feat: settings web UI (models / STT / TTS speed / language / LLM instructions)

Adds /settings (served by the bridge) to change the LLM model (from installed
Ollama models), Whisper model, TTS engine + MeloTTS speed, output language,
agentic max-turns, thinking mode, and free-form LLM instructions — live, with a
'apply' that restarts the bridge + TTS worker. Settings persist to the runtime
config JSON; engine reads output_language + llm_instructions and the TTS worker
reads melo_speed from it. Bridge port publishable for access.

.env.example

@@ -187,3 +187,10 @@ COMPOSE_FILE=docker-compose.yml:docker-compose.gpu-linux.yml
 # OLLAMA_CHAT_MODEL=qwen2.5:3b   # speed (fits easily, faster on 8GB GPUs)
 # WHISPER_MODEL=small            # small frees VRAM for a bigger LLM; medium=more accurate
 # MELO_DEVICE=cuda               # cpu if no GPU on the bot host
+
+# --- Settings web UI (http://localhost:8765/settings on the bot host) ---
+# To reach it, expose the bridge to the host loopback:
+#   BRIDGE_HOST=0.0.0.0
+#   SETTINGS_PUBLISH_BIND=127.0.0.1   # 0.0.0.0 to allow LAN access (no auth)
+# Change models / STT / TTS speed / language / LLM instructions live; "적용"
+# restarts the bridge + TTS worker so changes take effect.

bridge/melo_worker.py

@@ -36,7 +36,26 @@ from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
 HOST = os.environ.get("MELO_WORKER_HOST", "127.0.0.1")
 PORT = int(os.environ.get("MELO_WORKER_PORT", "8770"))
 LANGUAGE = os.environ.get("MELO_LANGUAGE", "KR")
-SPEED = float(os.environ.get("MELO_SPEED", "1.5"))
+
+
+def _resolve_speed() -> float:
+    """Speaking rate: the settings-UI value (runtime config JSON) wins, else the
+    MELO_SPEED env, else 1.5. Read at startup; the settings UI restarts this
+    worker on apply so a new value takes effect."""
+    try:
+        cp = os.environ.get("JARVIS_CONFIG_PATH", "/app/config/jarvis.json")
+        v = json.loads(open(cp, encoding="utf-8").read()).get("melo_speed")
+        if v is not None:
+            return float(v)
+    except Exception:
+        pass
+    try:
+        return float(os.environ.get("MELO_SPEED", "1.5"))
+    except ValueError:
+        return 1.5
+
+
+SPEED = _resolve_speed()
 DEVICE = os.environ.get("MELO_DEVICE", "cpu")
 
 # Model + speaker id are loaded once, guarded by a lock because MeloTTS

bridge/server.py

@@ -52,11 +52,18 @@ from flask import Flask, request, jsonify, Response, stream_with_context
 try:  # package-relative when imported as ``bridge.server``
     from bridge.text_utils import split_sentences
     from bridge.stt_filter import filter_speech_segments, has_speech
+    from bridge import settings_web
 except ImportError:  # script-relative when run as ``bridge/server.py``
     from text_utils import split_sentences
     from stt_filter import filter_speech_segments, has_speech
+    import settings_web
 
 app = Flask(__name__)
+# Settings web UI (/settings) — change models/language/TTS/instructions live.
+try:
+    settings_web.register(app)
+except Exception as _e:  # pragma: no cover - never block the bridge on the UI
+    print(f"[bridge] settings UI unavailable: {_e}", flush=True)
 
