blurt/blurt.py

import base64
import io
import logging
import os
import random
import re
import shlex
import signal
import subprocess
import sys
import tempfile
import threading
from dataclasses import dataclass
from logging.handlers import RotatingFileHandler
from math import gcd
from pathlib import Path
from typing import Callable

import numpy as np
import keyring
import pyperclip
import pystray
import requests
import sounddevice as sd
from PIL import Image, ImageDraw
from scipy import signal as dsp
from scipy.io import wavfile

OPENAI_TRANSCRIPTIONS_URL = "https://api.openai.com/v1/audio/transcriptions"
OPENROUTER_TRANSCRIPTIONS_URL = "https://openrouter.ai/api/v1/audio/transcriptions"
OPENROUTER_APP_URL = "https://git.felixm.de/felixm/blurt"
OPENROUTER_APP_TITLE = "Blurt"
DEFAULT_TRANSCRIPTION_MODEL = "whisper-1"
DEFAULT_TRANSCRIPTION_LANGUAGE = "en"
TRANSCRIPTION_MODELS = [
    ("whisper-1", "OpenAI: whisper-1", "openai"),
    ("gpt-4o-mini-transcribe", "OpenAI: gpt-4o-mini-transcribe", "openai"),
    (
        "gpt-4o-mini-transcribe-2025-12-15",
        "OpenAI: gpt-4o-mini-transcribe (2025-12-15)",
        "openai",
    ),
    ("gpt-4o-transcribe", "OpenAI: gpt-4o-transcribe", "openai"),
    ("gpt-4o-transcribe-diarize", "OpenAI: gpt-4o-transcribe-diarize", "openai"),
    (
        "openai/whisper-large-v3-turbo",
        "OpenRouter: whisper-large-v3-turbo (fast)",
        "openrouter",
    ),
    (
        "openai/whisper-large-v3",
        "OpenRouter: whisper-large-v3 (accurate)",
        "openrouter",
    ),
    (
        "openai/gpt-4o-mini-transcribe",
        "OpenRouter: gpt-4o-mini-transcribe",
        "openrouter",
    ),
    (
        "openai/gpt-4o-transcribe",
        "OpenRouter: gpt-4o-transcribe",
        "openrouter",
    ),
]
PROVIDER_KEY_HINTS = {
    "openai": ("set OPENAI_API_KEY or configure keyring/Credential Manager entry blurt/openai-api-key"),
    "openrouter": (
        "set OPENROUTER_API_KEY or configure Windows Credential Manager entry "
        "blurt/openrouter-api-key"
    ),
}
TRANSCRIPTION_LANGUAGES = [
    ("en", "English"),
    ("de", "German"),
]
SAMPLE_RATE = 16000
CHANNELS = 1
DTYPE = "float32"  # capture with headroom; the WAV sent to the API is int16
WARMUP_SECONDS = 0.15  # drop the click many mics emit when the stream opens
HIGHPASS_HZ = 70.0  # strip DC bias and rumble (raw digital mics carry a lot)
TARGET_RMS_DBFS = -20.0
PEAK_CEILING_DBFS = -1.0
MAX_GAIN_DB = 30.0
SILENT_RMS_DBFS = -65.0
# Playback endpoints that JACK re-exposes as inputs, and raw ALSA `hw:` devices
# that a sound server already owns — offering either in the menu only invites a
# hang or a silent recording.
OUTPUT_ENDPOINT_HINTS = ("HDMI", "DisplayPort", "Headphones")
RAW_ALSA_DEVICE = re.compile(r"\(hw:\d+,\d+\)")
SOUND_SERVER_DEVICES = ("pipewire", "pulse", "default")
APP_NAME = "Blurt"
APP_DIR = Path(__file__).resolve().parent
AGENT_PREFIX = "to my agent"
PID_FILE = Path(tempfile.gettempdir()) / "blurt.pid"
LOG_FILE = Path(tempfile.gettempdir()) / "blurt.log"
KEYRING_CREDENTIALS = [
    ("openai-api-key", "felixm"),
    ("blurt", "openai-api-key"),
]
OPENROUTER_KEYRING_CREDENTIALS = [
    ("openrouter-api-key", "felixm"),
    ("blurt", "openrouter-api-key"),
]


def setup_logging():
    logger = logging.getLogger()
    if logger.handlers:
        return

    logger.setLevel(logging.INFO)
    handler = RotatingFileHandler(
        LOG_FILE,
        maxBytes=512 * 1024,
        backupCount=3,
        encoding="utf-8",
    )
    handler.setFormatter(
        logging.Formatter("%(asctime)s %(levelname)s [%(threadName)s] %(message)s")
    )
    logger.addHandler(handler)

