import asyncio
import os
import sys

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

from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask, PipelineParams
from pipecat.services.azure import AzureTTSService
from pipecat.services.openrouter import OpenRouterLLMService
from pipecat.services.openai import OpenAILLMContext
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.audio.vad.silero import SileroVADAnalyzer

load_dotenv(override=True)

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


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

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

        tts = AzureTTSService(
            api_key=os.getenv("AZURE_API_KEY"),
            region="eastus",
            voice="en-US-JennyNeural",
            params=AzureTTSService.InputParams(
                language="en-US",
                rate="1.1",
                style="chat",
            ),
        )

        llm = OpenRouterLLMService(
            api_key=os.getenv("OPENROUTER_API_KEY"), model="microsoft/phi-4"
        )
        messages = [
            {
                "role": "system",
                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way.",
            }
        ]
        context = OpenAILLMContext(messages=messages)
        context_aggregator = llm.create_context_aggregator(context)

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

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

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            await transport.capture_participant_transcription(participant["id"])
            messages.append(
                {"role": "system", "content": "Please introduce yourself to the user."}
            )
            await task.queue_frames([LLMMessagesFrame(messages)])

        runner = PipelineRunner()

        await runner.run(task)


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