""" app.py — Legawa Gradio Space for Build Small Hackathon. Runs the 4 agent workflows (analis_ruu, peneliti, penyusun, surat) inside a Gradio web UI instead of the Typer CLI. Default LLM backend is HF Inference API (zero-config demo); users can override in Settings. """ from __future__ import annotations import io import os import sys import tempfile import threading import time import traceback from pathlib import Path from typing import Callable, Iterator # Ensure the src/ package is importable on HF Spaces _src = Path(__file__).resolve().parent / "src" if _src.exists() and str(_src) not in sys.path: sys.path.insert(0, str(_src)) import gradio as gr from rich.console import Console from legawa.agents import analis_ruu, peneliti, penyusun, surat from legawa.tools.cache import CachingPasalClient from legawa.tools.pasal import PasalClient from legawa.tools.ethics import ethics_verify # ── Default HF Inference API config (zero-config demo) ────────────────── # Uses huggingface_hub's InferenceClient (works reliably on HF Spaces). # Users can override via the Settings tab to use custom endpoints. HF_BIG_MODEL = os.environ.get("HF_BIG_MODEL", "Qwen/Qwen3.5-9B") HF_SMALL_MODEL = os.environ.get("HF_SMALL_MODEL", "Qwen/Qwen3.5-9B") HF_TOKEN = os.environ.get("HF_TOKEN", "") BUILD_INFO = "Build Small Hackathon 2026 · legawa v0.1" RUU_EXAMPLE = """RUU Perlindungan Data Pribadi Kesehatan Pasal 1 Data kesehatan pasien wajib dilindungi oleh fasilitas pelayanan kesehatan dan penyelenggara sistem elektronik kesehatan. Pasal 2 Setiap rumah sakit wajib meminta persetujuan tertulis sebelum membagikan data pasien kepada pihak ketiga. Pasal 3 Pemerintah daerah wajib menyediakan kanal pengaduan bagi pasien yang data kesehatannya disalahgunakan.""" SURAT_EXAMPLE = """Yth. Anggota DPRD, Saya warga Kelurahan Sukamaju. Sudah tiga bulan saluran drainase di depan rumah kami tersumbat dan menyebabkan banjir setiap hujan. Kami sudah melapor ke RT dan kelurahan, tetapi belum ada tindak lanjut. Mohon bantuan agar dinas terkait segera turun mengecek dan membersihkan saluran tersebut. Hormat kami, Warga RW 04""" SURAT_MBG_EXAMPLE = """Yth. Bapak/Ibu Anggota DPR RI, Saya Sulastri, orang tua murid SDN 2 Karangrejo. Hari Selasa lalu, 27 anak di sekolah \ kami mengalami mual dan muntah setelah menyantap menu Makan Bergizi Gratis dari dapur \ SPPG kecamatan. Tiga anak sampai dirawat di puskesmas. Menurut guru, lauk ayam yang \ dibagikan berbau asam karena makanan baru tiba di sekolah lewat pukul 11 padahal \ dimasak sejak subuh. Kami para orang tua sebenarnya mendukung program MBG karena sangat membantu, tetapi \ kami takut kejadian ini terulang. Kami mohon Bapak/Ibu memperjuangkan: (1) pemeriksaan \ standar keamanan pangan dapur SPPG di daerah kami, (2) kejelasan tanggung jawab dan \ biaya pengobatan anak-anak yang keracunan, dan (3) jadwal distribusi yang tidak \ membiarkan makanan terlalu lama di perjalanan. Hormat kami, Sulastri, perwakilan paguyuban orang tua murid SDN 2 Karangrejo""" def _llm_label(llm: object) -> str: """Return the model label for both HFLLM and OpenAI-compatible LLM objects.""" if hasattr(llm, "model_id"): return str(getattr(llm, "model_id")) cfg = getattr(llm, "cfg", None) if cfg is not None and hasattr(cfg, "model"): return str(cfg.model) return "model" def _is_hf_default(url_or_model: str) -> bool: """True if this is a model ID (no ://) or a default HF Inference API endpoint.""" return "://" not in url_or_model or "huggingface.co/models/" in url_or_model def _model_id_from_url(url: str) -> str: """Extract model ID from HF Inference API URL.""" # URL format: https://api-inference.huggingface.co/models/{model_id}/v1 if "/models/" in url: return url.split("/models/")[1].split("/v1")[0] return url # ── Bootstrap: create settings + pool given user overrides ────────────── def build_pool( big_url: str = "", big_key: str = "", big_model: str = "", small_url: str = "", small_key: str = "", small_model: str = "", pasal_token: str = "", temperature: float = 0.3, max_tokens: int = 4096, strict_citations: bool = True, ) -> tuple: """Build an LLM pool + CachingPasalClient from user-provided overrides. Uses HFLLMPool (InferenceClient) for HF endpoints, LLMPool (OpenAI client) for custom endpoints. Falls through to env vars / HF defaults for anything left blank. """ from datetime import date # Resolve Pasal token: user input → env var → empty pasal_token = pasal_token or os.environ.get("PASAL_API_TOKEN", "") # Resolve BIG endpoint: user input → env var → HF default resolved_big_url = big_url or os.environ.get("LLM_BIG_URL", "") resolved_big_key = big_key or os.environ.get("LLM_BIG_API_KEY", HF_TOKEN) resolved_big_model = big_model or os.environ.get("LLM_BIG_MODEL", HF_BIG_MODEL) # Resolve SMALL endpoint: user input → env var → HF default resolved_small_url = small_url or os.environ.get("LLM_SMALL_URL", "") resolved_small_key = small_key or os.environ.get("LLM_SMALL_API_KEY", HF_TOKEN) resolved_small_model = small_model or os.environ.get("LLM_SMALL_MODEL", HF_SMALL_MODEL) run_date = os.environ.get("LEGAWA_RUN_DATE", date.today().isoformat()) # Decide which backend to use if not resolved_big_url or _is_hf_default(resolved_big_url): # --- HF Inference Client (default, works reliably) --- from hf_llm import HFLLMPool big_mid = _model_id_from_url(resolved_big_url) if resolved_big_url else resolved_big_model small_mid = _model_id_from_url(resolved_small_url) if resolved_small_url else resolved_small_model pool = HFLLMPool(big_mid, small_mid, token=resolved_big_key) pool.settings.run_date = run_date pool.settings.corpus_watermark = os.environ.get("PASAL_CORPUS_WATERMARK", "") pool.settings.strict_citations = strict_citations else: # --- OpenAI client (custom endpoint, e.g. llama.cpp) --- from legawa.config import LLMConfig, Settings big_cfg = LLMConfig( base_url=resolved_big_url, api_key=resolved_big_key, model=resolved_big_model, temperature=temperature, max_tokens=max_tokens, ) small_cfg = LLMConfig( base_url=resolved_small_url, api_key=resolved_small_key, model=resolved_small_model, temperature=temperature, max_tokens=max_tokens, ) override_settings = Settings( pasal_token=pasal_token, pasal_base_url=os.environ.get("PASAL_BASE_URL", "https://pasal.id/api/v1"), big=big_cfg, small=small_cfg, run_date=run_date, corpus_watermark=os.environ.get("PASAL_CORPUS_WATERMARK", ""), strict_citations=strict_citations, ) from legawa.llm import LLMPool pool = LLMPool(override_settings) raw = PasalClient( _pasal_settings(pasal_token) ) pasal = CachingPasalClient(raw) return pool, pasal def _pasal_settings(pasal_token: str) -> Settings: """Build a minimal Settings