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

import io
import os
import re

from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import (
    BotStartedSpeakingFrame,
    BotStoppedSpeakingFrame,
    CancelFrame,
    EndFrame,
    Frame,
    FunctionCallResultFrame,
    InputAudioRawFrame,
    InterruptionFrame,
    LLMRunFrame,
    LLMTextFrame,
    StartFrame,
    UserStartedSpeakingFrame,
    UserStoppedSpeakingFrame,
    VADUserStartedSpeakingFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
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.daily.transport import DailyParams

load_dotenv(override=True)


class CustomFrameProcessor(FrameProcessor):
    """CustomFrameProcessor does 3 things:

    1. keeps count of `InputAudioRawFrame` frames and logs count
    when a `UserStoppedSpeakingFrame` is emitted.

    2. Filters `LLMTextFrame` frames and replaces "the" with "the pumpkin".

    3. Logs the following frames:
        BotStartedSpeakingFrame
        BotStoppedSpeakingFrame
        CancelFrame
        EndFrame
        InterruptionFrame
        StartFrame
        UserStartedSpeakingFrame
        VADUserStartedSpeakingFrame

    4. Always pushes all frames

    """

    def __init__(self):
        super().__init__()
        self._raw_audio_input_frame_count = 0

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

        #### 1.
        # InputAudioRawFrames are noisy- probably don't want to log every instance
        # keep a count and only log it when we see `UserStoppedSpeakingFrame`
        if isinstance(frame, InputAudioRawFrame):
            self._raw_audio_input_frame_count = self._raw_audio_input_frame_count + 1
            await self.push_frame(frame, direction)

        elif isinstance(frame, UserStoppedSpeakingFrame):
            logger.info(
                f"* * frame: {frame}; number of `InputAudioRawFrame` frames so far: {self._raw_audio_input_frame_count}"
            )
            await self.push_frame(frame, direction)

        #### 2.
        # everytime the LLM's response includes "the", replace it with "the pumpkin"
        elif isinstance(frame, LLMTextFrame):
            if "the" in frame.text:
                text = re.sub(r" the\b", " the pumpkin", frame.text)
                frame.text = text
            await self.push_frame(frame, direction)

        #### 3.
        # frames types to log
        elif isinstance(
            frame,
            (
                BotStartedSpeakingFrame,
                BotStoppedSpeakingFrame,
                CancelFrame,
                EndFrame,
                InterruptionFrame,
                StartFrame,
                UserStartedSpeakingFrame,
                VADUserStartedSpeakingFrame,
            ),
        ):
            logger.info(f"* * frame: {frame}")
            await self.push_frame(frame, direction)

        #### 4.
        # ALWAYS push all other frames
        else:
            # SUPER IMPORTANT: always push every frame!
            await self.push_frame(frame, direction)


# 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,
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    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"))

    custom_frame_processor = CustomFrameProcessor()

    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 = LLMContext(messages)
    context_aggregator = LLMContextAggregatorPair(context)

    pipeline = Pipeline(
        [
            transport.input(),
            stt,
            context_aggregator.user(),
            llm,
            custom_frame_processor,  # filter and log frames
            tts,
            transport.output(),
            context_aggregator.assistant(),
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected: {client}")
        # Kick off the conversation.
        messages.append(
            {
                "role": "system",
                "content": "Please introduce yourself to the user and inform them that your responses illustrate use of a Custom Frame Processor.",
            }
        )
        await task.queue_frames([LLMRunFrame()])

    @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=runner_args.handle_sigint)

    await runner.run(task)


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


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

    main()