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

"""Gemini Bot Implementation.

This module implements a chatbot using Google's Gemini Multimodal Live model.
It includes:
- Real-time audio/video interaction through Daily
- Animated robot avatar
- Speech-to-speech model

The bot runs as part of a pipeline that processes audio/video frames and manages
the conversation flow using Gemini's streaming capabilities.
"""

import asyncio
import os
import sys

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

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import (
    BotStartedSpeakingFrame,
    BotStoppedSpeakingFrame,
    EndFrame,
    Frame,
    OutputImageRawFrame,
    SpriteFrame,
)
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.processors.frameworks.rtvi import (
    RTVIBotTranscriptionProcessor,
    RTVIMetricsProcessor,
    RTVISpeakingProcessor,
    RTVIUserTranscriptionProcessor,
)
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

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(OutputImageRawFrame(image=img.tobytes(), size=img.size, format=img.format))

# Create a smooth animation by adding reversed frames
flipped = sprites[::-1]
sprites.extend(flipped)

# Define static and animated states
quiet_frame = sprites[0]  # Static frame for when bot is listening
talking_frame = SpriteFrame(images=sprites)  # Animation sequence for when bot is talking


class TalkingAnimation(FrameProcessor):
    """Manages the bot's visual animation states.

    Switches between static (listening) and animated (talking) states based on
    the bot's current speaking status.
    """

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

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        """Process incoming frames and update animation state.

        Args:
            frame: The incoming frame to process
            direction: The direction of frame flow in the pipeline
        """
        await super().process_frame(frame, direction)

        # Switch to talking animation when bot starts speaking
        if isinstance(frame, BotStartedSpeakingFrame):
            if not self._is_talking:
                await self.push_frame(talking_frame)
                self._is_talking = True
        # Return to static frame when bot stops speaking
        elif isinstance(frame, BotStoppedSpeakingFrame):
            await self.push_frame(quiet_frame)
            self._is_talking = False

        await self.push_frame(frame, direction)


async def main():
    """Main bot execution function.

    Sets up and runs the bot pipeline including:
    - Daily video transport with specific audio parameters
    - Gemini Live multimodal model integration
    - Voice activity detection
    - Animation processing
    - RTVI event handling
    """
    async with aiohttp.ClientSession() as session:
        (room_url, token) = await configure(session)

        # Set up Daily transport with specific audio/video parameters for Gemini
        transport = DailyTransport(
            room_url,
            token,
            "Chatbot",
            DailyParams(
                audio_in_sample_rate=16000,
                audio_out_sample_rate=24000,
                audio_out_enabled=True,
                camera_out_enabled=True,
                camera_out_width=1024,
                camera_out_height=576,
                vad_enabled=True,
                vad_audio_passthrough=True,
                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
            ),
        )

        # Initialize the Gemini Multimodal Live model
        llm = GeminiMultimodalLiveLLMService(
            api_key=os.getenv("GEMINI_API_KEY"),
            voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
            transcribe_user_audio=True,
            transcribe_model_audio=True,
        )

        messages = [
            {
                "role": "user",
                "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.",
            },
        ]

        # Set up conversation context and management
        # The context_aggregator will automatically collect conversation context
        context = OpenAILLMContext(messages)
        context_aggregator = llm.create_context_aggregator(context)

        ta = TalkingAnimation()

        #
        # RTVI events for Pipecat client UI
        #

        # This will send `user-*-speaking` and `bot-*-speaking` messages.
        rtvi_speaking = RTVISpeakingProcessor()

        # This will emit UserTranscript events.
        rtvi_user_transcription = RTVIUserTranscriptionProcessor()

        # This will emit BotTranscript events.
        rtvi_bot_transcription = RTVIBotTranscriptionProcessor()

        # This will send `metrics` messages.
        rtvi_metrics = RTVIMetricsProcessor()

        pipeline = Pipeline(
            [
                transport.input(),
                context_aggregator.user(),
                llm,
                rtvi_speaking,
                rtvi_user_transcription,
                rtvi_bot_transcription,
                ta,
                rtvi_metrics,
                transport.output(),
                context_aggregator.assistant(),
            ]
        )

        task = PipelineTask(
            pipeline,
            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
            ),
        )
        await task.queue_frame(quiet_frame)

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

        @transport.event_handler("on_participant_left")
        async def on_participant_left(transport, participant, reason):
            print(f"Participant left: {participant}")
            await task.queue_frame(EndFrame())

        runner = PipelineRunner()

        await runner.run(task)


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