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

import asyncio
import os
import sys
from pathlib import Path

import aiohttp
from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame
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.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.google import GoogleLLMService, LLMSearchResponseFrame
from pipecat.transports.services.daily import DailyParams, DailyTransport

sys.path.append(str(Path(__file__).parent.parent))
from runner import configure

load_dotenv(override=True)

logger.remove(0)
logger.add(sys.stderr, level="DEBUG")

# Function handlers for the LLM
search_tool = {"google_search_retrieval": {}}
tools = [search_tool]

system_instruction = """
You are an expert at providing the most recent news from any place. Your responses will be converted to audio, so avoid using special characters or overly complex formatting.

Always use the google search API to retrieve the latest news. You must also use it to check which day is today.

You can:
- Use the Google search API to check the current date.
- Provide the most recent and relevant news from any place by using the google search API.
- Answer any questions the user may have, ensuring your responses are accurate and concise.

Start each interaction by asking the user about which place they would like to know the information.
"""


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

        if isinstance(frame, LLMSearchResponseFrame):
            print(f"LLMSearchLoggerProcessor: {frame}")

        await self.push_frame(frame)


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

        transport = DailyTransport(
            room_url,
            token,
            "Latest news!",
            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="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        )

        # Initialize the Gemini Multimodal Live model
        llm = GoogleLLMService(
            api_key=os.getenv("GOOGLE_API_KEY"),
            system_instruction=system_instruction,
            tools=tools,
            model="gemini-1.5-flash-002",
        )

        context = OpenAILLMContext(
            [
                {
                    "role": "user",
                    "content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
                }
            ],
        )
        context_aggregator = llm.create_context_aggregator(context)

        llm_search_logger = LLMSearchLoggerProcessor()

        pipeline = Pipeline(
            [
                transport.input(),
                stt,
                context_aggregator.user(),
                llm,
                llm_search_logger,
                tts,
                transport.output(),
                context_aggregator.assistant(),
            ]
        )

        task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            await task.queue_frames([context_aggregator.user().get_context_frame()])

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


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