import os
import numpy as np
from flask import Flask, request, jsonify
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
from PIL import Image
from rembg import remove
from flask_cors import CORS

app = Flask(__name__)
CORS(app)  # Izinkan semua origin mengakses API ini

# 1. Load Model (Pastikan file ini satu folder dengan app.py nanti)
MODEL_PATH = 'best_model.keras'
model = load_model(MODEL_PATH)
classes = ['honey', 'natural', 'washed']

# 2. Fungsi Preprocessing (Harus sama persis dengan saat training)
def preprocess_image(input_img):
    # Hapus background & buat latar hitam
    output_rgba = remove(input_img)
    black_bg = Image.new("RGB", output_rgba.size, (0, 0, 0))
    black_bg.paste(output_rgba, mask=output_rgba.split()[3]) 
    
    # Resize ke 224x224 sesuai EfficientNetB0
    final_img = black_bg.resize((224, 224))
    
    # Konversi ke Array dan tambah dimensi batch (1, 224, 224, 3)
    img_array = img_to_array(final_img)
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

@app.route('/predict', methods=['POST'])
def predict():
    # Menyesuaikan dengan formData.append('image', ...) dari Scanner.vue
    if 'image' not in request.files:
        return jsonify({"status": "ERROR", "message": "File gambar tidak ditemukan"}), 400
        
    image_file = request.files['image']
    try:
        img = Image.open(image_file.stream).convert("RGBA")
        processed_img = preprocess_image(img)
        
        # Prediksi
        preds = model.predict(processed_img)[0]
        confidence = float(np.max(preds))
        predicted_class = classes[np.argmax(preds)]
        
        # Hitung Entropy (Mengukur tingkat kebingungan model)
        entropy = -np.sum(preds * np.log(preds + 1e-9))
        
        # --- LOGIKA PENYARINGAN (Threshold & Entropy) ---
        # Jika entropy > 0.8, berarti model bingung (probabilitas terbagi-bagi)
        if entropy > 0.85:
            return jsonify({
                "status": "DITOLAK",
                "pesan": "Sistem bingung. Mohon pastikan foto adalah biji kopi tunggal yang jelas.",
                "entropy_score": round(entropy, 4)
            }), 200

        # Jika keyakinan di bawah 75% (threshold)
        if confidence < 0.70:
            return jsonify({
                "status": "TIDAK YAKIN",
                "pesan": f"Model menduga {predicted_class}, tapi kurang yakin ({confidence*100:.1f}%).",
                "confidence": str(round(confidence * 100, 2)) # <--- BUNGKUS DENGAN str() DI SINI
            }), 200

        # Lolos verifikasi
        return jsonify({
            "status": "SUKSES",
            "label": predicted_class,
            "confidence": str(round(confidence * 100, 2)), # <--- BUNGKUS DENGAN str() DI SINI
            "pesan": f"Biji kopi teridentifikasi sebagai {predicted_class}."
        }), 200

    except Exception as e:
        return jsonify({"status": "ERROR", "message": str(e)}), 500

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5001)