv
from loguru import logger

from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.worker import PipelineParams, PipelineWorker
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.openai.realtime.events import (
    AudioConfiguration,
    AudioInput,
    InputAudioNoiseReduction,
    InputAudioTranscription,
    SemanticTurnDetection,
    SessionProperties,
)
from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams

load_dotenv(override=True)


async def fetch_weather_from_api(params: FunctionCallParams):
    # Simulate a long-running API call so we can demonstrate that the
    # conversation continues while the tool is in flight.
    await asyncio.sleep(10)
    temperature = (
        random.randint(60, 85)
        if params.arguments["format"] == "fahrenheit"
        else random.randint(15, 30)
    )
    await params.result_callback(
        {
            "conditions": "nice",
            "temperature": temperature,
            "location": params.arguments["location"],
            "format": params.arguments["format"],
            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
        }
    )


weather_function = FunctionSchema(
    name="get_current_weather",
    description="Get the current weather",
    properties={
        "location": {
            "type": "string",
            "description": "The city and state, e.g. San Francisco, CA",
        },
        "format": {
            "type": "string",
            "enum": ["celsius", "fahrenheit"],
            "description": "The temperature unit to use. Infer this from the users location.",
        },
    },
    required=["location", "format"],
)

tools = ToolsSchema(standard_tools=[weather_function])


system_instruction = (
    "You are a friendly assistant. The user and you will engage in a spoken "
    "dialog exchanging the transcripts of a natural real-time conversation. "
    "Keep your responses short, generally two or three sentences for chatty "
    "scenarios. When the user asks for the weather, call get_current_weather. "
    "While you wait for the result, keep chatting with the user. When the "
    "result arrives, share it with the user naturally."
)


transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot")

    llm = OpenAIRealtimeLLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        settings=OpenAIRealtimeLLMService.Settings(
            system_instruction=system_instruction,
            session_properties=SessionProperties(
                audio=AudioConfiguration(
                    input=AudioInput(
                        transcription=InputAudioTranscription(),
                        turn_detection=SemanticTurnDetection(),
                        noise_reduction=InputAudioNoiseReduction(type="near_field"),
                    )
                ),
            ),
        ),
    )

    llm.register_function(
        "get_current_weather",
        fetch_weather_from_api,
        cancel_on_interruption=False,
    )

    context = LLMContext(tools=tools)
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
    )

    pipeline = Pipeline(
        [
            transport.input(),
            user_aggregator,
            llm,
            transport.output(),
            assistant_aggregator,
        ]
    )

    worker = PipelineWorker(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        context.add_message(
            {"role": "developer", "content": "Please introduce yourself to the user."}
        )
        await worker.queue_frames([LLMRunFrame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")
        await worker.cancel()

    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
    await runner.run(worker)


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


if __name__ == "__main__":
    from pipecat.runner.run import main

    main()