"""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. "++space". DEFAULT_CONFIG = """\ # Blitztext for Linux — configuration # Hotkey format follows pynput: + 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 = "++" mode = "transcribe" [[workflow]] name = "Nicer email" icon = "✉" description = "Rough notes → polished email." hotkey = "++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 = "++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 = "++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 = "++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.''' """