    def log_thread_exception(args):
        logging.critical(
            "Uncaught thread exception in %s",
            args.thread.name if args.thread else "<unknown>",
            exc_info=(args.exc_type, args.exc_value, args.exc_traceback),
        )

    threading.excepthook = log_thread_exception


def load_config_value(env_name: str, file_name: str, default: str = "") -> str:
    env_value = os.environ.get(env_name, "").strip()
    if env_value:
        logging.info("Loaded %s from environment", env_name)
        return env_value

    config_file = APP_DIR / file_name
    try:
        value = config_file.read_text().strip()
        logging.info(
            "Loaded %s from %s: %s",
            env_name,
            config_file,
            "present" if value else "empty",
        )
        return value
    except FileNotFoundError:
        if default:
            logging.info("Config file not found, using default for %s: %s", env_name, default)
        else:
            logging.warning("Config file not found for %s: %s", env_name, config_file)
        return default


def _load_key_from_keyring(credentials: list[tuple[str, str]], label: str) -> str:
    for service_name, username in credentials:
        try:
            stored_key = keyring.get_password(service_name, username)
        except Exception:
            logging.exception(
                "Could not read %s key from keyring entry %s/%s",
                label,
                service_name,
                username,
            )
            continue
        if stored_key:
            logging.info("Loaded %s key from keyring entry %s/%s", label, service_name, username)
            return stored_key.strip()
    return ""


def load_api_key() -> str:
    env_key = os.environ.get("OPENAI_API_KEY", "").strip()
    if env_key:
        logging.info("Loaded OPENAI_API_KEY from environment")
        return env_key

    key = _load_key_from_keyring(KEYRING_CREDENTIALS, "OpenAI")
    if key:
        return key

    logging.warning("No OpenAI API key found in OPENAI_API_KEY or configured keyring entries")
    return ""


def load_openrouter_key() -> str:
    env_key = os.environ.get("OPENROUTER_API_KEY", "").strip()
    if env_key:
        logging.info("Loaded OPENROUTER_API_KEY from environment")
        return env_key

    key = _load_key_from_keyring(OPENROUTER_KEYRING_CREDENTIALS, "OpenRouter")
    if key:
        return key

    logging.warning(
        "No OpenRouter API key found in OPENROUTER_API_KEY or configured keyring entries"
    )
    return ""


def load_transcription_model() -> str:
    model = load_config_value(
        "BLURT_TRANSCRIPTION_MODEL",
        "model.txt",
        DEFAULT_TRANSCRIPTION_MODEL,
    )
    valid_models = {model_id for model_id, _, _ in TRANSCRIPTION_MODELS}
    if model not in valid_models:
        logging.warning("Unknown transcription model %s, using %s", model, DEFAULT_TRANSCRIPTION_MODEL)
        return DEFAULT_TRANSCRIPTION_MODEL
    return model


def save_transcription_model(model: str):
    model_file = APP_DIR / "model.txt"
    model_file.write_text(f"{model}\n")
    logging.info("Saved transcription model to %s: %s", model_file, model)


def load_transcription_language() -> str:
    language = load_config_value(
        "BLURT_TRANSCRIPTION_LANGUAGE",
        "language.txt",
        DEFAULT_TRANSCRIPTION_LANGUAGE,
    )
    valid_languages = {language_id for language_id, _ in TRANSCRIPTION_LANGUAGES}
    if language not in valid_languages:
        logging.warning(
            "Unknown transcription language %s, using %s",
            language,
            DEFAULT_TRANSCRIPTION_LANGUAGE,
        )
        return DEFAULT_TRANSCRIPTION_LANGUAGE
    return language


def save_transcription_language(language: str):
    language_file = APP_DIR / "language.txt"
    language_file.write_text(f"{language}\n")
    logging.info("Saved transcription language to %s: %s", language_file, language)


def load_input_device_name() -> str:
    return load_config_value("BLURT_INPUT_DEVICE", "device.txt")


def save_input_device_name(name: str):
    device_file = APP_DIR / "device.txt"
    device_file.write_text(f"{name}\n")
    logging.info("Saved input device to %s: %s", device_file, name or "<system default>")


def audio_stats(samples: np.ndarray) -> dict:
    """Level report for a float32 mono clip, in the units that explain bad audio."""
    if len(samples) == 0:
        return {"rms_dbfs": -120.0, "peak_dbfs": -120.0, "dc": 0.0, "clipped_pct": 0.0}
    dc = float(samples.mean())
    centered = samples - dc
    rms = float(np.sqrt(np.mean(np.square(centered))))
    peak = float(np.abs(samples).max())
    return {
        "rms_dbfs": 20 * np.log10(max(rms, 1e-6)),
        "peak_dbfs": 20 * np.log10(max(peak, 1e-6)),
        "dc": dc,
        "clipped_pct": float((np.abs(samples) >= 0.999).mean() * 100),
    }


def input_stream_settings(device) -> tuple[int, int]:
    """Rate and channel count the device will actually open with.

