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hmmm ya gt deh

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adindacintya 2026-06-07 22:45:05 +07:00
parent c0d803fe21 511e08d204
commit e7b0d53770
4 changed files with 81 additions and 73 deletions
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10
app/Models/Prediction.php Normal file
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@ -0,0 +1,10 @@
<?php
namespace App\Models;
use Illuminate\Database\Eloquent\Model;
class Prediction extends Model
{
//
}

27
database/migrations/2026_06_07_090437_create_predictions_table.php Normal file
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@ -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');
}
};

116
python_ai/app.py
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@ -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
@ -6,12 +7,15 @@ 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)
# 1. Sesuaikan nama file model!
MODEL_PATH = os.path.join(os.path.dirname(__file__), 'model_padi.h5')
# 2. Sesuaikan dengan urutan class di Colab kamu (4 class)
CLASS_NAMES = [
'Healthy',
'Blast',
@ -19,23 +23,34 @@ CLASS_NAMES = [
'Tungro'
]
DESCRIPTIONS = {
'Healthy': {
'deskripsi': 'Kondisi tanaman sehat.',
'solusi': 'Lanjutkan perawatan normal dan pemantauan rutin.'
},
'Blast': {
'deskripsi': 'Blast adalah penyakit daun padi akibat jamur Pyricularia oryzae.',
'solusi': 'Lakukan pengendalian jamur dan semprot fungisida sesuai anjuran.'
},
'Blight': {
'deskripsi': 'Blight merupakan hawar daun bakteri.',
'solusi': 'Gunakan varietas tahan dan sanitasi lahan yang baik.'
},
'Tungro': {
'deskripsi': 'Tungro adalah penyakit virus yang ditularkan oleh wereng.',
'solusi': 'Lakukan pengendalian hama wereng dan gunakan benih sehat.'
}
}
# Load model
model = None
try:
model = load_model(MODEL_PATH)
print("Model berhasil dimuat")
print("Input Shape :", model.input_shape)
print("Output Shape:", model.output_shape)
print("Model Type :", type(model))
print('Model berhasil dimuat dari', MODEL_PATH)
except Exception as e:
print("Gagal load model:", e)
# Preprocessing gambar
def preprocess_image(path):
image = Image.open(path).convert('RGB')
@ -48,10 +63,9 @@ def preprocess_image(path):
return image_array
@app.route('/prediksi', methods=['POST'])
def prediksi():
# 3. Menerima request dengan key 'gambar'
if 'gambar' not in request.files:
return jsonify({
'error': 'Tidak ada gambar'
@ -74,80 +88,33 @@ def prediksi():
print("Gambar diterima:", filename)
if model is None:
return jsonify({
'error': 'Model gagal dimuat'
}), 500
return jsonify({'error': 'Model tidak tersedia. Pastikan file .h5 ada.'}), 500
try:
# Preprocessing
image_array = preprocess_image(filepath)
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}'
# Prediksi
predictions = model.predict(image_array)
print("\n===== HASIL PREDIKSI =====")
for i, nama in enumerate(CLASS_NAMES):
print(f"{nama}: {predictions[0][i] * 100:.2f}%")
predicted_index = int(np.argmax(predictions[0]))
label = CLASS_NAMES[predicted_index]
confidence_display = round(
float(predictions[0][predicted_index]) * 100,
2
)
print(f"HASIL AKHIR: {label}")
print(f"CONFIDENCE: {confidence_display}%")
# Semua probabilitas
all_predictions = {}
for i, nama in enumerate(CLASS_NAMES):
all_predictions[nama] = round(
float(predictions[0][i]) * 100,
2
)
# Deskripsi penyakit
deskripsi = {
'Blast': {
'deskripsi': 'Penyakit jamur yang menyebabkan bercak pada daun.',
'solusi': 'Gunakan fungisida dan varietas tahan penyakit.'
},
'Blight': {
'deskripsi': 'Hawar daun akibat bakteri.',
'solusi': 'Gunakan benih sehat dan kurangi kelembapan.'
},
'Healthy': {
'deskripsi': 'Daun padi sehat tanpa gejala penyakit.',
'solusi': 'Pertahankan perawatan tanaman.'
},
'Tungro': {
'deskripsi': 'Penyakit virus yang ditularkan wereng.',
'solusi': 'Kendalikan wereng dan gunakan varietas tahan.'
}
confidence_display = f'{confidence:.2f}%'
all_predictions = {
CLASS_NAMES[i]: float(predictions[0][i] * 100.0)
for i in range(len(CLASS_NAMES))
}
description_data = DESCRIPTIONS.get(label, {
'deskripsi': 'Tidak ada deskripsi tersedia.',
'solusi': 'Tidak ada solusi tersedia.'
})
return jsonify({
'penyakit': label,
'confidence': confidence_display,
'all_predictions': all_predictions,
'deskripsi': deskripsi[label]['deskripsi'],
'solusi': deskripsi[label]['solusi']
'deskripsi': description_data['deskripsi'],
'solusi': description_data['solusi']
})
except Exception as e:
@ -160,4 +127,7 @@ def prediksi():
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)

1
python_ai/requirements.txt
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@ -1,4 +1,5 @@
flask
flask-cors
tensorflow
pillow
numpy