#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
    Frame,
    LLMFullResponseEndFrame,
    LLMFullResponseStartFrame,
    LLMRunFrame,
    TextFrame,
    TTSSpeakFrame,
    UserImageRequestFrame,
)
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import (
    create_transport,
    get_transport_client_id,
    maybe_capture_participant_camera,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.moondream.vision import MoondreamService
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)


async def fetch_user_image(params: FunctionCallParams):
    """Fetch the user image.

    When called, this function pushes a UserImageRequestFrame upstream to the
    transport. As a result, the transport will request the user image and push a
    UserImageRawFrame downstream. The result_callback will be invoked once the
    image is retrieved and processed.
    """
    user_id = params.arguments["user_id"]
    question = params.arguments["question"]
    logger.debug(f"Requesting image with user_id={user_id}, question={question}")

    # Request a user image frame. In this case, we don't want the requested
    # image to be added to the context because we will process it with
    # Moondream. Also associate it to the function call. Pass the result_callback
    # so it can be invoked when the image is actually retrieved.
    await params.llm.push_frame(
        UserImageRequestFrame(
            user_id=user_id,
            text=question,
            append_to_context=False,
            function_name=params.function_name,
            tool_call_id=params.tool_call_id,
            result_callback=params.result_callback,
        ),
        FrameDirection.UPSTREAM,
    )


class MoondreamTextFrameWrapper(FrameProcessor):
    """Wraps Moondream-provided TextFrames with LLM response start/end frames.

    This processor detects TextFrames and automatically wraps them with
    LLMFullResponseStartFrame and LLMFullResponseEndFrame to provide proper
    response boundaries for downstream processors.
    """

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

        # If we receive a TextFrame, wrap it with response start/end frames
        if isinstance(frame, TextFrame):
            await self.push_frame(LLMFullResponseStartFrame(), direction)
            await self.push_frame(frame, direction)
            await self.push_frame(LLMFullResponseEndFrame(), direction)
        else:
            # For all other frames, just pass them through
            await self.push_frame(frame, direction)


# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_in_enabled=True,
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_in_enabled=True,
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot")

    stt = DeepgramSTTService(api_key=os.environ["DEEPGRAM_API_KEY"])

    tts = CartesiaTTSService(
        api_key=os.environ["CARTESIA_API_KEY"],
        settings=CartesiaTTSService.Settings(
            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        ),
    )

    llm = OpenAILLMService(
        api_key=os.environ["OPENAI_API_KEY"],
        settings=OpenAILLMService.Settings(
            system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way. You are able to describe images from the user camera.",
        ),
    )
    llm.register_function("fetch_user_image", fetch_user_image)

    @llm.event_handler("on_function_calls_started")
    async def on_function_calls_started(service, function_calls):
        await tts.queue_frame(TTSSpeakFrame("Let me check on that."))

    fetch_image_function = FunctionSchema(
        name="fetch_user_image",
        description="Called when the user requests a description of their camera feed",
        properties={
            "user_id": {
                "type": "string",
                "description": "The ID of the user to grab the image from",
            },
            "question": {
                "type": "string",
                "description": "The question that the user is asking about the image",
            },
        },
        required=["user_id", "question"],
    )
    tools = ToolsSchema(standard_tools=[fetch_image_function])

    context = LLMContext(tools=tools)
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
    )

    # If you run into weird description, try with use_cpu=True
    moondream = MoondreamService()

    # Wrap TextFrames with LLM response start/end frames, which makes Moondream
    # output be treated like LLM responses for the purpose of context
    # aggregation. Without this, the assistant context aggregator would ignore
    # Moondream output (if the TTS service is disabled).
    moondream_text_wrapper = MoondreamTextFrameWrapper()

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt,  # STT
            user_aggregator,  # User responses
            ParallelPipeline(
                [llm],  # LLM
                [moondream, moondream_text_wrapper],
            ),
            tts,  # TTS
            transport.output(),  # Transport bot output
            assistant_aggregator,  # Assistant spoken responses
        ]
    )

    task = PipelineTask(
        pipeline,
        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}")

        await maybe_capture_participant_camera(transport, client)

        # Set the participant ID in the image requester
        client_id = get_transport_client_id(transport, client)

        # Kick off the conversation.
        context.add_message(
            {
                "role": "developer",
                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
            }
        )
        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()