    Only the resampling backends (`pipewire`, `default`) accept 16 kHz mono:
    raw ALSA `hw:` devices and PipeWire's JACK endpoints run at their own rate,
    so we capture natively and resample ourselves."""
    info = sd.query_devices(device, "input")
    native_rate = int(info["default_samplerate"])
    max_channels = max(int(info["max_input_channels"]), CHANNELS)
    for rate in dict.fromkeys((SAMPLE_RATE, native_rate)):
        for channels in dict.fromkeys((CHANNELS, max_channels)):
            try:
                sd.check_input_settings(
                    device=device, samplerate=rate, channels=channels, dtype=DTYPE
                )
            except Exception:
                continue
            return rate, channels
    logging.warning(
        "No probed capture format worked for %s; trying native %d Hz / %d ch",
        info["name"],
        native_rate,
        max_channels,
    )
    return native_rate, max_channels


def resample_to_target(samples: np.ndarray, rate: int) -> np.ndarray:
    if rate == SAMPLE_RATE:
        return samples
    divisor = gcd(rate, SAMPLE_RATE)
    resampled = dsp.resample_poly(samples, SAMPLE_RATE // divisor, rate // divisor)
    logging.info("Resampled %d Hz -> %d Hz", rate, SAMPLE_RATE)
    return resampled.astype(np.float32)


def without_warmup(samples: np.ndarray) -> np.ndarray:
    """The clip minus the stream-open click, which otherwise skews every stat."""
    warmup = int(WARMUP_SECONDS * SAMPLE_RATE)
    return samples[warmup:] if len(samples) > warmup * 2 else samples


def speech_level_dbfs(samples: np.ndarray) -> float:
    """Level of the loudest tenth of the clip: the part that should be speech.
    Robust to the click at stream start and to long pauses."""
    frame = SAMPLE_RATE // 50
    if len(samples) < frame * 2:
        return -120.0
    centered = samples - samples.mean()
    usable = centered[: len(centered) // frame * frame].reshape(-1, frame)
    energy = np.sqrt(np.mean(np.square(usable), axis=1))
    return 20 * np.log10(max(float(np.percentile(energy, 90)), 1e-6))


def condition_audio(samples: np.ndarray) -> np.ndarray:
    """Make raw capture look like what a Windows mic driver would have handed us:
    no start-up click, no DC bias, speech at a predictable level."""
    trimmed = len(samples)
    samples = without_warmup(samples)
    trimmed -= len(samples)
    sos = dsp.butter(2, HIGHPASS_HZ, btype="highpass", fs=SAMPLE_RATE, output="sos")
    # Seed the filter with the first sample, or the DC step at t=0 rings for
    # ~100 ms and lands right on top of the first word.
    zi = dsp.sosfilt_zi(sos) * (samples[0] if len(samples) else 0.0)
    filtered = dsp.sosfilt(sos, samples, zi=zi)[0].astype(np.float32)

    rms = float(np.sqrt(np.mean(np.square(filtered)))) if len(filtered) else 0.0
    # 99.9th percentile, not the max: one keyboard clack must not hold the
    # whole clip 20 dB down. Stray peaks above the ceiling get clipped below.
    peak = float(np.percentile(np.abs(filtered), 99.9)) if len(filtered) else 0.0
    if rms > 0 and peak > 0:
        gain = min(
            10 ** ((TARGET_RMS_DBFS - 20 * np.log10(rms)) / 20),
            10 ** ((PEAK_CEILING_DBFS - 20 * np.log10(peak)) / 20),
            10 ** (MAX_GAIN_DB / 20),
        )
    else:
        gain = 1.0
    logging.info(
        "Conditioned audio: trimmed=%d samples highpass=%gHz gain=%+.1f dB",
        trimmed,
        HIGHPASS_HZ,
        20 * np.log10(gain),
    )
    return np.clip(filtered * gain, -1.0, 1.0)


def get_model_label(model: str) -> str:
    for model_id, label, _ in TRANSCRIPTION_MODELS:
        if model_id == model:
            return label
    return model


def get_language_label(language: str) -> str:
    for language_id, label in TRANSCRIPTION_LANGUAGES:
        if language_id == language:
            return label
    return language


