import asyncio
import aiohttp
import os
import sys

from PIL import Image

from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
from pipecat.frames.frames import (
    AudioRawFrame,
    ImageRawFrame,
    SpriteFrame,
    Frame,
    LLMMessagesFrame,
    TTSStoppedFrame
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

from runner import configure

from loguru import logger

from dotenv import load_dotenv
load_dotenv(override=True)

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

sprites = []

script_dir = os.path.dirname(__file__)

for i in range(1, 26):
    # Build the full path to the image file
    full_path = os.path.join(script_dir, f"assets/robot0{i}.png")
    # Get the filename without the extension to use as the dictionary key
    # Open the image and convert it to bytes
    with Image.open(full_path) as img:
        sprites.append(ImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))

flipped = sprites[::-1]
sprites.extend(flipped)

# When the bot isn't talking, show a static image of the cat listening
quiet_frame = sprites[0]
talking_frame = SpriteFrame(images=sprites)


class TalkingAnimation(FrameProcessor):
    """
    This class starts a talking animation when it receives an first AudioFrame,
    and then returns to a "quiet" sprite when it sees a LLMResponseEndFrame.
    """

    def __init__(self):
        super().__init__()
        self._is_talking = False

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        if isinstance(frame, AudioRawFrame):
            if not self._is_talking:
                await self.push_frame(talking_frame)
                self._is_talking = True
        elif isinstance(frame, TTSStoppedFrame):
            await self.push_frame(quiet_frame)
            self._is_talking = False

        await self.push_frame(frame)


async def main(room_url: str, token):
    async with aiohttp.ClientSession() as session:
        transport = DailyTransport(
            room_url,
            token,
            "Chatbot",
            DailyParams(
                audio_out_enabled=True,
                camera_out_enabled=True,
                camera_out_width=1024,
                camera_out_height=576,
                transcription_enabled=True,
                vad_enabled=True
            )
        )

        tts = ElevenLabsTTSService(
            aiohttp_session=session,
            api_key=os.getenv("ELEVENLABS_API_KEY"),
            voice_id="pNInz6obpgDQGcFmaJgB",
        )

        llm = OpenAILLMService(
            api_key=os.getenv("OPENAI_API_KEY"),
            model="gpt-4-turbo-preview")

        messages = [
            {
                "role": "system",
                "content": "You are Chatbot, a friendly, helpful robot. 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, but keep your responses brief. Start by introducing yourself.",
            },
        ]

        user_response = LLMUserResponseAggregator()

        ta = TalkingAnimation()

        pipeline = Pipeline([transport.input(), user_response, llm, tts, ta, transport.output()])

        task = PipelineTask(pipeline)
        await task.queue_frame(quiet_frame)

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            transport.capture_participant_transcription(participant["id"])
            await task.queue_frames([LLMMessagesFrame(messages)])

        runner = PipelineRunner()

        await runner.run(task)


if __name__ == "__main__":
    (url, token) = configure()
    asyncio.run(main(url, token))