import cv2
import numpy as np
from flask import Flask, request
from ultralytics import YOLO
import firebase_admin
from firebase_admin import credentials, db
import time
import base64

# ── 1. Inisialisasi Firebase ──────────────────────────────────────────────────
cred = credentials.Certificate("serviceAccountKey.json")
firebase_admin.initialize_app(cred, {
    'databaseURL': 'https://smart-donation-adff1-default-rtdb.asia-southeast1.firebasedatabase.app/'
    # Firebase Storage TIDAK dipakai — foto disimpan sebagai base64 di Realtime DB
    # Jika suatu saat upgrade ke Blaze, tambahkan:
    # 'storageBucket': 'smart-donation-adff1.appspot.com'
})

# ── 2. Load Model YOLO ────────────────────────────────────────────────────────
model = YOLO("best.pt")
app = Flask(__name__)

print("Class model:", model.names)  # Verifikasi nama class saat startup

NOMINAL_MAP = {
    '1':   1000,
    '2':   2000,
    '5':   5000,
    '10':  10000,
    '20':  20000,
    '50':  50000,
    '100': 100000,
}


# ── 3. Helper: Simpan foto sebagai Base64 ke Realtime Database ───────────────
def simpan_foto_terakhir(img_bgr, deteksi_label):
    """
    Encode foto hasil deteksi ke Base64 lalu simpan langsung ke Realtime DB.
    Tidak butuh Firebase Storage — kompatibel dengan plan Spark (gratis).
    Dashboard web bisa langsung baca via <img src="data:image/jpeg;base64,...">
    """
    try:
        # Render bounding box dari YOLO
        results   = model(img_bgr, conf=0.7, verbose=False)
        annotated = results[0].plot()

        # Encode ke JPEG bytes lalu konversi ke Base64 string
        # Quality 60 → ukuran ~30-60KB, cukup untuk preview dashboard
        _, buffer   = cv2.imencode('.jpg', annotated, [cv2.IMWRITE_JPEG_QUALITY, 60])
        img_base64  = base64.b64encode(buffer).decode('utf-8')
        foto_data   = "data:image/jpeg;base64," + img_base64

        # Simpan ke Realtime DB — selalu overwrite node yang sama
        db.reference('smart_donation_box/last_detection').set({
            'foto_url':  foto_data,        # Dashboard langsung pakai nilai ini di <img src>
            'label':     deteksi_label,
            'timestamp': int(time.time() * 1000)
        })

        print(f"📸 Foto tersimpan ke Realtime DB ({len(img_base64) // 1024} KB)")

    except Exception as e:
        print(f"⚠️  Gagal simpan foto: {e}")


# ── 4. Helper: Catat transaksi donasi & update summary ───────────────────────
def update_firebase(nominal, confidence):
    ref_log     = db.reference('smart_donation_box/donasi_log')
    ref_summary = db.reference('smart_donation_box/summary')

    # Tambah entri baru di donasi_log
    ref_log.push({
        'nominal':    nominal,
        'confidence': round(float(confidence), 2),
        'timestamp':  int(time.time() * 1000)
    })

    # Update summary (total & saldo)
    summary         = ref_summary.get() or {}
    old_total       = summary.get('total_donasi', 0)
    old_pengeluaran = summary.get('total_pengeluaran', 0)
    new_total       = old_total + nominal
    new_saldo       = new_total - old_pengeluaran

    ref_summary.update({
        'total_donasi':    new_total,
        'donasi_hari_ini': summary.get('donasi_hari_ini', 0) + nominal,
        'saldo':           new_saldo
    })

    print(f"✅ Donasi Rp {nominal:,} | Confidence {confidence:.0%} | Total Rp {new_total:,}")


# ── 5. Endpoint /predict ──────────────────────────────────────────────────────
@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Terima raw JPEG bytes dari ESP32-CAM
        file  = request.data
        nparr = np.frombuffer(file, np.uint8)
        img   = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

        if img is None:
            print("❌ Gagal decode gambar dari ESP32")
            return "Gagal decode gambar", 400

        print(f"📥 Frame diterima: {len(file) // 1024} KB")

        # Jalankan prediksi YOLO
        results    = model.predict(img, conf=0.7, verbose=False)
        detections = []

        for r in results:
            for box in r.boxes:
                label = model.names[int(box.cls[0])]
                conf  = float(box.conf[0])

                nominal = NOMINAL_MAP.get(label)
                if nominal is not None:
                    update_firebase(nominal, conf)
                    detections.append(f"Rp {nominal:,}")
                    print(f"🎯 Terdeteksi: label='{label}' → Rp {nominal:,} ({conf:.0%})")
                else:
                    print(f"⚠️  Label '{label}' tidak ada di NOMINAL_MAP, dilewati")
                    continue

        # Simpan foto ke DB (selalu, baik ada deteksi maupun tidak)
        label_str = ', '.join(detections) if detections else 'Tidak ada deteksi'
        simpan_foto_terakhir(img, label_str)

        if not detections:
            print("🔍 Tidak ada uang terdeteksi")
            return "Tidak ada objek terdeteksi", 200

        print(f"🎯 Terdeteksi: {label_str}")
        return f"Berhasil: {label_str}", 200

    except Exception as e:
        print(f"❌ Error di /predict: {e}")
        return str(e), 500


# ── 6. Health check endpoint (opsional, untuk test koneksi) ──────────────────
@app.route('/ping', methods=['GET'])
def ping():
    return "Smart Donation Box Server OK", 200


# ── 7. Jalankan Server ────────────────────────────────────────────────────────
if __name__ == '__main__':
    print("=" * 50)
    print("  Smart Donation Box — Flask Server")
    print("  Listening on http://0.0.0.0:5000")
    print("  Endpoint: POST /predict")
    print("=" * 50)
    app.run(host='0.0.0.0', port=5000, debug=False)