@dataclass
class Platform:
    notify: Callable[[str, str], None]
    dispatch: Callable[[str], None]
    open_logs: Callable[[], None]
    setup_toggle_trigger: Callable[["BlurtApp"], None]
    cleanup_toggle_trigger: Callable[[], None]


def _make_windows_platform() -> Platform:
    from pynput import keyboard
    from winotify import Notification

    listener = None

    def notify(title: str, message: str):
        try:
            toast = Notification(app_id=APP_NAME, title=title, msg=message)
            toast.show()
        except Exception:
            logging.exception("Windows notification failed")

    def dispatch(task: str):
        wezterm = r"C:\Program Files\WezTerm\wezterm-gui.exe"
        dispatch_sh = "/mnt/c/Users/felix.martin/AppData/Roaming/FlowLauncher/Plugins/Dispatch-1.0.0/dispatch.sh"
        safe_query = shlex.quote(task)
        cmd = [
            wezterm,
            "-e", "wsl.exe",
            "-d", "Ubuntu",
            "-e", "zsh",
            "-c", f"{dispatch_sh} {safe_query}",
        ]
        logging.info("Dispatching agent task with %d characters", len(task))
        subprocess.Popen(cmd, creationflags=subprocess.DETACHED_PROCESS)

    def open_logs():
        subprocess.Popen(["notepad.exe", str(LOG_FILE)])

    def setup_toggle_trigger(app: "BlurtApp"):
        nonlocal listener

        def on_press(key):
            if key == keyboard.Key.f20:
                app.toggle_recording()

        listener = keyboard.Listener(on_press=on_press)
        listener.start()
        logging.info("Started Windows F20 hotkey listener")

    def cleanup_toggle_trigger():
        nonlocal listener
        if listener:
            listener.stop()

    return Platform(
        notify=notify,
        dispatch=dispatch,
        open_logs=open_logs,
        setup_toggle_trigger=setup_toggle_trigger,
        cleanup_toggle_trigger=cleanup_toggle_trigger,
    )


def _make_linux_platform() -> Platform:
    prev_handler = None

    def notify(title: str, message: str):
        try:
            subprocess.Popen(
                ["notify-send", APP_NAME, f"{title}: {message}"],
                stdout=subprocess.DEVNULL,
                stderr=subprocess.DEVNULL,
            )
        except Exception:
            logging.exception("Linux notification failed")

    def dispatch(task: str):
        dispatch_sh = Path.home() / ".local" / "bin" / "dispatch.sh"
        safe_query = shlex.quote(task)
        cmd = ["bash", str(dispatch_sh), safe_query]
        logging.info("Dispatching agent task with %d characters", len(task))
        subprocess.Popen(
            cmd,
            start_new_session=True,
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL,
        )

    def open_logs():
        subprocess.Popen(
            ["xdg-open", str(LOG_FILE)],
            start_new_session=True,
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL,
        )

    def setup_toggle_trigger(app: "BlurtApp"):
        nonlocal prev_handler
        PID_FILE.write_text(str(os.getpid()))
        logging.info("Wrote toggle PID file: %s", PID_FILE)

        def on_sigusr1(signum, frame):
            threading.Thread(target=app.toggle_recording, daemon=True).start()

        prev_handler = signal.signal(signal.SIGUSR1, on_sigusr1)

    def cleanup_toggle_trigger():
        nonlocal prev_handler
        if prev_handler is not None:
            signal.signal(signal.SIGUSR1, prev_handler)
        PID_FILE.unlink(missing_ok=True)

    return Platform(
        notify=notify,
        dispatch=dispatch,
        open_logs=open_logs,
        setup_toggle_trigger=setup_toggle_trigger,
        cleanup_toggle_trigger=cleanup_toggle_trigger,
    )


def get_platform() -> Platform:
    if sys.platform == "win32":
        return _make_windows_platform()
    return _make_linux_platform()


class AwakeKeeper:
    """Periodically clicks the Windows Start button area to prevent PC lock."""

    def __init__(self):
        from pynput.mouse import Controller, Button

        self._mouse = Controller()
        self._button = Button.left
        self._stop_event = threading.Event()
        self._thread: threading.Thread | None = None

    @property
    def is_active(self) -> bool:
        return self._thread is not None and self._thread.is_alive()

    def start(self):
        if self.is_active:
            return
        self._stop_event.clear()
        self._thread = threading.Thread(
            target=self._run, daemon=True, name="AwakeKeeper"
        )
        self._thread.start()
        logging.info("AwakeKeeper started")

    def stop(self):
        self._stop_event.set()
        self._thread = None
        logging.info("AwakeKeeper stopped")

    def _run(self):
        while not self._stop_event.wait(timeout=random.uniform(60, 120)):
            x = random.randint(5, 20)
            y = random.randint(5, 20)
            self._mouse.position = (x, y)
            self._mouse.click(self._button)
            logging.debug("AwakeKeeper clicked at (%d, %d)", x, y)


def send_toggle():
    """Send toggle signal to running blurt instance (Linux only)."""
    if sys.platform == "win32":
        print("'blurt toggle' is not supported on Windows.", file=sys.stderr)
        sys.exit(1)
    try:
        pid = int(PID_FILE.read_text().strip())
        logging.info("Sending toggle signal to PID %s", pid)
        os.kill(pid, signal.SIGUSR1)
    except (FileNotFoundError, ValueError, ProcessLookupError):
        print("No running blurt instance found.", file=sys.stderr)
        sys.exit(1)


