109 lines
4.0 KiB
Plaintext
109 lines
4.0 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)
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# 1. Load Model
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# Pastikan file model terbaru sudah kamu download dan ganti namanya menjadi ini
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MODEL_PATH = 'train_part4.keras'
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model = load_model(MODEL_PATH)
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# Pastikan urutan kelas sesuai dengan test_generator.class_indices
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# Tadi di Colab urutannya: honey, natural, wash (bukan washed)
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classes = ['honey', 'natural', 'wash']
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# 2. Fungsi Preprocessing Robust (Sesuai eksperimen terakhir di Colab)
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def preprocess_image(input_img):
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# A. Hapus Background
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output_rgba = remove(input_img)
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# B. Auto-Crop ke Bounding Box (Fokus ke biji kopi saja)
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bbox = output_rgba.getbbox()
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if bbox:
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output_rgba = output_rgba.crop(bbox)
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# C. Center Padding (Membuat kanvas hitam persegi)
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max_dim = max(output_rgba.size)
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black_bg = Image.new("RGB", (max_dim, max_dim), (0, 0, 0))
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# Hitung posisi agar biji kopi di tengah
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paste_x = (max_dim - output_rgba.size[0]) // 2
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paste_y = (max_dim - output_rgba.size[1]) // 2
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# Tempelkan gambar transparan ke latar hitam
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black_bg.paste(output_rgba, (paste_x, paste_y), mask=output_rgba.split()[3])
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# D. Resize ke 224x224 (Input EfficientNetB0)
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final_img = black_bg.resize((224, 224))
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# E. Konversi ke Array
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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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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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# Load gambar asli sebagai RGBA agar rembg bekerja maksimal
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img = Image.open(image_file.stream).convert("RGBA")
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# Jalankan Preprocessing Robust
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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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prob_details = "Analisis Probabilitas Model:\n"
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for i, cls_name in enumerate(classes):
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prob_details += f"- {cls_name.capitalize()}: {round(float(preds[i]) * 100, 2)}%\n"
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prob_details = prob_details.strip()
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# --- LOGIKA PENYARINGAN (Threshold & Entropy) ---
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# 1. Jika entropy tinggi (Model bingung parah)
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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": f"Sistem mendeteksi ketidakjelasan. Pastikan objek adalah biji kopi tunggal dengan pencahayaan cukup.\n\n{prob_details}",
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"entropy_score": round(float(entropy), 4)
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}), 200
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# 2. Jika Keyakinan Rendah (Di bawah 70%)
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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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"label": predicted_class,
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"confidence": str(round(confidence * 100, 2)),
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"pesan": f"Model menduga ini proses {predicted_class}, namun tingkat keyakinan rendah.\n\n{prob_details}"
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}), 200
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# 3. Lolos Verifikasi (Status SUKSES)
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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)),
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"pesan": f"Biji kopi teridentifikasi sebagai proses {predicted_class}.\n\n{prob_details}"
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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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# Pastikan port sesuai dengan yang dibuka di firewall server/local kamu
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app.run(host='0.0.0.0', port=5001, debug=False) |