 # ---------------------------------------------------------------------------
 # Configuration (env-driven; see .env.example)

bridge/settings_web.py

@@ -0,0 +1,176 @@
+"""Settings web UI for the Jarvis bridge.
+
+A small in-app page (served by the Flask bridge) to change models, language,
+TTS and the LLM instructions WITHOUT editing files or rebuilding. Writes to the
+runtime config JSON (JARVIS_CONFIG_PATH) that ``load_settings()`` reads, then
+restarts the bridge (and TTS worker) via supervisord so changes take effect.
+
+Internal-network use only (no auth, per deployment decision).
+"""
+
+from __future__ import annotations
+
+import json
+import os
+import subprocess
+import urllib.request
+from pathlib import Path
+from typing import Any, Dict
+
+# Fields the UI manages. Each maps to a key in the runtime config JSON, with a
+# label and an input kind for the form.
+FIELDS = [
+    ("ollama_chat_model", "LLM 모델", "model"),
+    ("whisper_model", "STT(Whisper) 모델", "select:tiny,base,small,medium,large,large-v3"),
+    ("tts_engine", "TTS 엔진", "select:melo,piper"),
+    ("melo_speed", "TTS 속도 (MeloTTS)", "number:0.5:2.5:0.1"),
+    ("output_language", "출력 언어 (비우면 사용자 언어)", "text"),
+    ("llm_thinking_enabled", "LLM 사고(thinking) 모드", "bool"),
+    ("agentic_max_turns", "에이전트 최대 반복", "number:1:12:1"),
+    ("llm_instructions", "LLM 추가 지침 (시스템 프롬프트에 덧붙임)", "textarea"),
+]
+_KEYS = [k for k, _, _ in FIELDS]
+
+
+def _config_path() -> Path:
+    p = os.environ.get("JARVIS_CONFIG_PATH")
+    return Path(p).expanduser() if p else (Path.home() / ".config" / "jarvis" / "config.json")
+
+
+def _read_config() -> Dict[str, Any]:
+    try:
+        return json.loads(_config_path().read_text("utf-8"))
+    except Exception:
+        return {}
+
+
+def _current() -> Dict[str, Any]:
+    cfg = _read_config()
+    out: Dict[str, Any] = {}
+    for k in _KEYS:
+        if k == "melo_speed":
+            out[k] = cfg.get("melo_speed", os.environ.get("MELO_SPEED", "1.5"))
+        elif k == "output_language":
+            out[k] = cfg.get("output_language", os.environ.get("OUTPUT_LANGUAGE", ""))
+        else:
+            out[k] = cfg.get(k, "")
+    return out
+
+
+def _ollama_models() -> list[str]:
+    base = os.environ.get("OLLAMA_BASE_URL", "http://127.0.0.1:11434").rstrip("/")
+    try:
+        with urllib.request.urlopen(f"{base}/api/tags", timeout=4) as r:
+            data = json.loads(r.read())
+        return sorted(m.get("name", "") for m in data.get("models", []) if m.get("name"))
+    except Exception:
+        return []
+
+
+def _save(updates: Dict[str, Any]) -> None:
+    cfg = _read_config()
+    for k, v in updates.items():
+        if k not in _KEYS:
+            continue
+        if k == "melo_speed":
+            try:
+                v = float(v)
+            except (TypeError, ValueError):
+                continue
+        elif k == "agentic_max_turns":
+            try:
+                v = int(v)
+            except (TypeError, ValueError):
+                continue
+        elif k == "llm_thinking_enabled":
+            v = str(v).lower() in ("1", "true", "on", "yes")
+        cfg[k] = v
+    p = _config_path()
+    p.parent.mkdir(parents=True, exist_ok=True)
+    p.write_text(json.dumps(cfg, ensure_ascii=False, indent=2), "utf-8")
+
+
+def _apply() -> str:
+    # Restart the bridge (re-reads config) and the TTS worker (picks up speed).
+    out = []
+    for prog in ("melo-worker", "bridge"):
+        try:
+            r = subprocess.run(["supervisorctl", "restart", prog], capture_output=True, text=True, timeout=30)
+            out.append(f"{prog}: {(r.stdout or r.stderr).strip()}")
+        except Exception as e:  # pragma: no cover
+            out.append(f"{prog}: {e}")
+    return " | ".join(out)
+
+
+_PAGE = """<!doctype html><html lang=ko><head><meta charset=utf-8>
+<meta name=viewport content="width=device-width,initial-scale=1">