class BlurtApp:
    def __init__(self, platform: Platform):
        self.platform = platform
        self.recording = False
        self.audio_frames: list[np.ndarray] = []
        self.stream: sd.InputStream | None = None
        self.capture_rate = SAMPLE_RATE
        self.capture_channels = CHANNELS
        self.lock = threading.Lock()
        self.icon: pystray.Icon | None = None
        self.input_devices = self._get_input_devices()
        self.device_name = load_input_device_name()
        self.device_index = self._resolve_device(self.device_name)
        self.api_keys = {
            "openai": load_api_key(),
            "openrouter": load_openrouter_key(),
        }
        self.transcription_model = self._available_model_or_fallback(
            load_transcription_model()
        )
        self.transcription_language = load_transcription_language()
        self.session = requests.Session()
        self._awake_keeper: AwakeKeeper | None = (
            AwakeKeeper() if sys.platform == "win32" else None
        )
        self.pending_transcriptions = 0
        logging.info(
            "BlurtApp initialized with %d input devices, model=%s, language=%s, awake_available=%s",
            len(self.input_devices),
            self.transcription_model,
            self.transcription_language,
            self._awake_keeper is not None,
        )

    def _get_input_devices(self) -> list[dict]:
        devices = sd.query_devices()
        self.all_input_devices = [
            {"index": i, "name": d["name"]}
            for i, d in enumerate(devices)
            if d["max_input_channels"] > 0
        ]
        input_devices = []
        server_present = any(
            dev["name"] in SOUND_SERVER_DEVICES for dev in self.all_input_devices
        )
        for dev in self.all_input_devices:
            if any(hint in dev["name"] for hint in OUTPUT_ENDPOINT_HINTS):
                logging.info("Skipping output endpoint %s: %s", dev["index"], dev["name"])
                continue
            if server_present and RAW_ALSA_DEVICE.search(dev["name"]):
                logging.info("Skipping raw ALSA device %s: %s", dev["index"], dev["name"])
                continue
            logging.info("Input device %s: %s", dev["index"], dev["name"])
            input_devices.append(dev)
        return input_devices

    def _resolve_device(self, name: str) -> int | None:
        """Devices are addressed by name, not index: PortAudio indices shift
        whenever a USB mic or dock comes and goes."""
        if not name:
            logging.info("No input device configured; recording from the system default")
            return None
        for dev in self.all_input_devices:
            if dev["name"] == name:
                logging.info("Using configured input device %s: %s", dev["index"], name)
                return dev["index"]
        logging.warning("Configured input device %r is not connected; using system default", name)
        return None

    def _create_icon_image(self, color: tuple[int, int, int]) -> Image.Image:
        img = Image.new("RGBA", (64, 64), (0, 0, 0, 0))
        draw = ImageDraw.Draw(img)
        draw.ellipse([8, 8, 56, 56], fill=color)
        return img

    def _update_icon(self):
        if self.icon:
            if self.recording:
                color = (220, 40, 40)  # red
                state = "Recording..."
            elif self.pending_transcriptions > 0:
                color = (40, 100, 220)  # blue
                state = "Transcribing..."
            else:
                color = (120, 120, 120)  # gray
                state = "Ready"
            self.icon.icon = self._create_icon_image(color)
            self.icon.title = f"{APP_NAME} - {state}"
            self.icon.update_menu()

    def _audio_callback(self, indata, frames, time_info, status):
        if status:
            logging.warning("sounddevice input status: %s", status)
        self.audio_frames.append(indata.copy())

    def _provider_for_model(self, model: str) -> str:
        for model_id, _, provider in TRANSCRIPTION_MODELS:
            if model_id == model:
                return provider
        return "openai"

    def _available_model_or_fallback(self, model: str) -> str:
        provider = self._provider_for_model(model)
        if self.api_keys.get(provider):
            return model

        for fallback_model, _, fallback_provider in TRANSCRIPTION_MODELS:
            if self.api_keys.get(fallback_provider):
                logging.warning(
                    "Model %s requires a %s API key, which is unavailable; "
                    "using available model %s for this session",
                    model,
                    provider,
                    fallback_model,
                )
                return fallback_model

        logging.error(
            "Model %s requires a %s API key, but no API keys are available for any provider",
            model,
            provider,
        )
        return model

    def _current_api_key(self) -> str:
        return self.api_keys.get(self._provider_for_model(self.transcription_model), "")