just for PasalClient.""" from legawa.config import LLMConfig, Settings dummy = LLMConfig(base_url="", api_key="", model="", temperature=0.3, max_tokens=4096) return Settings( pasal_token=pasal_token, pasal_base_url=os.environ.get("PASAL_BASE_URL", "https://pasal.id/api/v1"), big=dummy, small=dummy, run_date="", corpus_watermark="", strict_citations=False, ) return pool, pasal # ── Live agent-activity streaming ──────────────────────────────────────── # Agents report progress via rich Console. Each run gets its own capturing # console; lines are streamed to a gr.Chatbot so the multi-agent pipeline # (query expansion → probes → search → synthesis → ethics) is visible live. AGENT_LABELS = { "peneliti": "🔎 Peneliti Hukum", "penyusun": "📝 Penyusun Naskah", "surat": "📬 Agen Surat", "analis_ruu": "📄 Analis RUU", "analis": "📄 Analis RUU", "etika": "⚖️ Verifikator Etika & HAM", } ORCHESTRATOR = "🧭 Orkestrator" # Gradio 6 dropped Chatbot(type=...) — "messages" is the only/default format. _CHATBOT_KW = {} if int(gr.__version__.split(".")[0]) >= 6 else {"type": "messages"} def _activity_chatbot() -> gr.Chatbot: return gr.Chatbot(label="🤖 Aktivitas Agen (live)", height=260, **_CHATBOT_KW) def _label_for(line: str) -> tuple[str, str]: head, sep, rest = line.partition(":") if sep and head.strip().lower() in AGENT_LABELS: return AGENT_LABELS[head.strip().lower()], rest.strip() return ORCHESTRATOR, line.strip() def _stream_run(work: Callable[[Console], str]) -> Iterator[tuple[list[dict], str]]: """Run a pipeline in a thread, yielding (activity_messages, final_markdown).""" buf = io.StringIO() console = Console(file=buf, force_terminal=False, no_color=True, width=200) result: dict[str, str] = {} def runner() -> None: try: result["output"] = work(console) except Exception as e: # noqa: BLE001 result["error"] = str(e) traceback.print_exc() thread = threading.Thread(target=runner, daemon=True) thread.start() messages: list[dict] = [] seen = 0 while True: alive = thread.is_alive() text = buf.getvalue() chunk = text[seen:] lines = chunk.splitlines(keepends=True) for line in lines: if not line.endswith("\n") and alive: break # hold back a partial trailing line seen += len(line) stripped = line.strip() if not stripped: continue label, msg = _label_for(stripped) messages.append({"role": "assistant", "content": f"**{label}** · {msg}"}) if not alive: break yield messages, "⏳ *Agen sedang bekerja...*" time.sleep(0.4) thread.join() if "error" in result: messages.append({"role": "assistant", "content": f"**{ORCHESTRATOR}** · ❌ gagal: {result['error']}"}) yield messages, f"**Error:** {result['error']}" else: messages.append({"role": "assistant", "content": f"**{ORCHESTRATOR}** · ✅ selesai"}) yield messages, result.get("output", "") # ── Agent wrappers (called by Gradio) ─────────────────────────────────── def agent_analyze( source: str, big_url: str, big_key: str, small_url: str, small_key: str, pasal_token: str, ): if not source.strip(): yield [], "Masukkan teks RUU atau upload file PDF." return def work(console: Console) -> str: console.print("orkestrator: memuat model & koneksi, menyerahkan ke Analis RUU") pool, pasal = build_pool( big_url=big_url, big_key=big_key, small_url=small_url, small_key=small_key, pasal_token=pasal_token, ) try: result = analis_ruu.analyze(pool, pasal, source, console=console) console.print("etika: verifikasi 4 nilai demokrasi & HAM") return ethics_verify(result.output, pool.small) finally: pasal.close() yield from _stream_run(work) def agent_research( topic: str, big_url: str, big_key: str, small_url: str, small_key: str, pasal_token: str, ): if not topic.strip(): yield [], "Masukkan topik riset hukum." return def work(console: Console) -> str: console.print("orkestrator: memuat model & koneksi, menyerahkan ke Peneliti Hukum") pool, pasal = build_pool( big_url=big_url, big_key=big_key, small_url=small_url, small_key=small_key, pasal_token=pasal_token, ) try: output = peneliti.research(pool, pasal, topic, console=console) console.print("etika: verifikasi 4 nilai demokrasi & HAM") return ethics_verify(output, pool.small) finally: pasal.close() yield from _stream_run(work) def agent_draft( kind: str, topic: str, extra_instructions: str, with_research: bool, big_url: str, big_key: str, small_url: str, small_key: str, pasal_token: str, ): if not topic.strip(): yield [], "Masukkan topik." return def work(console: Console) -> str: console.print("orkestrator: memuat model & koneksi, menyerahkan ke Penyusun Naskah") pool, pasal = build_pool( big_url=big_url, big_key=big_key, small_url=small_url, small_key=small_key, pasal_token=pasal_token, ) try: output = penyusun.draft( pool, pasal, kind, topic, with_research=with_research, extra_instructions=extra_instructions or None, console=console, ) console.print("etika: verifikasi 4 nilai demokrasi & HAM") return ethics_verify(output, pool.small) finally: pasal.close() yield from _stream_run(work) def agent_surat( surat_text: str, verify_law: bool, big_url: str, big_key: str, small_url: str, small_key: str, pasal_token: str, ): if not surat_text.strip(): yield [], "Masukkan teks surat konstituen." return def work(console: Console) -> str: console.print("orkestrator: memuat model & koneksi, menyerahkan ke Agen Surat") pool, pasal = build_pool( big_url=big_url, big_key=big_key, small_url=small_url, small_key=small_key, pasal_token=pasal_token, ) try: result = surat.reply( pool, pasal, surat_text, verify_law=verify_law, console=console, ) output = surat.format_report(result) console.print("etika: verifikasi 4 nilai demokrasi & HAM") return ethics_verify(output, pool.small) finally: pasal.close() yield from _stream_run(work) def agent_health( big_url: str, big_key: str, small_url: str, small_key: str, pasal_token: str, ) -> str: """Quick connectivity check for all services.""" lines: list[str] = [] pool, pasal = build_pool( big_url=big_url, big_key=big_key, small_url=small_url, small_key=small_key, pasal_token=pasal_token, ) try: # Check BIG LLM try: resp = pool.big.chat( [{"role": "user", "content": "Jawab dengan satu kata: OK"}], max_tokens=10, ) lines.append(f"✅ **BIG LLM** ({_llm_label(pool.big)[:30]}...): {resp.strip()}") except Exception as e: lines.append(f"❌ **BIG LLM**: {e}") # Check SMALL LLM try: resp = pool.small.chat( [{"role": "user", "content": "Jawab dengan satu kata: OK"}], max_tokens=10, ) lines.append(f"✅ **SMALL LLM** ({_llm_label(pool.small)[:30]}...): {resp.strip()}") except Exception as e: lines.append(f"❌ **SMALL LLM**: {e}") # Check pasal.id try: result = pasal.search("ketenagakerjaan", limit=1) count = len(result.get("results", result.get("hits", []))) lines.append(f"✅ **pasal.id**: {count} hasil untuk 'ketenagakerjaan'") except Exception as e: lines.append(f"❌ **pasal.id**: {e}") lines.append(f"\n{BUILD_INFO}") return "\n\n".join(lines) finally: pasal.close() # ── File upload helper for analis_ruu ─────────────────────────────────── def handle_file_upload(file: object | None) -> str: if file is None: return "" path = Path(getattr(file, "name")) if path.suffix.lower() == ".pdf": from pypdf import PdfReader reader = PdfReader(str(path)) return "\n\n".join(page.extract_text() or "" for page in reader.pages) return path.read_text(encoding="utf-8") # ── Build Gradio UI ───────────────────────────────────────────────────── CSS = """ /* Space is compact, judge-friendly, and readable */ .gradio-container { max-width: 1100px !important; margin: 0 auto !important; } .legawa-hero { padding: 1.25rem 1.4rem; border-radius: 18px; background: linear-gradient(135deg, rgba(79,70,229,.16), rgba(16,185,129,.12)); border: 1px solid rgba(99,102,241,.25); margin-bottom: 1rem; } .legawa-hero h1 { margin-top: 0; } .legawa-card { padding: .85rem 1rem; border-radius: 14px; border: 1px solid rgba(148,163,184,.25); background: rgba(148,163,184,.08); } .legawa-card strong { color: #4f46e5; } footer { display: none !important; } .dark table { color: #e0e0e0; } """ def build_app() -> gr.Blocks: with gr.Blocks( css=CSS, title="Legawa — Asisten Legislatif", theme=gr.themes.Soft(), ) as app: gr.HTML( f"""

🏛️ Legawa

Backyard AI untuk staf DPR/DPRD: triase surat warga, riset aturan, analisis RUU, dan draf naskah kebijakan dalam menit — bukan hari.

{BUILD_INFO} · 2× Qwen3.5-9B = 18B params total, under the 32B trail limit.

""" ) # ── Hidden state for connection config shared across tabs ────── # NOTE: values start empty; build_pool falls back to env vars. # This avoids embedding secrets in the page HTML/JS. big_url = gr.Textbox(label="BIG LLM Model", value=HF_BIG_MODEL, visible=False) big_key = gr.Textbox(label="BIG LLM API Key", value="", visible=False) small_url = gr.Textbox(label="SMALL LLM Model", value=HF_SMALL_MODEL, visible=False) small_key = gr.Textbox(label="SMALL LLM API Key", value="", visible=False) pasal_token = gr.Textbox( label="pasal.id Token", value="", visible=False, ) with gr.Tabs(): # ─── Tab 1: Beranda — Welcome + Quick Guide ──────────────── with gr.TabItem("🏠 Beranda"): gr.Markdown( "## Dibangun untuk masalah nyata: kantor legislator yang kebanjiran dokumen\n\n" "Staf ahli DPR/DPRD sering harus membaca RUU panjang, mengecek dasar hukum, " "menyusun memo, dan membalas surat warga dengan waktu terbatas. Legawa mengubah " "pekerjaan awal yang repetitif menjadi draft terstruktur yang tetap bisa diverifikasi manusia.\n\n" "**Masukan produk:** fitur etika, demokrasi, dan HAM dibuat dari masukan Taufik Basari, " "anggota DPR RI 2019–2024. Ini menargetkan *Backyard AI*: masalah lokal/spesifik " "untuk orang yang benar-benar bekerja dengan dokumen legislatif.\n\n" ) with gr.Row(): gr.HTML( "
📬 Surat warga → triase
" "Ringkas keluhan, klasifikasi urgensi, sarankan tindak lanjut, lalu buat balasan resmi.
" ) gr.HTML( "
📄 RUU → catatan pasal
" "Temukan isu implementasi, potensi konflik, dan risiko HAM/demokrasi per pasal.
" ) gr.HTML( "
🔍 Topik → memo hukum
" "Cari konteks aturan via pasal.id, lalu susun memo awal yang bisa diaudit.
