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


import asyncio
import io
import os
import re
import shutil

import aiohttp
from dotenv import load_dotenv
from loguru import logger
from mcp import StdioServerParameters
from mcp.client.session_group import SseServerParameters
from PIL import Image

from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
    Frame,
    FunctionCallResultFrame,
    URLImageRawFrame,
)
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.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.services.daily 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):
        pattern = r"!\[[^\]]*\]\((https?://[^)]+\.(png|jpg|jpeg|PNG|JPG|JPEG|gif))\)"
        match = re.search(pattern, text)
        if match:
            return match.group(1)
        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)


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


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"),
            voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        )

        llm = AnthropicLLMService(
            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
        )

        system = f"""
        You are a helpful LLM in a WebRTC call.
        Your goal is to demonstrate your capabilities in a succinct way.
        You have access to a number of tools provided by NASA MCP. Use any and all tools to help users.
        When asked for today's date, use 'https://www.datetoday.net/'.
        When asked for the astronomy picture of the day, use 'https://www.datetoday.net/', to get today's date.
        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.
        Don't overexplain what you are doing.
        Just respond with short sentences when you are carrying out tool calls.
        """

        messages = [{"role": "system", "content": system}]

        try:
            mcp = MCPClient(
                server_params=StdioServerParameters(
                    command=shutil.which("npx"),
                    args=["-y", "@programcomputer/nasa-mcp-server@latest"],
                    # https://api.nasa.gov
                    env={"NASA_API_KEY": os.getenv("NASA_API_KEY")},
                )
            )
        except Exception as e:
            logger.error(f"error setting up nasa mcp")
            logger.exception("error trace:")
        try:
            # https://docs.mcp.run/integrating/tutorials/mcp-run-sse-openai-agents/
            # ie. "https://www.mcp.run/api/mcp/sse?..."
            # ensure the profile has a tool or few installed
            mcp_run = MCPClient(server_params=SseServerParameters(url=os.getenv("MCP_RUN_SSE_URL")))
        except Exception as e:
            logger.error(f"error setting up mcp.run")
            logger.exception("error trace:")

        tools = await mcp.register_tools(llm)
        run_tools = await mcp_run.register_tools(llm)

        all_standard_tools = run_tools.standard_tools + tools.standard_tools
        all_tools = ToolsSchema(standard_tools=all_standard_tools)

        context = OpenAILLMContext(messages, all_tools)
        context_aggregator = llm.create_context_aggregator(context)
        mcp_image_processor = UrlToImageProcessor(aiohttp_session=session)

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                stt,
                context_aggregator.user(),  # User spoken responses
                llm,  # LLM
                tts,  # TTS
                mcp_image_processor,  # URL image -> output
                transport.output(),  # Transport bot output
                context_aggregator.assistant(),  # 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([context_aggregator.user().get_context_frame()])

        @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()