    def _api_key_label(self) -> str:
        provider = self._provider_for_model(self.transcription_model)
        provider_label = "OpenRouter" if provider == "openrouter" else "OpenAI"
        key = self._current_api_key()
        if not key:
            return f"API key: {provider_label} (missing)"
        return f"API key: {provider_label} (…{key[-4:]})"

    def _start_recording(self):
        if not self._current_api_key():
            provider = self._provider_for_model(self.transcription_model)
            key_hint = PROVIDER_KEY_HINTS.get(provider, f"set the {provider} API key")
            logging.error(
                "Recording blocked: model=%s provider=%s has no API key; "
                "%s or select a model with an available key",
                self.transcription_model,
                provider,
                key_hint,
            )
            self.platform.notify("Error", f"No {provider} API key; {key_hint}.")
            return
        self.audio_frames = []
        try:
            self.capture_rate, self.capture_channels = input_stream_settings(self.device_index)
            self.stream = sd.InputStream(
                device=self.device_index,
                samplerate=self.capture_rate,
                channels=self.capture_channels,
                dtype=DTYPE,
                callback=self._audio_callback,
            )
            self.stream.start()
            logging.info(
                "Started recording: device_index=%s name=%s rate=%d channels=%d",
                self.device_index,
                self.device_name or "<system default>",
                self.capture_rate,
                self.capture_channels,
            )
        except Exception as e:
            logging.exception(
                "Could not start recording on device_index=%s name=%s",
                self.device_index,
                self.device_name or "<system default>",
            )
            self.platform.notify("Error", f"Could not start recording: {e}")
            return
        self.recording = True
        self._update_icon()
        self.platform.notify("Recording", "Press hotkey or click tray to stop.")

    def _stop_recording(self) -> np.ndarray | None:
        if self.stream:
            self.stream.stop()
            self.stream.close()
            self.stream = None
        self.recording = False
        self._update_icon()
        if not self.audio_frames:
            logging.warning("Stopped recording with no audio frames")
            return None
        frames = np.concatenate(self.audio_frames, axis=0)
        self.audio_frames = []
        audio_data = frames.mean(axis=1) if frames.shape[1] > 1 else frames[:, 0]
        audio_data = resample_to_target(audio_data, self.capture_rate)
        raw = audio_stats(without_warmup(audio_data))
        speech_level = speech_level_dbfs(without_warmup(audio_data))
        logging.info(
            "Stopped recording: samples=%d (%.1fs) speech=%.1f dBFS rms=%.1f dBFS "
            "peak=%.1f dBFS dc=%+.4f clipped=%.3f%% device=%s",
            len(audio_data),
            len(audio_data) / SAMPLE_RATE,
            speech_level,
            raw["rms_dbfs"],
            raw["peak_dbfs"],
            raw["dc"],
            raw["clipped_pct"],
            self.device_name or "<system default>",
        )
        if speech_level < SILENT_RMS_DBFS:
            logging.error(
                "Capture is essentially silent (speech level %.1f dBFS) on device %s; "
                "not sending it for transcription",
                speech_level,
                self.device_name or "<system default>",
            )
            return None
        if raw["clipped_pct"] > 1.0:
            logging.warning(
                "Capture clips on %.2f%% of samples; lower the input gain for %s",
                raw["clipped_pct"],
                self.device_name or "<system default>",
            )

        conditioned = condition_audio(audio_data)
        clean = audio_stats(conditioned)
        logging.info(
            "Conditioned capture: rms=%.1f dBFS peak=%.1f dBFS dc=%+.4f",
            clean["rms_dbfs"],
            clean["peak_dbfs"],
            clean["dc"],
        )
        return (conditioned * 32767).astype(np.int16)

    def _transcribe(self, audio_data: np.ndarray):
        self.platform.notify(
            "Transcribing",
            f"Using {get_model_label(self.transcription_model)} ({self.transcription_language})",
        )
        logging.info("Starting transcription: samples=%d", len(audio_data))

        buffer = io.BytesIO()
        wavfile.write(buffer, SAMPLE_RATE, audio_data)
        wav_bytes = buffer.getvalue()
        logging.info("Encoded WAV in memory: %d bytes", len(wav_bytes))

        try:
            if self._provider_for_model(self.transcription_model) == "openrouter":
                result = self._transcribe_openrouter(wav_bytes)
            else:
                result = self._transcribe_openai(wav_bytes)
            text = result.get("text", "").lstrip()
            logging.info("Transcription text length: %d", len(text))
            if "usage" in result:
                logging.info("Transcription usage: %s", result["usage"])

            if not text:
                error_msg = result.get("error", {}).get("message", "Unknown error")
                logging.error("Transcription response did not include text: %s", result)
                self.platform.notify("Error", f"Transcription failed: {error_msg}")
                return

