"""
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 -------------------------------------
@app.on_event("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 -----------------------------------
@app.get("/health")
def health():
    return {
        "status": "ok",
        "service": "Taxy ML API",
        "model_loaded": inference._model is not None,
        "genai": genai.status(),
    }


@app.post("/predict")
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"]}


@app.post("/explanation")
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"]}


@app.post("/chat")
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)