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| """ | |
| Taxy ML — REST API Mandiri (FastAPI) | |
| ==================================== | |
| Melayani model Deep Learning + fitur Generative AI (Gemini) pada port 5001. | |
| Endpoints: | |
| GET /health -> status model & GenAI | |
| POST /predict -> prediksi risiko ketidakpatuhan (MODEL ML) + penjelasan GenAI | |
| POST /explanation -> penjelasan hasil kalkulasi pajak (GenAI) [dipakai dashboard] | |
| POST /chat -> chatbot Q&A pajak (GenAI) [fitur sekunder] | |
| Menjalankan: | |
| uvicorn serve:app --host 0.0.0.0 --port 5001 | |
| (atau) python serve.py | |
| Backend Node (be/controllers/aiController.js) sudah menunjuk ke | |
| http://127.0.0.1:5001 dan punya fallback bila server ini mati. | |
| """ | |
| import os | |
| from typing import Optional, Dict, Any | |
| from fastapi import FastAPI | |
| from fastapi.middleware.cors import CORSMiddleware | |
| from pydantic import BaseModel | |
| # Muat .env sederhana (tanpa dependency tambahan) | |
| def _load_dotenv(): | |
| path = os.path.join(os.path.dirname(os.path.abspath(__file__)), ".env") | |
| if os.path.exists(path): | |
| for line in open(path, encoding="utf-8"): | |
| line = line.strip() | |
| if line and not line.startswith("#") and "=" in line: | |
| k, v = line.split("=", 1) | |
| os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'")) | |
| _load_dotenv() | |
| import inference | |
| import genai | |
| app = FastAPI(title="Taxy ML API", version="1.0.0", | |
| description="Deep Learning Tax-Compliance Risk Profiler + Gemini GenAI") | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=["*"], allow_methods=["*"], allow_headers=["*"], | |
| ) | |
| # ----------------------------- Schemas ------------------------------------- | |
| class PredictRequest(BaseModel): | |
| usia: float = 30 | |
| gaji_bulanan: float = 0 | |
| skor_literasi_pajak: float = 50 | |
| punya_npwp: int = 0 | |
| aktif_taxy: int = 0 | |
| status_ptkp: str = "TK/0" | |
| pekerjaan: str = "Junior Staff" | |
| kategori_pendapatan: Optional[str] = None | |
| class ExplanationRequest(BaseModel): | |
| taxData: Dict[str, Any] | |
| category: Optional[str] = "Pajak" | |
| class ChatRequest(BaseModel): | |
| message: str | |
| context: Optional[Dict[str, Any]] = None | |
| # ----------------------------- Startup ------------------------------------- | |
| def _startup(): | |
| inference.load() # muat model lebih awal | |
| genai.init_genai() # coba aktifkan Gemini (silent jika tak ada key) | |
| print("[Taxy ML API] Model dimuat. GenAI:", genai.status()) | |
| # ----------------------------- Endpoints ----------------------------------- | |
| def health(): | |
| return { | |
| "status": "ok", | |
| "service": "Taxy ML API", | |
| "model_loaded": inference._model is not None, | |
| "genai": genai.status(), | |
| } | |
| def predict(req: PredictRequest): | |
| """Prediksi risiko ketidakpatuhan pajak dari profil pengguna (MODEL ML) | |
| + penjelasan natural dari Gemini (fallback lokal jika offline).""" | |
| profile = req.model_dump() | |
| pred = inference.predict(profile) | |
| nice = (f"usia {profile['usia']:.0f}, gaji {profile['gaji_bulanan']:,.0f}, " | |
| f"NPWP={'ya' if profile['punya_npwp'] else 'tidak'}, " | |
| f"literasi {profile['skor_literasi_pajak']:.0f}/100, " | |
| f"aktif Taxy={'ya' if profile['aktif_taxy'] else 'tidak'}") | |
| expl = genai.explain_risk(nice, pred) | |
| return {"success": True, "prediction": pred, | |
| "explanation": expl["explanation"], "source": expl["source"]} | |
| def explanation(req: ExplanationRequest): | |
| """Penjelasan hasil kalkulasi pajak (dipakai 'Insight Pajak AI' di dashboard).""" | |
| out = genai.explain_tax(req.taxData, req.category) | |
| return {"success": True, "data": {"explanation": out["explanation"]}, | |
| "source": out["source"]} | |
| def chat(req: ChatRequest): | |
| """Chatbot Q&A pajak (fitur sekunder Generative AI).""" | |
| out = genai.chat(req.message, req.context) | |
| return {"success": True, "reply": out["reply"], "source": out["source"]} | |
| if __name__ == "__main__": | |
| import uvicorn | |
| # PORT dari environment (Hugging Face Spaces=7860, Cloud Run=8080, lokal=5001) | |
| port = int(os.environ.get("PORT", 5001)) | |
| uvicorn.run(app, host="0.0.0.0", port=port) | |