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

import aiohttp
import asyncio
import os
import sys
import time

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMMessagesFrame, TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import (
    OpenAILLMContext,
)
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.openai import OpenAILLMService
from pipecat.sync.event_notifier import EventNotifier
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.processors.frame_processor import FrameProcessor, FrameDirection
from pipecat.frames.frames import (
    CancelFrame,
    EndFrame,
    Frame,
    StartFrame,
    StartInterruptionFrame,
    SystemFrame,
    TranscriptionFrame,
    UserStartedSpeakingFrame,
    UserStoppedSpeakingFrame,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
from pipecat.sync.base_notifier import BaseNotifier
from pipecat.processors.filters.function_filter import FunctionFilter


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")


classifier_statement = "Determine if the user's statement ends with a complete sentence or question. The user text is transcribed speech. It may contain multiple fragments concatentated together. Categorize the text as either complete with the user now expecting a response, or incomplete. Return 'YES' if text is likely complete and the user is expecting a response. Return 'NO' if the text seems to be a partial expression or unfinished thought."


class StatementJudgeContextFilter(FrameProcessor):
    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        # We must not block system frames.
        if isinstance(frame, SystemFrame):
            await self.push_frame(frame, direction)
            return

        # We only want to handle OpenAILLMContextFrames, and only want to push a simple
        # messages frame that contains a system prompt and the most recent user messages,
        # concatenated.
        if isinstance(frame, OpenAILLMContextFrame):
            logger.debug(f"Context Frame: {frame}")
            # Take text content from the most recent user messages.
            messages = frame.context.messages
            user_text_messages = []
            last_assistant_message = None
            for message in reversed(messages):
                if message["role"] != "user":
                    if message["role"] == "assistant":
                        last_assistant_message = message
                    break
                if isinstance(message["content"], str):
                    user_text_messages.append(message["content"])
                elif isinstance(message["content"], list):
                    for content in message["content"]:
                        if content["type"] == "text":
                            user_text_messages.append(content["text"])
            # If we have any user text content, push an LLMMessagesFrame
            if user_text_messages:
                logger.debug(f"User text messages: {user_text_messages}")
                user_message = " ".join(reversed(user_text_messages))
                logger.debug(f"User message: {user_message}")
                messages = [
                    {
                        "role": "system",
                        "content": classifier_statement,
                    }
                ]
                if last_assistant_message:
                    messages.append(last_assistant_message)
                messages.append({"role": "user", "content": user_message})
                await self.push_frame(LLMMessagesFrame(messages))


class CompletenessCheck(FrameProcessor):
    def __init__(self, complete_notifier: BaseNotifier, incomplete_notifier: BaseNotifier):
        super().__init__()
        self._complete_notifier = complete_notifier
        self._incomplete_notifier = incomplete_notifier

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)
        if isinstance(frame, TextFrame) and frame.text == "YES":
            logger.debug("Completeness check YES")
            await self.push_frame(UserStoppedSpeakingFrame())
            await self._complete_notifier.notify()
        elif isinstance(frame, TextFrame) and frame.text == "NO":
            logger.debug("Completeness check NO")
            await self._incomplete_notifier.notify()


class OutputGate(FrameProcessor):
    def __init__(
        self, complete_notifier: BaseNotifier, incomplete_notifier: BaseNotifier, **kwargs
    ):
        super().__init__(**kwargs)
        self._gate_open = False
        self._frames_buffer = []
        self._complete_notifier = complete_notifier
        self._incomplete_notifier = incomplete_notifier

    def close_gate(self):
        self._gate_open = False

    def open_gate(self):
        self._gate_open = True

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

        # We must not block system frames.
        if isinstance(frame, SystemFrame):
            if isinstance(frame, StartFrame):
                await self._start()
            if isinstance(frame, (EndFrame, CancelFrame)):
                await self._stop()
            if isinstance(frame, StartInterruptionFrame):
                self._frames_buffer = []
                self.close_gate()
            await self.push_frame(frame, direction)
            return

