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from tensorflow.keras.models import load_model # type: ignore
from tensorflow.keras.preprocessing import image as keras_image # type: ignore
import numpy as np
from PIL import Image
import io
import os
import gdown
# === Path model lokal ===
model_path = 'saved_model_palm_disease.keras'
# === Unduh model dari Google Drive jika belum ada ===
if not os.path.exists(model_path):
url = 'https://drive.google.com/uc?id=1g-QPUIsySVm1oBl0KXpKKlxe7x_JPe7B'
gdown.download(url, model_path, quiet=False)
# === Load model hanya sekali ===
model = load_model(model_path)
# === Label urutan class_name (dari training) ===
labels = ['Boron Excess', 'Ganoderma', 'Healthy', 'Scale insect']
# === Fungsi preprocessing gambar ===
def preprocess_image(image_bytes):
img = Image.open(io.BytesIO(image_bytes)).convert("RGB").resize((224, 224))
img_array = keras_image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
return img_array
# === Fungsi prediksi utama ===
def predict_image(image_bytes):
img_array = preprocess_image(image_bytes)
predictions = model.predict(img_array)
class_index = int(np.argmax(predictions))
confidence = float(np.max(predictions))
return {
'class': labels[class_index],
'confidence': confidence
}
# === Fungsi handler yang dipanggil Hugging Face API ===
def handler(inputs):
try:
# Input berupa base64 atau byte dari gambar
image_bytes = inputs['inputs']
# Jika data berupa string base64, ubah ke byte
if isinstance(image_bytes, str):
import base64
image_bytes = base64.b64decode(image_bytes)
result = predict_image(image_bytes)
return result
except Exception as e:
return {"error": str(e)}
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