+<title>Jarvis 설정</title><style>
+body{font-family:system-ui,Segoe UI,Apple SD Gothic Neo,sans-serif;max-width:680px;margin:24px auto;padding:0 16px;color:#222}
+h1{font-size:20px}label{display:block;margin:14px 0 4px;font-weight:600}
+input,select,textarea{width:100%;padding:8px;border:1px solid #ccc;border-radius:8px;font-size:14px;box-sizing:border-box}
+textarea{min-height:90px}.row{margin-bottom:6px}.btns{margin-top:18px;display:flex;gap:8px}
+button{padding:10px 16px;border:0;border-radius:8px;font-size:14px;cursor:pointer}
+.save{background:#2d6cdf;color:#fff}.apply{background:#16a34a;color:#fff}
+#msg{margin-top:12px;color:#16a34a;white-space:pre-wrap}.hint{color:#888;font-weight:400;font-size:12px}
+</style></head><body>
+<h1>⚙️ Jarvis 설정</h1>
+<p class=hint>저장 후 [적용]을 누르면 브리지/TTS가 재시작되며 반영됩니다. (내부망 전용)</p>
+<form id=f></form>
+<div class=btns><button class=save type=button onclick=save()>저장</button>
+<button class=apply type=button onclick=apply()>저장 후 적용(재시작)</button></div>
+<div id=msg></div>
+<script>
+const FIELDS=__FIELDS__, MODELS=__MODELS__, CUR=__CUR__;
+const f=document.getElementById('f');
+for(const [k,label,kind] of FIELDS){
+  const id='fld_'+k; let el;
+  if(k==='ollama_chat_model' && MODELS.length){
+    el=`<select id="${id}">`+MODELS.map(m=>`<option ${m===CUR[k]?'selected':''}>${m}</option>`).join('')+`</select>`;
+  } else if(kind.startsWith('select:')){
+    el='<select id="'+id+'">'+kind.slice(7).split(',').map(o=>`<option ${o===CUR[k]?'selected':''}>${o}</option>`).join('')+'</select>';
+  } else if(kind==='textarea'){
+    el=`<textarea id="${id}">${CUR[k]??''}</textarea>`;
+  } else if(kind==='bool'){
+    el=`<select id="${id}"><option value=false ${!CUR[k]?'selected':''}>off</option><option value=true ${CUR[k]?'selected':''}>on</option></select>`;
+  } else if(kind.startsWith('number:')){
+    const [mn,mx,st]=kind.slice(7).split(':');
+    el=`<input id="${id}" type=number min=${mn} max=${mx} step=${st} value="${CUR[k]??''}">`;
+  } else { el=`<input id="${id}" type=text value="${CUR[k]??''}">`; }
+  f.insertAdjacentHTML('beforeend',`<div class=row><label>${label}</label>${el}</div>`);
+}
+function collect(){const o={};for(const [k] of FIELDS){o[k]=document.getElementById('fld_'+k).value;}return o;}
+async function post(url){const r=await fetch(url,{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify(collect())});return r.json();}
+async function save(){const j=await post('/api/settings');document.getElementById('msg').textContent=j.ok?'저장됨':'오류: '+(j.error||'');}
+async function apply(){await post('/api/settings');const j=await fetch('/api/settings/apply',{method:'POST'}).then(r=>r.json());document.getElementById('msg').textContent='적용: '+(j.result||j.error||'');}
+</script></body></html>"""
+
+
+def register(app) -> None:
+    """Attach the settings routes to the Flask ``app``."""
+    from flask import request, jsonify, Response
+
+    @app.get("/settings")
+    def _settings_page():  # noqa: ANN202
+        html = (
+            _PAGE.replace("__FIELDS__", json.dumps(FIELDS, ensure_ascii=False))
+            .replace("__MODELS__", json.dumps(_ollama_models()))
+            .replace("__CUR__", json.dumps(_current(), ensure_ascii=False))
+        )
+        return Response(html, mimetype="text/html")
+
+    @app.get("/api/settings")
+    def _get_settings():  # noqa: ANN202
+        return jsonify({"ok": True, "settings": _current(), "models": _ollama_models()})
+
+    @app.post("/api/settings")
+    def _post_settings():  # noqa: ANN202
+        data = request.get_json(silent=True) or {}
+        try:
+            _save(data)
+            return jsonify({"ok": True})
+        except Exception as e:  # pragma: no cover
+            return jsonify({"ok": False, "error": str(e)}), 500
+
+    @app.post("/api/settings/apply")
+    def _apply_settings():  # noqa: ANN202
+        return jsonify({"ok": True, "result": _apply()})