        # Ignore frames that are not following the direction of this gate.
        if direction != FrameDirection.DOWNSTREAM:
            await self.push_frame(frame, direction)
            return

        if self._gate_open:
            await self.push_frame(frame, direction)
            return

        self._frames_buffer.append((frame, direction))

    async def _start(self):
        self._frames_buffer = []
        self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
        self._interrupt_task = self.get_event_loop().create_task(self._interrupt_task_handler())

    async def _stop(self):
        self._gate_task.cancel()
        await self._gate_task

    async def _gate_task_handler(self):
        while True:
            try:
                await self._complete_notifier.wait()
                self.open_gate()
                for frame, direction in self._frames_buffer:
                    await self.push_frame(frame, direction)
                self._frames_buffer = []
            except asyncio.CancelledError:
                break

    async def _interrupt_task_handler(self):
        while True:
            try:
                await self._incomplete_notifier.wait()
                await self.push_frame(StartInterruptionFrame(), FrameDirection.UPSTREAM)
                self._frames_buffer = []

            except asyncio.CancelledError:
                break


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

        transport = DailyTransport(
            room_url,
            None,
            "Respond bot",
            DailyParams(
                audio_out_enabled=True,
                vad_enabled=True,
                vad_analyzer=SileroVADAnalyzer(),
                vad_audio_passthrough=True,
            ),
        )

        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        # This is the LLM that will be used to detect if the user has finished a
        # statement. This doesn't really need to be an LLM, we could use NLP
        # libraries for that, but we have the machinery to use an LLM, so we might as well!
        statement_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

        # This is the regular LLM.
        llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")

        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)

        # We have instructed the LLM to return 'YES' if it thinks the user
        # completed a sentence. So, if it's 'YES' we will return true in this
        # predicate which will wake up the notifier.
        async def wake_check_filter(frame):
            logger.debug(f"Completeness check frame: {frame}")
            return frame.text == "YES"

        # This is a notifier that we use to synchronize the two LLMs.
        notifier = EventNotifier()
        # rename/comment?
        interrupt_notifier = EventNotifier()

        # This sends a UserStoppedSpeakingFrame and triggers the notifier event
        completeness_check = CompletenessCheck(
            complete_notifier=notifier, incomplete_notifier=interrupt_notifier
        )

        # # Notify if the user hasn't said anything.
        # async def user_idle_notifier(frame):
        #     await notifier.notify()

        # # Sometimes the LLM will fail detecting if a user has completed a
        # # sentence, this will wake up the notifier if that happens.
        # user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=10.0)

        bot_output_gate = OutputGate(
            complete_notifier=notifier, incomplete_notifier=interrupt_notifier
        )

        async def block_user_stopped_speaking(frame):
            return not isinstance(frame, UserStoppedSpeakingFrame)

        async def pass_only_llm_trigger_frames(frame):
            return isinstance(frame, OpenAILLMContextFrame) or isinstance(frame, LLMMessagesFrame)

        pipeline = Pipeline(
            [
                transport.input(),
                stt,
                # user_idle,
                context_aggregator.user(),
                ParallelPipeline(
                    [
                        # Pass everything except UserStoppedSpeaking to the elements after
                        # this ParallelPipeline
                        FunctionFilter(filter=block_user_stopped_speaking),
                    ],
                    [
                        # Ignore everything except an OpenAILLMContextFrame. Pass a specially constructed
                        # LLMMessagesFrame to the statement classifier LLM. The only frame this
                        # sub-pipeline will output is a UserStoppedSpeakingFrame.
                        StatementJudgeContextFilter(),
                        statement_llm,
                        completeness_check,
                    ],
                    [
                        # Block everything except OpenAILLMContextFrame and LLMMessagesFrame
                        FunctionFilter(filter=pass_only_llm_trigger_frames),
                        llm,
                        tts,
                        bot_output_gate,  # Buffer all llm/tts output until notified.
                    ],
                ),
                transport.output(),
                context_aggregator.assistant(),
            ]
        )

        task = PipelineTask(
            pipeline,
            PipelineParams(
                allow_interruptions=True,
                enable_metrics=True,
                enable_usage_metrics=True,
                report_only_initial_ttfb=True,
            ),
        )

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

        @transport.event_handler("on_app_message")
        async def on_app_message(transport, message, sender):
            logger.debug(f"Received app message: {message} - {sender}")
            if "message" not in message:
                return

            await task.queue_frames(
                [
                    UserStartedSpeakingFrame(),
                    TranscriptionFrame(
                        user_id=sender, timestamp=time.time(), text=message["message"]
                    ),
                    UserStoppedSpeakingFrame(),
                ]
            )

        runner = PipelineRunner()
        await runner.run(task)


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