            if text.lower().startswith(AGENT_PREFIX):
                task = text[len(AGENT_PREFIX):].lstrip(" ,.:;-")
                if task:
                    pyperclip.copy(task)
                    try:
                        self.platform.dispatch(task)
                    except Exception as e:
                        self.platform.notify("Dispatch Error", str(e))
                    preview = task[:50] + ("..." if len(task) > 50 else "")
                    self.platform.notify("Dispatched", preview)
                    logging.info("Dispatched transcription task with %d characters", len(task))
                else:
                    pyperclip.copy(text)
                    self.platform.notify("Error", f"No task after '{AGENT_PREFIX}'")
                    logging.warning("Agent prefix detected with no task")
            else:
                pyperclip.copy(text)
                preview = text[:50] + ("..." if len(text) > 50 else "")
                self.platform.notify("Copied", preview)
                logging.info("Copied transcription to clipboard")

        except requests.RequestException as e:
            logging.exception("API request failed")
            self.platform.notify("Error", f"API request failed: {e}")
        except Exception as e:
            logging.exception("Unexpected transcription failure")
            self.platform.notify("Error", f"Unexpected transcription failure: {e}")
        finally:
            self.pending_transcriptions -= 1
            self._update_icon()

    def _transcribe_openai(self, wav_bytes: bytes) -> dict:
        data = {
            "model": self.transcription_model,
            "language": self.transcription_language,
        }
        if self.transcription_model == "gpt-4o-transcribe-diarize":
            data.update(response_format="diarized_json", chunking_strategy="auto")

        response = self.session.post(
            OPENAI_TRANSCRIPTIONS_URL,
            headers={"Authorization": f"Bearer {self.api_keys['openai']}"},
            files={"file": ("blurt.wav", wav_bytes, "audio/wav")},
            data=data,
            timeout=30,
        )
        self._log_transcription_response(response)
        response.raise_for_status()
        return response.json()

    def _transcribe_openrouter(self, wav_bytes: bytes) -> dict:
        audio_b64 = base64.b64encode(wav_bytes).decode("ascii")
        response = self.session.post(
            OPENROUTER_TRANSCRIPTIONS_URL,
            headers={
                "Authorization": f"Bearer {self.api_keys['openrouter']}",
                "HTTP-Referer": OPENROUTER_APP_URL,
                "X-OpenRouter-Title": OPENROUTER_APP_TITLE,
            },
            json={
                "model": self.transcription_model,
                "input_audio": {"data": audio_b64, "format": "wav"},
                "language": self.transcription_language,
            },
            timeout=30,
        )
        self._log_transcription_response(response)
        response.raise_for_status()
        return response.json()

    def _log_transcription_response(self, response: requests.Response):
        logging.info(
            "Transcription response: status=%s content_type=%s bytes=%d",
            response.status_code,
            response.headers.get("content-type", ""),
            len(response.content),
        )
        if not response.ok:
            logging.error("Transcription error response: %s", response.text[:1000])

    def toggle_recording(self):
        with self.lock:
            if not self.recording:
                self._start_recording()
            else:
                audio_data = self._stop_recording()
                if audio_data is not None:
                    self.pending_transcriptions += 1
                    self._update_icon()
                    t = threading.Thread(
                        target=self._transcribe, args=(audio_data,), daemon=True
                    )
                    t.start()
                else:
                    self.platform.notify("Error", "No audio recorded.")

    def _on_click(self, icon, item):
        self.toggle_recording()

    def _show_logs(self, icon, item):
        try:
            logging.info("Opening log file: %s", LOG_FILE)
            self.platform.open_logs()
        except Exception as e:
            logging.exception("Could not open log file")
            self.platform.notify("Error", f"Could not open log file: {e}")

    def _toggle_awake(self, icon, item):
        if self._awake_keeper is None:
            return
        if self._awake_keeper.is_active:
            self._awake_keeper.stop()
        else:
            self._awake_keeper.start()
        self.icon.update_menu()

    def _select_model(self, model: str):
        def handler(icon, item):
            self.api_keys = {
                "openai": load_api_key(),
                "openrouter": load_openrouter_key(),
            }
            provider = self._provider_for_model(model)
            if not self.api_keys.get(provider):
                logging.error(
                    "Model selection blocked: model=%s provider=%s has no API key",
                    model,
                    provider,
                )
                self.platform.notify(
                    "Model Unavailable",
                    f"No {provider} API key is configured.",
                )
                self.icon.update_menu()
                return
            self.transcription_model = model
            save_transcription_model(model)
            self.icon.update_menu()
            self.platform.notify("Model Selected", get_model_label(model))
        return handler

    def _select_language(self, language: str):
        def handler(icon, item):
            self.transcription_language = language
            save_transcription_language(language)
            self.icon.update_menu()
            self.platform.notify("Language Selected", get_language_label(language))
        return handler