" ) gr.Markdown( "### 🚀 Panduan Cepat\n\n" "1. Buka **📬 Surat Konstituen** dan klik contoh untuk demo tercepat.\n" "2. Coba **📄 Analisis RUU** untuk melihat audit pasal + guardrail etika.\n" "3. Gunakan **🔍 Riset Hukum** atau **✍️ Draf Dokumen** untuk workflow staf ahli.\n" "4. **⚙️ Pengaturan** hanya diperlukan jika ingin mengganti model/token.\n\n" "---\n" ) gr.Markdown( "### 🎬 Panduan Video\n\n" "Tonton video demo Legawa untuk melihat cara kerja setiap fitur:\n\n" "▶️ **[Video Panduan Lengkap](https://www.youtube.com/watch?v=jgYXyij1P9Q)** " "*— 51 detik, animasi penuh 5 fitur + arsitektur SMALL-BIG + etika*\n\n" "---\n" ) gr.Markdown( "### ⚖️ Nilai-nilai Demokrasi & HAM\n\n" "Setiap output Legawa diperiksa terhadap 4 pilar:\n" "- **Kedaulatan Rakyat** — apakah keputusan berpihak pada rakyat?\n" "- **Prinsip Demokrasi** — apakah checks and balances terjaga?\n" "- **Hak Asasi Manusia** — apakah HAM dilindungi?\n" "- **Etika Politik** — apakah ada do's and don'ts untuk legislator?\n\n" "*Inisiatif ini terinspirasi dari masukan Taufik Basari, S.H., S.Hum., LL.M., " "anggota DPR RI 2019–2024.*\n" ) # ─── Tab 2: Analisis RUU ────────────────────────────────── with gr.TabItem("📄 Analisis RUU"): gr.Markdown( "Upload atau tempel teks RUU untuk dianalisis pasal-per-pasal." ) with gr.Row(): with gr.Column(scale=2): ruu_text = gr.Textbox( label="Teks RUU", placeholder="Tempel teks RUU di sini, atau upload file...", lines=12, ) with gr.Column(scale=1): ruu_file = gr.File( label="Upload PDF/TXT", file_types=[".pdf", ".txt", ".md"], ) with gr.Row(): ruu_btn = gr.Button("Analisis RUU", variant="primary", size="lg") ruu_act = _activity_chatbot() ruu_out = gr.Markdown(label="Hasil Analisis") ruu_file.change( fn=handle_file_upload, inputs=[ruu_file], outputs=[ruu_text], ) gr.Examples( examples=[[RUU_EXAMPLE]], inputs=[ruu_text], label="Contoh cepat", ) ruu_btn.click( fn=agent_analyze, inputs=[ ruu_text, big_url, big_key, small_url, small_key, pasal_token, ], outputs=[ruu_act, ruu_out], ) # ─── Tab 2: Riset Hukum ──────────────────────────────────── with gr.TabItem("🔍 Riset Hukum"): gr.Markdown("Cari peraturan terkait topik tertentu di pasal.id.") with gr.Row(): riset_topic = gr.Textbox( label="Topik Riset", placeholder="Contoh: perlindungan data pribadi sektor kesehatan", lines=3, scale=3, ) with gr.Row(): riset_btn = gr.Button("Riset Hukum", variant="primary", size="lg") riset_act = _activity_chatbot() riset_out = gr.Markdown(label="Memo Riset") gr.Examples( examples=[ ["perlindungan data pribadi pasien di rumah sakit"], ["kewenangan DPRD dalam pengawasan banjir dan drainase kota"], ], inputs=[riset_topic], label="Contoh cepat", ) riset_btn.click( fn=agent_research, inputs=[ riset_topic, big_url, big_key, small_url, small_key, pasal_token, ], outputs=[riset_act, riset_out], ) # ─── Tab 3: Draf Dokumen ────────────────────────────────── with gr.TabItem("✍️ Draf Dokumen"): gr.Markdown("Susun pidato, naskah akademik, memo kebijakan, atau siaran pers.") with gr.Row(): draft_kind = gr.Dropdown( label="Jenis Dokumen", choices=[ ("Pidato", "pidato"), ("Naskah