Update MobileNetV2 4 kelas

app/Models/Prediction.php

@@ -0,0 +1,10 @@
+<?php
+
+namespace App\Models;
+
+use Illuminate\Database\Eloquent\Model;
+
+class Prediction extends Model
+{
+    //
+}

database/migrations/2026_06_07_090437_create_predictions_table.php

@@ -0,0 +1,27 @@
+<?php
+
+use Illuminate\Database\Migrations\Migration;
+use Illuminate\Database\Schema\Blueprint;
+use Illuminate\Support\Facades\Schema;
+
+return new class extends Migration
+{
+    /**
+     * Run the migrations.
+     */
+    public function up(): void
+    {
+        Schema::create('predictions', function (Blueprint $table) {
+            $table->id();
+            $table->timestamps();
+        });
+    }
+
+    /**
+     * Reverse the migrations.
+     */
+    public function down(): void
+    {
+        Schema::dropIfExists('predictions');
+    }
+};

python_ai/app.py

@@ -1,4 +1,5 @@
 from flask import Flask, request, jsonify
+from flask_cors import CORS
 import os
 from datetime import datetime
 import numpy as np
@@ -6,30 +7,33 @@ from PIL import Image
 from tensorflow.keras.models import load_model
 
 app = Flask(__name__)
+CORS(app)
 
 UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), 'uploads')
 os.makedirs(UPLOAD_FOLDER, exist_ok=True)
 
-MODEL_PATH = os.path.join(os.path.dirname(__file__), 'model_padi.h5')
+# 1. Sesuaikan nama file model!
-CLASS_NAMES = ['Blast', 'Blight', 'Tungro']
+MODEL_PATH = os.path.join(os.path.dirname(__file__), 'model_padi_blast.h5')
+
+# 2. Sesuaikan dengan urutan class di Colab kamu (4 class)
+CLASS_NAMES = ['Healthy', 'Blast', 'Blight', 'Tungro']
 
 model = None
 try:
     model = load_model(MODEL_PATH)
-    print('Model dimuat dari', MODEL_PATH)
+    print('Model berhasil dimuat dari', MODEL_PATH)
 except Exception as e:
     print('Gagal memuat model:', e)
 
-
 def preprocess_image(path):
     image = Image.open(path).convert('RGB')
     image = image.resize((150, 150))
     image_array = np.array(image).astype('float32') / 255.0
     return np.expand_dims(image_array, axis=0)
 
-
 @app.route('/prediksi', methods=['POST'])
 def prediksi():
+    # 3. Menerima request dengan key 'gambar'
     if 'gambar' not in request.files:
         return jsonify({'error': 'Tidak ada file gambar'}), 400
 
@@ -45,11 +49,11 @@ def prediksi():
     print('GAMBAR DITERIMA:', filename)
 
     if model is None:
-        return jsonify({'error': 'Model tidak tersedia. Pastikan model_padi.h5 ada dan Flask dapat memuatnya.'}), 500
+        return jsonify({'error': 'Model tidak tersedia. Pastikan file .h5 ada.'}), 500
 
     try:
         image_array = preprocess_image(filepath)
-        predictions = model.predict(image_array)
+        predictions = model.predict(image_array, verbose=0)
         predicted_index = int(np.argmax(predictions, axis=1)[0])
         confidence = float(np.max(predictions, axis=1)[0]) * 100.0
         label = CLASS_NAMES[predicted_index] if predicted_index < len(CLASS_NAMES) else f'Kelas {predicted_index + 1}'
@@ -63,4 +67,7 @@ def prediksi():
         return jsonify({'error': 'Terjadi kesalahan saat memprediksi gambar.'}), 500
 
 if __name__ == '__main__':
-    app.run(debug=True)
+    print('=' * 50)
+    print('Klasifikasi Penyakit Daun Padi')
+    print('=' * 50)
+    app.run(host='127.0.0.1', port=5000, debug=False)

python_ai/requirements.txt

@@ -1,4 +1,5 @@
 flask
+flask-cors
 tensorflow
 pillow
 numpy