129 lines
5.1 KiB
Python
129 lines
5.1 KiB
Python
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 tensorflow.keras.applications.mobilenet_v2 import preprocess_input # WAJIB UNTUK MOBILENETV2
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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) # Agar bisa diakses dari Frontend Vue.js atau Laravel
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# ==============================================================================
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# 1. KONFIGURASI MODEL & KELAS
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# ==============================================================================
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# Pastikan file model .keras hasil training sudah dipindahkan ke folder ini
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MODEL_PATH = 'Arsitektur_MobileNetV2.keras'
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if os.path.exists(MODEL_PATH):
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model = load_model(MODEL_PATH)
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print(f"✅ Model {MODEL_PATH} berhasil dimuat.")
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else:
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print(f"❌ ERROR: File {MODEL_PATH} tidak ditemukan!")
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# Urutan kelas sesuai dengan training di Colab
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classes = ['honey', 'natural', 'wash']
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# ==============================================================================
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# 2. FUNGSI PREPROCESSING (Sesuai Standar MobileNetV2 + Rembg)
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# ==============================================================================
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def preprocess_robust_mobilenet(input_img):
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# A. Hapus Background (Mengubah objek acak menjadi transparan)
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output_rgba = remove(input_img)
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# B. Auto-Crop ke Bounding Box (Menghilangkan sisa ruang kosong)
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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 1:1)
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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 tepat 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 menggunakan mask (untuk menjaga transparansi)
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black_bg.paste(output_rgba, (paste_x, paste_y), mask=output_rgba.split()[3])
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# D. Resize Standar MobileNetV2 (224x224)
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final_img = black_bg.resize((224, 224))
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# E. Konversi ke Array & Normalisasi Khusus MobileNetV2 (-1 hingga 1)
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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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img_array = preprocess_input(img_array) # INI KUNCINYA
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return img_array
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# ==============================================================================
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# 3. ENDPOINT API PREDIKSI
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# ==============================================================================
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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 sebagai RGBA agar rembg bekerja maksimal
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img = Image.open(image_file.stream).convert("RGBA")
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# Jalankan Preprocessing
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processed_img = preprocess_robust_mobilenet(img)
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# Prediksi menggunakan model
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preds = model.predict(processed_img, verbose=0)[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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# Detail Probabilitas untuk ditampilkan di Frontend
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prob_details = {}
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for i, cls_name in enumerate(classes):
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prob_details[cls_name] = round(float(preds[i]) * 100, 2)
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# --- LOGIKA PENYARINGAN STATUS ---
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# 1. Kasus: Gambar Tidak Jelas (Entropy Terlalu Tinggi)
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if entropy > 0.85:
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return jsonify({
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"status": "DITOLAK",
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"label": "Tidak terdeteksi",
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"confidence": str(round(confidence * 100, 2)),
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"pesan": "Sistem bingung. Pastikan foto hanya berisi satu biji kopi dengan latar belakang yang tidak terlalu ramai.",
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"details": prob_details
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}), 200
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# 2. Kasus: Model Ditolak (Confidence < 70%)
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if confidence < 0.70:
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return jsonify({
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"status": "DITOLAK",
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"label": "Tidak terdeteksi",
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"confidence": str(round(confidence * 100, 2)),
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"pesan": f"Sistem ditolak. Tingkat keyakinan hanya {str(round(confidence * 100, 2))}%.",
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"details": prob_details
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}), 200
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# 3. Kasus: Berhasil (Sukses, Confidence >= 70%)
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return jsonify({
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"status": "BERHASIL",
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"label": predicted_class,
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# DIBUNGKUS STRING AGAR .replace() DI VUE.JS TIDAK ERROR
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"confidence": str(round(confidence * 100, 2)),
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"pesan": f"Biji kopi berhasil diidentifikasi sebagai proses {predicted_class.upper()}.",
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"details": 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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# Jalankan pada port 5001 (sesuaikan dengan settingan Laravel/Vue kamu)
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app.run(host='0.0.0.0', port=5001, debug=False) |