Akademik", "naskah_akademik"), ("Memo Kebijakan", "memo_kebijakan"), ("Siaran Pers", "siaran_pers"), ], value="memo_kebijakan", ) draft_topic = gr.Textbox( label="Topik", placeholder="Contoh: urgensi RUU Masyarakat Adat", lines=2, scale=2, ) with gr.Row(): draft_extra = gr.Textbox( label="Instruksi Tambahan (opsional)", placeholder="fokus pada aspek fiskal...", lines=2, scale=2, ) with gr.Row(): draft_research = gr.Checkbox( label="Sertakan riset hukum pendukung", value=True, ) with gr.Row(): draft_btn = gr.Button("Susun Naskah", variant="primary", size="lg") draft_act = _activity_chatbot() draft_out = gr.Markdown(label="Draf Dokumen") gr.Examples( examples=[ ["memo_kebijakan", "langkah DPRD mempercepat perbaikan drainase kota", "buat ringkas untuk rapat komisi", True], ["siaran_pers", "perlindungan data pribadi pasien", "nada tegas tapi empatik", True], ], inputs=[draft_kind, draft_topic, draft_extra, draft_research], label="Contoh cepat", ) draft_btn.click( fn=agent_draft, inputs=[ draft_kind, draft_topic, draft_extra, draft_research, big_url, big_key, small_url, small_key, pasal_token, ], outputs=[draft_act, draft_out], ) # ─── Tab 4: Surat Konstituen ─────────────────────────────── with gr.TabItem("📬 Surat Konstituen"): gr.Markdown( "Tempel surat/email dari konstituen untuk triase dan draft balasan." ) surat_text = gr.Textbox( label="Surat Konstituen", placeholder="Tempel surat konstituen di sini...", lines=10, ) with gr.Row(): surat_verify = gr.Checkbox( label="Verifikasi peraturan yang disebut di pasal.id", value=True, ) with gr.Row(): surat_btn = gr.Button("Triase & Balas", variant="primary", size="lg") surat_act = _activity_chatbot() surat_out = gr.Markdown(label="Hasil") gr.Examples( examples=[[SURAT_EXAMPLE, True], [SURAT_MBG_EXAMPLE, True]], inputs=[surat_text, surat_verify], label="Contoh cepat untuk juri", ) surat_btn.click( fn=agent_surat, inputs=[ surat_text, surat_verify, big_url, big_key, small_url, small_key, pasal_token, ], outputs=[surat_act, surat_out], ) # ─── Tab 5: Pengaturan ────────────────────────────────────── with gr.TabItem("⚙️ Pengaturan"): gr.Markdown( "### Pengaturan Opsional\n\n" "Untuk juri: langsung pakai tab demo. Halaman ini hanya untuk mengganti model, " "memasukkan token sendiri, atau menguji koneksi. Default Legawa memakai " "**Qwen/Qwen3.5-9B untuk BIG dan SMALL** agar tetap jauh di bawah batas 32B parameter.\n" ) with gr.Accordion("🔧 Advanced: model, token, dan endpoint", open=False): gr.Markdown( "**HF Token**: [buat read-only token](https://huggingface.co/settings/tokens). " "**pasal.id Token**: [daftar di pasal.id](https://pasal.id). " "Custom endpoint mendukung llama.cpp / vLLM / OpenAI-compatible." ) with gr.Group(): gr.Markdown("### 🧠 LLM BIG (sintesis, drafting)") s_big_url = gr.Textbox(label="Model ID / URL", value=HF_BIG_MODEL) s_big_key = gr.Textbox( label="API Key", type="password", value="", placeholder="Kosongkan — pakai HF_TOKEN env var", ) s_big_model = gr.Textbox( label="Model Name", value="Qwen3.5-9B", ) with gr.Group(): gr.Markdown("### 🧠 LLM SMALL (klasifikasi, ekstraksi)") s_small_url = gr.Textbox(label="Model