#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import os

from bedrock_agentcore import BedrockAgentCoreApp
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.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.serializers.protobuf import ProtobufFrameSerializer
from pipecat.services.aws.llm import AWSBedrockLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.transports.base_transport import BaseTransport
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams, FastAPIWebsocketTransport

app = BedrockAgentCoreApp()

load_dotenv(override=True)


async def fetch_weather_from_api(params: FunctionCallParams):
    await params.result_callback({"conditions": "nice", "temperature": "75"})


async def fetch_restaurant_recommendation(params: FunctionCallParams):
    await params.result_callback({"name": "The Golden Dragon"})


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

    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
    )

    # Automatically uses AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, and AWS_REGION env vars.
    llm = AWSBedrockLLMService(
        model="us.amazon.nova-2-lite-v1:0",
        params=AWSBedrockLLMService.InputParams(temperature=0.8),
    )

    # You can also register a function_name of None to get all functions
    # sent to the same callback with an additional function_name parameter.
    llm.register_function("get_current_weather", fetch_weather_from_api)
    llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)

    @llm.event_handler("on_function_calls_started")
    async def on_function_calls_started(service, function_calls):
        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))

    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 user's location.",
            },
        },
        required=["location", "format"],
    )
    restaurant_function = FunctionSchema(
        name="get_restaurant_recommendation",
        description="Get a restaurant recommendation",
        properties={
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA",
            },
        },
        required=["location"],
    )
    tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])

    messages = [
        {
            "role": "system",
            "content": "You are a helpful LLM in a voice call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
        },
        {"role": "user", "content": "Say hello and briefly introduce yourself."},
    ]

    context = LLMContext(messages, tools)
    context_aggregator = LLMContextAggregatorPair(context)

    rtvi = RTVIProcessor()

    pipeline = Pipeline(
        [
            transport.input(),
            rtvi,
            stt,
            context_aggregator.user(),
            llm,
            tts,
            transport.output(),
            context_aggregator.assistant(),
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
        observers=[RTVIObserver(rtvi)],
    )

    @rtvi.event_handler("on_client_ready")
    async def on_client_ready(rtvi):
        logger.info(f"Client ready")
        await rtvi.set_bot_ready()
        # Kick off the conversation
        await task.queue_frames([LLMRunFrame()])

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")

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

    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)

    await runner.run(task)


@app.websocket
async def agentcore_bot(websocket, context):
    """Bot entry point for running on Amazon Bedrock AgentCore Runtime."""
    await websocket.accept()

    transport = FastAPIWebsocketTransport(
        websocket=websocket,
        params=FastAPIWebsocketParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            add_wav_header=False,
            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
            turn_analyzer=LocalSmartTurnAnalyzerV3(),
            serializer=ProtobufFrameSerializer(),
        ),
    )

    await run_bot(transport, RunnerArguments())


if __name__ == "__main__":
    # Running on AgentCore Runtime
    app.run()