81 lines
3.0 KiB
Plaintext
81 lines
3.0 KiB
Plaintext
import os
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import numpy as np
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from flask import Flask, request, jsonify
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from tensorflow.keras.models import load_model
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from tensorflow.keras.preprocessing.image import img_to_array
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from PIL import Image
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from rembg import remove
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from flask_cors import CORS
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app = Flask(__name__)
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CORS(app) # Izinkan semua origin mengakses API ini
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# 1. Load Model (Pastikan file ini satu folder dengan app.py nanti)
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MODEL_PATH = 'best_model.keras'
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model = load_model(MODEL_PATH)
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classes = ['honey', 'natural', 'washed']
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# 2. Fungsi Preprocessing (Harus sama persis dengan saat training)
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def preprocess_image(input_img):
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# Hapus background & buat latar hitam
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output_rgba = remove(input_img)
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black_bg = Image.new("RGB", output_rgba.size, (0, 0, 0))
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black_bg.paste(output_rgba, mask=output_rgba.split()[3])
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# Resize ke 224x224 sesuai EfficientNetB0
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final_img = black_bg.resize((224, 224))
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# Konversi ke Array dan tambah dimensi batch (1, 224, 224, 3)
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img_array = img_to_array(final_img)
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img_array = np.expand_dims(img_array, axis=0)
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return img_array
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@app.route('/predict', methods=['POST'])
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def predict():
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# Menyesuaikan dengan formData.append('image', ...) dari Scanner.vue
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if 'image' not in request.files:
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return jsonify({"status": "ERROR", "message": "File gambar tidak ditemukan"}), 400
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image_file = request.files['image']
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try:
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img = Image.open(image_file.stream).convert("RGBA")
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processed_img = preprocess_image(img)
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# Prediksi
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preds = model.predict(processed_img)[0]
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confidence = float(np.max(preds))
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predicted_class = classes[np.argmax(preds)]
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# Hitung Entropy (Mengukur tingkat kebingungan model)
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entropy = -np.sum(preds * np.log(preds + 1e-9))
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# --- LOGIKA PENYARINGAN (Threshold & Entropy) ---
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# Jika entropy > 0.8, berarti model bingung (probabilitas terbagi-bagi)
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if entropy > 0.85:
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return jsonify({
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"status": "DITOLAK",
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"pesan": "Sistem bingung. Mohon pastikan foto adalah biji kopi tunggal yang jelas.",
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"entropy_score": round(entropy, 4)
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}), 200
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# Jika keyakinan di bawah 75% (threshold)
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if confidence < 0.70:
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return jsonify({
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"status": "TIDAK YAKIN",
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"pesan": f"Model menduga {predicted_class}, tapi kurang yakin ({confidence*100:.1f}%).",
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"confidence": str(round(confidence * 100, 2)) # <--- BUNGKUS DENGAN str() DI SINI
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}), 200
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# Lolos verifikasi
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return jsonify({
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"status": "SUKSES",
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"label": predicted_class,
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"confidence": str(round(confidence * 100, 2)), # <--- BUNGKUS DENGAN str() DI SINI
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"pesan": f"Biji kopi teridentifikasi sebagai {predicted_class}."
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}), 200
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except Exception as e:
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return jsonify({"status": "ERROR", "message": str(e)}), 500
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if __name__ == '__main__':
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app.run(host='0.0.0.0', port=5001) |