blitztext-app-linux/linux/blitztext/config.py

"""Configuration loading and the default config template."""

from __future__ import annotations

import os
import tomllib
from dataclasses import dataclass, field
from pathlib import Path

CONFIG_DIR = Path(os.environ.get("XDG_CONFIG_HOME", Path.home() / ".config")) / "blitztext"
CONFIG_PATH = CONFIG_DIR / "config.toml"


@dataclass
class Workflow:
    name: str
    hotkey: str
    mode: str  # "transcribe" | "rewrite"
    prompt: str = ""
    # Optional per-workflow overrides of the [rewrite] defaults.
    model: str | None = None
    temperature: float | None = None
    # Cosmetic, used by the GUI.
    description: str = ""
    icon: str = "⚡"


@dataclass
class Config:
    # general
    recorder: str = "auto"
    output: str = "type"          # type | paste
    type_delay_ms: int = 12
    notify: bool = True
    language: str = "de"          # whisper hint; "" = autodetect
    # whisper
    model: str = "small"
    device: str = "auto"          # auto | cuda | cpu
    compute_type: str = "auto"    # auto | int8 | float16 | int8_float16
    beam_size: int = 5
    # rewrite (OpenAI-compatible)
    base_url: str = "https://api.openai.com/v1"
    api_key_env: str = "OPENAI_API_KEY"
    rewrite_model: str = "gpt-4o-mini"
    temperature: float = 0.3
    timeout: int = 45
    # workflows
    workflows: list[Workflow] = field(default_factory=list)

    @property
    def api_key(self) -> str | None:
        return os.environ.get(self.api_key_env) or None


def load(path: Path = CONFIG_PATH) -> Config:
    """Load config from TOML, creating a default file on first run."""
    if not path.exists():
        ensure_default(path)

    with path.open("rb") as fh:
        data = tomllib.load(fh)

    g = data.get("general", {})
    w = data.get("whisper", {})
    r = data.get("rewrite", {})

    cfg = Config(
        recorder=g.get("recorder", "auto"),
        output=g.get("output", "type"),
        type_delay_ms=int(g.get("type_delay_ms", 4)),
        notify=bool(g.get("notify", True)),
        language=g.get("language", "de"),
        model=w.get("model", "small"),
        device=w.get("device", "auto"),
        compute_type=w.get("compute_type", "auto"),
        beam_size=int(w.get("beam_size", 5)),
        base_url=r.get("base_url", "https://api.openai.com/v1").rstrip("/"),
        api_key_env=r.get("api_key_env", "OPENAI_API_KEY"),
        rewrite_model=r.get("model", "gpt-4o-mini"),
        temperature=float(r.get("temperature", 0.3)),
        timeout=int(r.get("timeout", 45)),
    )

    for entry in data.get("workflow", []):
        cfg.workflows.append(
            Workflow(
                name=entry["name"],
                hotkey=entry["hotkey"],
                mode=entry.get("mode", "transcribe"),
                prompt=entry.get("prompt", ""),
                model=entry.get("model"),
                temperature=entry.get("temperature"),
                description=entry.get("description", ""),
                icon=entry.get("icon", "⚡"),
            )
        )

    if not cfg.workflows:
        raise ValueError(f"No [[workflow]] entries defined in {path}")

    return cfg


def save(cfg: Config, path: Path = CONFIG_PATH) -> None:
    """Write the config back to TOML (used by the settings UI).

    Note: inline comments from the template are not preserved on save.
    """
    import tomli_w

    data: dict = {
        "general": {
            "recorder": cfg.recorder,
            "output": cfg.output,
            "type_delay_ms": cfg.type_delay_ms,
            "notify": cfg.notify,
            "language": cfg.language,
        },
        "whisper": {
            "model": cfg.model,
            "device": cfg.device,
            "compute_type": cfg.compute_type,
            "beam_size": cfg.beam_size,
        },
        "rewrite": {
            "base_url": cfg.base_url,
            "api_key_env": cfg.api_key_env,
            "model": cfg.rewrite_model,
            "temperature": cfg.temperature,
            "timeout": cfg.timeout,
        },
        "workflow": [],
    }
    for wf in cfg.workflows:
        entry: dict = {"name": wf.name, "hotkey": wf.hotkey, "mode": wf.mode}
        if wf.prompt:
            entry["prompt"] = wf.prompt
        if wf.model:
            entry["model"] = wf.model
        if wf.temperature is not None:
            entry["temperature"] = wf.temperature
        if wf.description:
            entry["description"] = wf.description
        if wf.icon and wf.icon != "⚡":
            entry["icon"] = wf.icon
        data["workflow"].append(entry)

    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open("wb") as fh:
        tomli_w.dump(data, fh)


def ensure_default(path: Path = CONFIG_PATH) -> Path:
    path.parent.mkdir(parents=True, exist_ok=True)
    if not path.exists():
        path.write_text(DEFAULT_CONFIG, encoding="utf-8")
    return path


# Hotkey syntax is pynput's GlobalHotKeys format, e.g. "<ctrl>+<alt>+space".
DEFAULT_CONFIG = """\
# Blitztext for Linux — configuration
# Hotkey format follows pynput: <ctrl> <alt> <shift> <cmd> + a letter/keyname.
# Each hotkey TOGGLES recording: press to start speaking, press again to finish.

