Update prediction.py

prediction.py

@@ -1,51 +1,50 @@
 import streamlit as st
 import numpy as np
-import cv2
 from PIL import Image
 import tensorflow as tf
-import os
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import KerasLayer
 
-# Load model yang telah dilatih (ganti 'model.h5' dengan file model kamu)
-model = load_model('model_aug.keras', custom_objects={'KerasLayer': KerasLayer})
+# Load model sekali saat aplikasi di-start
+@st.cache_resource
+def load_skin_model():
+    return load_model('model_aug.keras', custom_objects={'KerasLayer': KerasLayer})
 
-# Kelas target (ubah sesuai dengan model kamu)
+model = load_skin_model()
+
+# Kelas target
 CLASS_NAMES = ['oily', 'dry', 'normal']
 
+def preprocess_image(image):
+    """Preprocess image to match model input."""
+    img_resized = tf.image.resize(image, [220, 220])  # Resize gambar
+    img_normalized = img_resized / 255.0  # Normalisasi
+    return tf.expand_dims(img_normalized, axis=0)  # Tambah batch dimension
+
 def run():
-    # Set judul aplikasi
     st.title('Skin Type Classification')
     st.write('---')
-
-    # Tambahkan deskripsi
     st.write('Upload an image of skin, and this app will predict the skin type.')
 
-    # Gambar ilustrasi (opsional)
-    link_gambar = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQntnqn33t_1jWqaFszEgEdMCQjGNNtWLxv8A&s'  # Ganti dengan URL gambar
+    link_gambar = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQntnqn33t_1jWqaFszEgEdMCQjGNNtWLxv8A&s'
     st.image(link_gambar, caption='Know your skin type!', use_container_width=True)
 
-    # Form untuk upload gambar
     uploaded_file = st.file_uploader('Upload an image:', type=['jpg', 'png', 'jpeg'])
 
     if uploaded_file is not None:
-        # Tampilkan gambar yang di-upload
         image = Image.open(uploaded_file)
         st.image(image, caption='Uploaded Image', use_column_width=True)
 
-        # Convert gambar ke format yang diterima model
         img_array = np.array(image)
-        img_resized = cv2.resize(img_array, (220, 220))  # Ubah ukuran ke input model
-        img_normalized = img_resized / 255.0  # Normalisasi
-        img_expanded = np.expand_dims(img_normalized, axis=0)  # Tambahkan batch dimension
+        img_tensor = preprocess_image(img_array)
 
         # Prediksi menggunakan model
-        prediction = model.predict(img_expanded)
+        prediction = model.predict(img_tensor)
         predicted_class = CLASS_NAMES[np.argmax(prediction)]
         confidence = np.max(prediction) * 100
 
-        # Tampilkan hasil prediksi
         st.write(f"### Predicted Skin Type: {predicted_class}")
         st.write(f"### Confidence: {confidence:.2f}%")
-
     else:
         st.write('Please upload an image to get a prediction.')