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1a572c9 edc6096 1a572c9 fccf0b8 1a572c9 8668cea | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | import io
import os
from dotenv import load_dotenv
import numpy as np
from flask import Flask, jsonify, render_template, request, send_from_directory
from PIL import Image
import tensorflow as tf
from tensorflow.keras.preprocessing import image
from model import Conv2DBatchNMaxP, Conv2DModel
from google import genai
# Cara membuat file .env:
# 1. Buat file bernama ".env" di folder proyek (sama dengan app.py).
# 2. Tambahkan baris: GENAI_API_KEY=your_api_key_here
# 3. Jangan commit .env ke repositori (tambahkan ke .gitignore).
load_dotenv()
GENAI_API_KEY = os.getenv('GENAI_API_KEY')
if not GENAI_API_KEY:
print('Peringatan: GENAI_API_KEY tidak ditemukan di environment')
client = genai.Client(api_key=GENAI_API_KEY)
CLASS_NAMES = [
'Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy',
'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew', 'Cherry_(including_sour)___healthy',
'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot', 'Corn_(maize)___Common_rust_',
'Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___healthy', 'Grape___Black_rot',
'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 'Grape___healthy',
'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot', 'Peach___healthy',
'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 'Potato___Early_blight',
'Potato___Late_blight', 'Potato___healthy', 'Raspberry___healthy', 'Soybean___healthy',
'Squash___Powdery_mildew', 'Strawberry___Leaf_scorch', 'Strawberry___healthy',
'Tomato___Bacterial_spot', 'Tomato___Early_blight', 'Tomato___Late_blight',
'Tomato___Leaf_Mold', 'Tomato___Septoria_leaf_spot',
'Tomato___Spider_mites Two-spotted_spider_mite', 'Tomato___Target_Spot',
'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus',
'Tomato___healthy', 'test'
]
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
MODEL_PATH = os.path.join(BASE_DIR, 'model.keras')
model = None
try:
model = tf.keras.models.load_model(
MODEL_PATH,
custom_objects={'Conv2DBatchNMaxP': Conv2DBatchNMaxP, 'Conv2DModel': Conv2DModel},
compile=False
)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
print(f"Model loaded: {MODEL_PATH}")
except Exception as e:
print(f"Failed to load model: {e}")
app = Flask(__name__)
def preprocess_image(image_file, target_size=(150, 150)):
img = Image.open(io.BytesIO(image_file.read())).convert('L').resize(target_size)
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) / 255.0
return img_array
@app.route('/', methods=['GET'])
def index():
try:
return render_template('index.html')
except Exception:
return send_from_directory(BASE_DIR, 'index.html')
@app.route('/predict', methods=['POST'])
def predict():
if model is None:
print(f"Model = {model}")
return jsonify({'error': 'Model belum dimuat'}), 500
if 'file' not in request.files or request.files['file'].filename == '':
return jsonify({'error': 'Tidak ada file gambar yang diunggah'}), 400
try:
processed_image = preprocess_image(request.files['file'])
predictions = model.predict(processed_image)
idx = np.argmax(predictions, axis=1)[0]
prompt = f"Tolong berikan penjelasan tentang penyakit tanaman {CLASS_NAMES[idx]}. Dan berikan solusinya"
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt
)
print(response.text)
return jsonify({
'predicted_class': CLASS_NAMES[idx],
'confidence': f"{predictions[0][idx] * 100:.2f}%",
'':response.text
})
except Exception as e:
print(f"Error: {e}")
return jsonify({'error': f'Gagal memproses gambar: {str(e)}'}), 500
if __name__ == '__main__':
# Wajib host 0.0.0.0 dan port 7860 untuk Hugging Face Space
app.run(host='0.0.0.0', port=7860, debug=False) |