ID / URL", value=HF_SMALL_MODEL) s_small_key = gr.Textbox( label="API Key", type="password", value="", placeholder="Kosongkan — pakai HF_TOKEN env var", ) s_small_model = gr.Textbox( label="Model Name", value="Qwen3.5-9B", ) with gr.Group(): gr.Markdown("### 📜 pasal.id") s_pasal_token = gr.Textbox( label="API Token", type="password", value="", placeholder="Kosongkan — cari peraturan tidak akan jalan", ) with gr.Group(): gr.Markdown("### ⚙️ Lainnya") s_temp = gr.Slider( label="Temperature", minimum=0.0, maximum=1.0, step=0.05, value=0.3, ) s_max_tokens = gr.Slider( label="Max Tokens", minimum=512, maximum=8192, step=256, value=4096, ) s_strict = gr.Checkbox( label="Strict citations (tolak draft jika sitasi tidak terverifikasi)", value=True, ) with gr.Row(): save_btn = gr.Button("Simpan & Uji Koneksi", variant="primary") health_out = gr.Markdown(label="Status Koneksi") def save_settings( bu, bk, bm, su, sk, sm, pt, temp, mt, strict, ): # Push settings to the hidden state boxes return bu, bk, su, sk, pt, gr.update() # Save button writes to hidden state AND runs health check save_btn.click( fn=lambda bu, bk, bm, su, sk, sm, pt, temp, mt, strict: ( bu, bk, su, sk, pt, agent_health(bu, bk, su, sk, pt), ), inputs=[ s_big_url, s_big_key, s_big_model, s_small_url, s_small_key, s_small_model, s_pasal_token, s_temp, s_max_tokens, s_strict, ], outputs=[big_url, big_key, small_url, small_key, pasal_token, health_out], ) # ─── Tab 6: Kredit — Attribution ────────────────────────── with gr.TabItem("👤 Kredit"): gr.Markdown( "### 🗣️ Masukan dari Legislator\n\n" "Fitur **Nilai-nilai Demokrasi & HAM** dikembangkan berdasarkan " "masukan dari:\n\n" "**Taufik Basari, S.H., S.Hum., LL.M.**\n" "*Anggota Dewan Perwakilan Rakyat Republik Indonesia*\n" "*Masa jabatan: 1 Oktober 2019 – 30 September 2024*\n\n" "> *\"AI agent nya mesti dilatih utk kasih do's and don'ts, " "konsep kedaulatan rakyat, prinsip demokrasi dan HAM serta " "mengingatkan pentingnya political ethics di setiap jawaban " "yg diberikan. Jd kalau mau pake bahan dari AI, legislator " "tsb harus sertakan jg nilai2 itu.\"\n" "> — Taufik Basari, 29 Mei 2026*\n\n" "---\n" "[🔗 X/Twitter](https://x.com/taufikbasari) | " "[Wikipedia](https://id.wikipedia.org/wiki/Taufik_Basari)\n\n" "---\n" "### 🔌 Database Peraturan\n\n" "Data peraturan Indonesia disediakan oleh **[pasal.id](https://pasal.id)** " "— API database peraturan perundang-undangan Indonesia oleh " "[@ilhamfputra](https://x.com/ilhamfputra).\n\n" "---\n" "### 🏛️ Legawa\n\n" "*Small models, big adventure* 🏕️\n\n" "Dibangun untuk [Build Small Hackathon](https://huggingface.co/build-small-hackathon) " "oleh [@pebaryan](https://x.com/pebaryan).\n\n" "Kode terbuka di [GitHub](https://github.com/pebaryan/Legawa).\n\n" ) gr.Markdown( f"---\n" f"**Legawa** — *small models, big adventure* 🏕️ | " f"[GitHub](https://github.com/pebaryan/Legawa) | " f"[pasal.id](https://pasal.id)" ) return app # ── Entry point ───────────────────────────────────────────────────────── app = build_app() app.queue(default_concurrency_limit=5) if __name__ == "__main__": app.launch()