    def _is_model_selected(self, model: str):
        def check(item):
            return self.transcription_model == model
        return check

    def _is_provider_available(self, provider: str):
        def check(item):
            return bool(self.api_keys.get(provider))
        return check

    def _is_language_selected(self, language: str):
        def check(item):
            return self.transcription_language == language
        return check

    def _select_device(self, name: str):
        def handler(icon, item):
            self.device_name = name
            self.device_index = self._resolve_device(name)
            save_input_device_name(name)
            self.icon.update_menu()
        return handler

    def _is_device_selected(self, name: str):
        def check(item):
            return self.device_name == name
        return check

    def _build_menu(self):
        device_items = [
            pystray.MenuItem(
                "System default",
                self._select_device(""),
                checked=self._is_device_selected(""),
                radio=True,
            )
        ]
        for dev in self.input_devices:
            device_items.append(
                pystray.MenuItem(
                    dev["name"],
                    self._select_device(dev["name"]),
                    checked=self._is_device_selected(dev["name"]),
                    radio=True,
                )
            )

        model_items = []
        for model, label, provider in TRANSCRIPTION_MODELS:
            model_items.append(
                pystray.MenuItem(
                    label,
                    self._select_model(model),
                    checked=self._is_model_selected(model),
                    enabled=self._is_provider_available(provider),
                    radio=True,
                )
            )

        language_items = []
        for language, label in TRANSCRIPTION_LANGUAGES:
            language_items.append(
                pystray.MenuItem(
                    label,
                    self._select_language(language),
                    checked=self._is_language_selected(language),
                    radio=True,
                )
            )

        awake_items = []
        if self._awake_keeper is not None:
            awake_items.append(
                pystray.MenuItem(
                    "Awake",
                    self._toggle_awake,
                    checked=lambda item: self._awake_keeper.is_active,
                )
            )

        return pystray.Menu(
            pystray.MenuItem(
                lambda item: "Stop Recording" if self.recording else "Start Recording",
                self._on_click,
                default=True,
            ),
            pystray.Menu.SEPARATOR,
            pystray.MenuItem("Input Device", pystray.Menu(*device_items)),
            pystray.MenuItem("Model", pystray.Menu(*model_items)),
            pystray.MenuItem(lambda item: self._api_key_label(), None, enabled=False),
            pystray.MenuItem("Language", pystray.Menu(*language_items)),
            pystray.MenuItem("Show Logs", self._show_logs),
            *awake_items,
            pystray.Menu.SEPARATOR,
            pystray.MenuItem("Quit", self._on_quit),
        )

    def _on_quit(self, icon, item):
        if self.recording:
            self._stop_recording()
        if self._awake_keeper and self._awake_keeper.is_active:
            self._awake_keeper.stop()
        self.platform.cleanup_toggle_trigger()
        icon.stop()

    def _setup(self, icon):
        icon.visible = True

    def run(self):
        self.platform.setup_toggle_trigger(self)
        logging.info("Starting tray icon")
        self.icon = pystray.Icon(
            APP_NAME,
            icon=self._create_icon_image((120, 120, 120)),
            title=f"{APP_NAME} - Ready",
            menu=self._build_menu(),
        )
        self.icon.run(setup=self._setup)


def _silence_benign_gtk_warnings():
    """Drop a harmless, repeating Gdk-CRITICAL emitted by Gtk.StatusIcon.

    pystray's GTK backend (the one qtile's XEmbed Systray widget needs) uses the
    deprecated Gtk.StatusIcon, which logs this assertion on icon/menu updates:
        gdk_window_thaw_toplevel_updates: assertion
        'window->update_and_descendants_freeze_count > 0' failed
    It is cosmetic. Suppress only that message and re-emit every other Gdk log so
    genuine problems still surface.
    """
    try:
        from gi.repository import GLib
    except Exception:
        return

    benign = "gdk_window_thaw_toplevel_updates"

    def handler(domain, level, message, _user_data):
        if benign not in message:
            print(f"{domain}: {message}", file=sys.stderr)

    GLib.log_set_handler(
        "Gdk",
        GLib.LogLevelFlags.LEVEL_CRITICAL
        | GLib.LogLevelFlags.LEVEL_WARNING
        | GLib.LogLevelFlags.FLAG_FATAL
        | GLib.LogLevelFlags.FLAG_RECURSION,
        handler,
        None,
    )


def main():
    setup_logging()
    logging.info("Starting %s argv=%s platform=%s log_file=%s", APP_NAME, sys.argv, sys.platform, LOG_FILE)
    if len(sys.argv) > 1 and sys.argv[1] == "toggle":
        send_toggle()
        return

    _silence_benign_gtk_warnings()
    platform = get_platform()
    app = BlurtApp(platform)
    app.run()


if __name__ == "__main__":
    main()