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

"""Audio Recording Example with Pipecat.

This example demonstrates how to record audio from a conversation between a user and an AI assistant,
saving both merged and individual audio tracks. It showcases the AudioBufferProcessor's capabilities
to handle both combined and separate audio streams.

The example:
    1. Sets up a basic conversation with an AI assistant
    2. Records the entire conversation
    3. Saves three separate WAV files:
        - A merged recording of both participants
        - Individual recording of user audio
        - Individual recording of assistant audio

Example usage (run from pipecat root directory):
    $ pip install "pipecat-ai[daily,openai,cartesia,silero]"
    $ pip install -r dev-requirements.txt
    $ python examples/foundational/34-audio-recording.py

Requirements:
    - OpenAI API key (for GPT-4)
    - Cartesia API key (for text-to-speech)
    - Daily API key (for video/audio transport)

    Environment variables (.env file):
        OPENAI_API_KEY=your_openai_key
        CARTESIA_API_KEY=your_cartesia_key
        DAILY_API_KEY=your_daily_key
        DEEPGRAM_API_KEY=your_deepgram_key

The recordings will be saved in a 'recordings' directory with timestamps:
    recordings/
        merged_20240315_123456.wav  (Combined audio)
        user_20240315_123456.wav    (User audio only)
        bot_20240315_123456.wav     (Bot audio only)

Note:
    This example requires the AudioBufferProcessor with track-specific audio support,
    which provides both 'on_audio_data' and 'on_track_audio_data' events for
    handling merged and separate audio tracks respectively.
"""

import argparse
import datetime
import io
import os
import wave

import aiofiles
from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
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.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection

load_dotenv(override=True)


async def save_audio_file(audio: bytes, filename: str, sample_rate: int, num_channels: int):
    """Save audio data to a WAV file."""
    if len(audio) > 0:
        with io.BytesIO() as buffer:
            with wave.open(buffer, "wb") as wf:
                wf.setsampwidth(2)
                wf.setnchannels(num_channels)
                wf.setframerate(sample_rate)
                wf.writeframes(audio)
            async with aiofiles.open(filename, "wb") as file:
                await file.write(buffer.getvalue())
        logger.info(f"Audio saved to {filename}")


async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
    logger.info(f"Starting bot")

    transport = SmallWebRTCTransport(
        webrtc_connection=webrtc_connection,
        params=TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            vad_analyzer=SileroVADAnalyzer(),
        ),
    )

    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True)

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
    )

    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4")

    # Create audio buffer processor
    audiobuffer = AudioBufferProcessor()

    messages = [
        {
            "role": "system",
            "content": "You are a helpful assistant demonstrating audio recording capabilities. Keep your responses brief and clear.",
        },
    ]

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

    pipeline = Pipeline(
        [
            transport.input(),
            stt,
            context_aggregator.user(),
            llm,
            tts,
            transport.output(),
            audiobuffer,  # Add audio buffer to pipeline
            context_aggregator.assistant(),
        ]
    )

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

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Start recording audio
        await audiobuffer.start_recording()
        # Start conversation - empty prompt to let LLM follow system instructions
        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")

    @transport.event_handler("on_client_closed")
    async def on_client_closed(transport, client):
        logger.info(f"Client closed connection")
        await task.cancel()

    # Handler for merged audio
    @audiobuffer.event_handler("on_audio_data")
    async def on_audio_data(buffer, audio, sample_rate, num_channels):
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = f"recordings/merged_{timestamp}.wav"
        os.makedirs("recordings", exist_ok=True)
        await save_audio_file(audio, filename, sample_rate, num_channels)

    # Handler for separate tracks
    @audiobuffer.event_handler("on_track_audio_data")
    async def on_track_audio_data(buffer, user_audio, bot_audio, sample_rate, num_channels):
        timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        os.makedirs("recordings", exist_ok=True)

        # Save user audio
        user_filename = f"recordings/user_{timestamp}.wav"
        await save_audio_file(user_audio, user_filename, sample_rate, 1)

        # Save bot audio
        bot_filename = f"recordings/bot_{timestamp}.wav"
        await save_audio_file(bot_audio, bot_filename, sample_rate, 1)

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


if __name__ == "__main__":
    from run import main

    main()