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

import asyncio
import io
import json
import os
import re
import shutil

import aiohttp
from dotenv import load_dotenv
from loguru import logger
from mcp import StdioServerParameters
from PIL import Image

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
    Frame,
    FunctionCallResultFrame,
    LLMRunFrame,
    URLImageRawFrame,
)
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,
    LLMUserAggregatorParams,
)
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.anthropic.llm import AnthropicLLMService
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.mcp_service import MCPClient
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams

load_dotenv(override=True)


class UrlToImageProcessor(FrameProcessor):
    def __init__(self, aiohttp_session: aiohttp.ClientSession, **kwargs):
        super().__init__(**kwargs)
        self._aiohttp_session = aiohttp_session

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

        if isinstance(frame, FunctionCallResultFrame):
            await self.push_frame(frame, direction)
            image_url = self.extract_url(frame.result)
            if image_url:
                await self.run_image_process(image_url)
                # sometimes we get multiple image urls- process 1 at a time
                await asyncio.sleep(1)
        else:
            await self.push_frame(frame, direction)

    def extract_url(self, text: str):
        try:
            data = json.loads(text)
            if "artObject" in data:
                return data["artObject"]["webImage"]["url"]
            if "artworks" in data and len(data["artworks"]):
                return data["artworks"][0]["webImage"]["url"]
        except (json.JSONDecodeError, KeyError, TypeError):
            pass

        return None

    async def run_image_process(self, image_url: str):
        try:
            logger.debug(f"handling image from url: '{image_url}'")
            async with self._aiohttp_session.get(image_url) as response:
                image_stream = io.BytesIO(await response.content.read())
                image = Image.open(image_stream)
                image = image.convert("RGB")
                frame = URLImageRawFrame(
                    url=image_url, image=image.tobytes(), size=image.size, format="RGB"
                )
                await self.push_frame(frame)
        except Exception as e:
            error_msg = f"Error handling image url {image_url}: {str(e)}"
            logger.error(error_msg)


# full list of tools available from rijksmuseum MCP:
# - get_artwork_details
# - get_artwork_image
# - get_user_sets
# - get_user_set_details
# - open_image_in_browser
# - get_artist_timeline

mcp_tools_filter = ["get_artwork_details", "get_artwork_image", "open_image_in_browser"]


def open_image_output_filter(output: str):
    pattern = r"Successfully opened image in browser: "
    text_to_print = re.sub(pattern, "", output)
    print(f"🖼️ link to high resolution artwork: {text_to_print}")


# 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_out_enabled=True,
        video_out_width=1024,
        video_out_height=1024,
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_out_enabled=True,
        video_out_width=1024,
        video_out_height=1024,
    ),
}


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

    # Create an HTTP session for API calls
    async with aiohttp.ClientSession() as session:
        stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

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

        system_prompt = f"""
        You are a helpful LLM in a voice call.
        Your goal is to demonstrate your capabilities in a succinct way.
        You have access to tools to search the Rijksmuseum collection.
        Offer, for example, to show a floral still life, use the `search_artwork` tool.
        The tool may respond with a JSON object with an `artworks` array. Choose the art from that array.
        Once the tool has responded, tell the user the title and use the `open_image_in_browser` tool.
        Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points.
        Respond to what the user said in a creative and helpful way.
        Don't overexplain what you are doing.
        Just respond with short sentences when you are carrying out tool calls.
        """

        llm = AnthropicLLMService(
            api_key=os.getenv("ANTHROPIC_API_KEY"),
            settings=AnthropicLLMService.Settings(
                system_instruction=system_prompt,
            ),
        )

        try:
            mcp = MCPClient(
                server_params=StdioServerParameters(
                    command=shutil.which("npx"),
                    # https://github.com/r-huijts/rijksmuseum-mcp
                    args=["-y", "mcp-server-rijksmuseum"],
                    env={"RIJKSMUSEUM_API_KEY": os.getenv("RIJKSMUSEUM_API_KEY")},
                ),
                # Optional
                tools_filter=mcp_tools_filter,  # Optional
                tools_output_filters={"open_image_in_browser": open_image_output_filter},
            )
        except Exception as e:
            logger.error(f"error setting up mcp")
            logger.exception("error trace:")

        mcp_image = UrlToImageProcessor(aiohttp_session=session)

        tools = {}
        try:
            tools = await mcp.register_tools(llm)
        except Exception as e:
            logger.error(f"error registering tools")
            logger.exception("error trace:")

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

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                stt,
                user_aggregator,  # User spoken responses
                llm,  # LLM
                tts,  # TTS
                mcp_image,  # URL image -> output
                transport.output(),  # Transport bot output
                assistant_aggregator,  # Assistant spoken responses and tool context
            ]
        )

        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.
            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__":
    if not os.getenv("RIJKSMUSEUM_API_KEY"):
        logger.error(
            f"Please set RIJKSMUSEUM_API_KEY environment variable for this example. See https://github.com/r-huijts/rijksmuseum-mcp and https://www.rijksmuseum.nl/en/register?redirectUrl=https://www.https://www.rijksmuseum.nl/en/rijksstudio/my/profile"
        )
        import sys

        sys.exit(1)
    from pipecat.runner.run import main

    main()
