#
# 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.audio.vad.silero import SileroVADAnalyzer
from pipecat.evals.transport import EvalTransportParams
from pipecat.frames.frames import LLMRunFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.worker import PipelineParams, PipelineWorker, ProcessorUnusablePolicy
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.assemblyai.stt import AssemblyAISTTService
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
from pipecat.workers.runner import WorkerRunner

load_dotenv(override=True)


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


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    """AssemblyAI u3-rt-pro with Built-in Turn Detection

    This example demonstrates using AssemblyAI's u3-rt-pro Speech-to-Text model
    with AssemblyAI's built-in turn detection for more natural conversation flow.

    Key features:

    1. AssemblyAI Turn Detection
       - Set `vad_force_turn_endpoint=False` to use AssemblyAI's built-in turn detection
       - AssemblyAI's model determines when user starts/stops speaking
       - More natural turn detection based on speech patterns and pauses

    2. Advanced Turn Detection Tuning
       - `min_turn_silence`: Minimum silence (ms) when confident about end-of-turn.
         Lower values = faster responses. Default: 100ms
       - `max_turn_silence`: Maximum silence (ms) before forcing end-of-turn.
         Prevents long pauses. Default: 1000ms

    3. Prompt-Based Transcription Enhancement
       - Use `prompt` parameter to improve accuracy for specific names/terms
       - Particularly useful for proper nouns, technical terms, domain vocabulary
       - Example: "Names: Xiomara, Saoirse, Krzystof. Technical terms: API, OAuth."

    4. Speaker Diarization (Optional)
       - Enable with `speaker_labels=True`
       - Automatically identifies different speakers in multi-party conversations
       - TranscriptionFrame includes speaker_id field (e.g., "Speaker A", "Speaker B")

    5. Language Detection (Optional, multilingual model only)
       - Enable with `language_detection=True`
       - Automatically detects spoken language
       - Available with universal-streaming-multilingual model

    For more information: https://www.assemblyai.com/docs/speech-to-text/streaming
    """
    logger.info("Starting bot")

    stt = AssemblyAISTTService(
        api_key=os.environ["ASSEMBLYAI_API_KEY"],
        vad_force_turn_endpoint=False,  # Use AssemblyAI's built-in turn detection
        settings=AssemblyAISTTService.Settings(
            model="u3-rt-pro",
            # Optional: Tune turn detection timing (defaults shown below)
            # min_turn_silence=100,  # Default
            # max_turn_silence=1000,  # Default
            # Optional: Boost accuracy for specific names/terms
            # keyterms_prompt=["Xiomara", "Saoirse", "Krzystof", "API", "OAuth"],
            # Optional: Enable speaker diarization
            # speaker_labels=True,
        ),
    )

    tts = CartesiaTTSService(
        api_key=os.environ["CARTESIA_API_KEY"],
        settings=CartesiaTTSService.Settings(
            voice="86e30c1d-714b-4074-a1f2-1cb6b552fb49",
        ),
    )

    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.",
        ),
    )

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

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

    worker = PipelineWorker(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
        processor_unusable_policy=ProcessorUnusablePolicy.END,
    )

    runner = WorkerRunner(handle_sigint=runner_args.handle_sigint)

    await runner.add_workers(worker)

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info("Client connected")
        # Kick off the conversation.
        context.add_message(
            {"role": "developer", "content": "Please introduce yourself to the user."}
        )
        await worker.queue_frames([LLMRunFrame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info("Client disconnected")
        await runner.cancel()

    await runner.run()


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()