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

import argparse
import asyncio
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 LLMMessagesFrame
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.filters.stt_mute_filter import (
    STTMuteConfig,
    STTMuteFilter,
    STTMuteFrame,
    STTMuteStrategy,
)
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
from pipecat.transports.services.daily import DailyParams

load_dotenv(override=True)


# 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,
        vad_analyzer=SileroVADAnalyzer(),
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
}


async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
    logger.info(f"Starting bot")

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

    # Configure the mute processor with both strategies
    stt_mute_processor = STTMuteFilter(
        config=STTMuteConfig(
            strategies={
                STTMuteStrategy.FUNCTION_CALL,
                STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE,
            }
        ),
    )

    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")

    async def transfer_to_human(params: FunctionCallParams):
        # Add a delay to test interruption during function calls

        caller_name = params.arguments.get("caller_name", "Unknown")
        human_agent_name = params.arguments.get("human_agent_name", "Unknown")
        logger.info(f"Transfer starting... {caller_name} wants to transfer to {human_agent_name}")
        await task.queue_frame(STTMuteFrame(True))
        await asyncio.sleep(5)  # 5-second delay
        logger.info("Transfer complete, calling result callback")
        messages.clear()
        messages.append(
            {
                "role": "system",
                "content": f"You are now an agent named {human_agent_name}. Greet {caller_name} and let them know you are taking over the conversation.",
            }
        )
        await params.llm.push_frame(LLMMessagesFrame(messages))
        await params.result_callback({"transfer_successful": True})

    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
    llm.register_function("transfer_to_human", transfer_to_human)

    transfer_function = FunctionSchema(
        name="transfer_to_human",
        description="Transfer the conversation to a human agent.",
        properties={
            "caller_name": {
                "type": "string",
                "description": "The name of the person who is calling. This will be used to greet them.",
            },
            "human_agent_name": {
                "type": "string",
                "description": "The name of the human agent to transfer the conversation to.",
            },
        },
        required=["caller_name", "human_agent_name"],
    )
    tools = ToolsSchema(standard_tools=[transfer_function])

    messages = [
        {
            "role": "system",
            "content": "You are a cheerful and helpful assistant named James. It is your job to ask the user their name, and the name of the person they want to transfer the conversation to.",
        },
    ]

    context = OpenAILLMContext(messages, tools)
    context_aggregator = llm.create_context_aggregator(context)

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt_mute_processor,  # Add the mute processor before STT
            stt,  # STT
            context_aggregator.user(),  # User responses
            llm,  # LLM
            tts,  # TTS
            transport.output(),  # Transport bot output
            context_aggregator.assistant(),  # Assistant spoken responses
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Kick off the conversation with a weather-related prompt
        messages.append(
            {
                "role": "system",
                "content": "Ask the user what city they'd like to know the weather for.",
            }
        )
        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=handle_sigint)

    await runner.run(task)


if __name__ == "__main__":
    from pipecat.examples.run import main

    main(run_example, transport_params=transport_params)