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

import asyncio
import os
import sys

import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure

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.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.anthropic import AnthropicLLMService
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport

load_dotenv(override=True)

logger.remove(0)
logger.add(sys.stderr, level="DEBUG")


async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
    location = arguments["location"]
    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")


async def main():
    async with aiohttp.ClientSession() as session:
        (room_url, token) = await configure(session)

        transport = DailyTransport(
            room_url,
            token,
            "Respond bot",
            DailyParams(
                audio_out_enabled=True,
                transcription_enabled=True,
                vad_enabled=True,
                vad_analyzer=SileroVADAnalyzer(),
            ),
        )

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

        llm = AnthropicLLMService(
            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
        )
        llm.register_function("get_weather", get_weather)

        weather_function = FunctionSchema(
            name="get_weather",
            description="Get the current weather",
            properties={
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA",
                },
            },
            required=["location"],
        )
        tools = ToolsSchema(standard_tools=[weather_function])

        # todo: test with very short initial user message

        # messages = [{"role": "system",
        #              "content": "You are a helpful assistant who can report the weather in any location in the universe. Respond concisely. Your response will be turned into speech so use only simple words and punctuation."},
        #             {"role": "user",
        #              "content": " Start the conversation by introducing yourself."}]

        messages = [{"role": "user", "content": "Say 'hello' to start the conversation."}]

        context = OpenAILLMContext(messages, tools)
        context_aggregator = llm.create_context_aggregator(context)

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                context_aggregator.user(),  # User spoken responses
                llm,  # LLM
                tts,  # TTS
                transport.output(),  # Transport bot output
                context_aggregator.assistant(),  # Assistant spoken responses and tool context
            ]
        )

        task = PipelineTask(
            pipeline,
            params=PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
            ),
        )

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            await transport.capture_participant_transcription(participant["id"])
            # Kick off the conversation.
            await task.queue_frames([context_aggregator.user().get_context_frame()])

        runner = PipelineRunner()

        await runner.run(task)


if __name__ == "__main__":
    asyncio.run(main())