[general]
recorder = "auto"        # auto | pw-record | parecord | arecord
output = "type"          # "type" = xdotool types it; "paste" = clipboard + Ctrl+V
type_delay_ms = 12       # per-keystroke delay for xdotool type (raise if chars drop)
notify = true            # desktop notifications for each phase
language = "de"          # Whisper language hint; "" = autodetect

[whisper]
model = "small"          # tiny | base | small | medium | large-v3, or a local path
device = "auto"          # auto | cuda | cpu  (auto tries cuda, falls back to cpu)
compute_type = "auto"    # auto | int8 | float16 | int8_float16
beam_size = 5

[rewrite]
# OpenAI-compatible chat endpoint. Define your own provider/API/model here.
# Point base_url at OpenAI, OR any local server that speaks the OpenAI chat API
# (vLLM, llama-swap, Ollama's /v1, LM Studio, ...). Examples:
#   base_url = "https://api.openai.com/v1"          (OpenAI)
#   base_url = "http://localhost:8000/v1"           (local vLLM / llama-swap)
# api_key_env names the ENV VAR holding the key (local servers often ignore it).
base_url = "https://api.openai.com/v1"
api_key_env = "OPENAI_API_KEY"
model = "gpt-4o-mini"            # default model for rewrite workflows
temperature = 0.3
timeout = 45

# ----------------------------------------------------------------------------
# Workflows. mode = "transcribe" types the raw transcript. mode = "rewrite"
# sends the transcript through the LLM with `prompt` as the system prompt.
# Any workflow may override the [rewrite] defaults with its own:
#     model = "gpt-4o"
#     temperature = 0.4
# ----------------------------------------------------------------------------

[[workflow]]
name = "Transcribe"
icon = "⚡"
description = "Speak, get plain text."
hotkey = "<ctrl>+<alt>+<space>"
mode = "transcribe"

[[workflow]]
name = "Nicer email"
icon = "✉"
description = "Rough notes → polished email."
hotkey = "<ctrl>+<alt>+e"
mode = "rewrite"
prompt = '''Du bist ein Schreibassistent fuer E-Mails. Du erhaeltst ein gesprochenes Transkript.
Schreibe daraus eine freundliche, gut formulierte und etwas ausfuehrlichere E-Mail:
- Korrigiere Rechtschreibung und Grammatik
- Formuliere hoeflich, klar und professionell
- Ergaenze sinnvolle Hoeflichkeitsfloskeln (Anrede/Gruss), wenn passend
- Behalte die urspruengliche Aussage und Absicht bei, erfinde keine Fakten
- Antworte in der Sprache des Transkripts
- Gib NUR den E-Mail-Text zurueck, keine Erklaerungen'''

[[workflow]]
name = "Improve text"
icon = "✨"
description = "Speak → cleaner writing."
hotkey = "<ctrl>+<alt>+i"
mode = "rewrite"
prompt = '''Du bist ein Lektor und Schreibassistent. Verbessere den folgenden gesprochenen Text:
- Korrigiere Rechtschreibung und Grammatik
- Verbessere Formulierung und Lesefluss, behalte die Bedeutung bei
- Antworte in der Sprache des Transkripts
- Gib NUR den verbesserten Text zurueck, keine Erklaerungen'''

[[workflow]]
name = "Calm down"
icon = "☺"
description = "Frustrated in → calm out."
hotkey = "<ctrl>+<alt>+c"
mode = "rewrite"
prompt = '''Du erhaeltst ein gesprochenes, frustriertes oder veraergertes Transkript.
Formuliere es in eine ruhige, sachliche und hoefliche Nachricht um, die dasselbe
Anliegen professionell vermittelt. Antworte in der Sprache des Transkripts.
Gib NUR die umformulierte Nachricht zurueck, keine Erklaerungen.'''

[[workflow]]
name = "Add emojis"
icon = "✿"
description = "Text in → emojis out."
hotkey = "<ctrl>+<alt>+j"
mode = "rewrite"
prompt = '''Du erhaeltst ein gesprochenes Transkript. Gib den Text moeglichst originalgetreu
zurueck, fuege aber regelmaessig passende Emojis ein (etwa alle 1-2 Saetze).
Korrigiere offensichtliche Fehler, behalte Stil und Bedeutung bei.
Gib NUR den Text mit Emojis zurueck, keine Erklaerungen.'''
"""