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

import argparse
import os

from dotenv import load_dotenv
from loguru import logger
from PIL import Image

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
    BotStartedSpeakingFrame,
    BotStoppedSpeakingFrame,
    Frame,
    OutputImageRawFrame,
)
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.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.services.daily import DailyParams

load_dotenv(override=True)


class ImageSyncAggregator(FrameProcessor):
    def __init__(self, speaking_path: str, waiting_path: str):
        super().__init__()
        self._speaking_image = Image.open(speaking_path)
        self._speaking_image_format = self._speaking_image.format
        self._speaking_image_bytes = self._speaking_image.tobytes()

        self._waiting_image = Image.open(waiting_path)
        self._waiting_image_format = self._waiting_image.format
        self._waiting_image_bytes = self._waiting_image.tobytes()

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if isinstance(frame, BotStartedSpeakingFrame):
            await self.push_frame(
                OutputImageRawFrame(
                    image=self._speaking_image_bytes,
                    size=(1024, 1024),
                    format=self._speaking_image_format,
                )
            )

        elif isinstance(frame, BotStoppedSpeakingFrame):
            await self.push_frame(
                OutputImageRawFrame(
                    image=self._waiting_image_bytes,
                    size=(1024, 1024),
                    format=self._waiting_image_format,
                )
            )

        await self.push_frame(frame)


# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_out_enabled=True,
        video_out_width=1024,
        video_out_height=1024,
        vad_analyzer=SileroVADAnalyzer(),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_out_enabled=True,
        video_out_width=1024,
        video_out_height=1024,
        vad_analyzer=SileroVADAnalyzer(),
    ),
}


async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
    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
    )

    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))

    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 and helpful way.",
        },
    ]

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

    image_sync_aggregator = ImageSyncAggregator(
        os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
        os.path.join(os.path.dirname(__file__), "assets", "waiting.png"),
    )

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

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

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Kick off the conversation.
        await task.queue_frames([context_aggregator.user().get_context_frame()])

    @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=handle_sigint)
    await runner.run(task)


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

    main(run_example, transport_params=transport_params)