docker-compose.yml

@@ -119,6 +119,9 @@ services:
       # Browser-control endpoint a remote bot posts to (real xdotool input runs
       # on THIS host). Published on the LAN for the browser-host layout.
       - "${CDP_PUBLISH_BIND:-127.0.0.1}:${BROWSER_CONTROL_PORT:-8777}:8777"  # control-server
+      # Settings UI + brain API (bridge). Reach it at http://localhost:8765/settings
+      # on the bot host. Requires BRIDGE_HOST=0.0.0.0 (set in .env) to forward.
+      - "${SETTINGS_PUBLISH_BIND:-127.0.0.1}:${BRIDGE_PORT:-8765}:8765"  # bridge / settings
       # The brain bridge is NOT published: it binds the container's loopback
       # (BRIDGE_HOST=127.0.0.1) and is only consumed by the bot in this same
       # container, so it needs no host port and stays unreachable off-container.

src/jarvis/reply/engine.py

@@ -826,6 +826,20 @@ def _build_enrichment_context_hint(cfg, recent_messages: list) -> Optional[str]:
 
 
 # Site tokens (proper nouns, not language patterns) → controlBrowser search site.
+def _extra_config(key: str, default=""):
+    """Read a key from the runtime config JSON (JARVIS_CONFIG_PATH) for settings
+    the settings-web UI manages but that aren't on the Settings dataclass
+    (llm_instructions, output_language override). Cheap + fail-open."""
+    try:
+        import json as _json
+        from pathlib import Path as _Path
+        p = os.environ.get("JARVIS_CONFIG_PATH")
+        path = _Path(p).expanduser() if p else (_Path.home() / ".config" / "jarvis" / "config.json")
+        return _json.loads(path.read_text("utf-8")).get(key, default) or default
+    except Exception:
+        return default
+
+
 _SITE_TOKEN_MAP = {
     "네이버": "naver", "naver": "naver",
     "구글": "google", "google": "google",
@@ -1683,7 +1697,7 @@ def run_reply_engine(db: "Database", cfg, tts: Optional[Any],
         # it primacy over the persona's "use the user's language" lines — a tail
         # instruction loses to those when the query itself is in another language.
         _lang_directive = reply_language_directive(
-            os.environ.get("OUTPUT_LANGUAGE"),
+            os.environ.get("OUTPUT_LANGUAGE") or _extra_config("output_language", ""),
             getattr(cfg, "tts_engine", "piper"),
         )
         if _lang_directive:
@@ -1768,6 +1782,11 @@ def run_reply_engine(db: "Database", cfg, tts: Optional[Any],
         # else: tools are passed via the native tools API parameter — do not include tools_desc
         # here as well, since that confuses the model and causes it to not use tools properly.
 
+        # User-defined extra LLM instructions from the settings UI.
+        _user_instructions = str(_extra_config("llm_instructions", "")).strip()
+        if _user_instructions:
+            guidance.append("Additional instructions from the operator:\n" + _user_instructions)
+
         # Recency reinforcement: repeat the language lock at the very END too.
         # In a ~5k-token prompt the front-placed rule gets "lost in the middle";
         # bigger models (qwen2.5:7b) otherwise leak Chinese/Cyrillic mid-reply.