trying other model again

app.py

@@ -4,7 +4,7 @@ import numpy as np
 from PIL import Image
 import io
 import sys
-from tensorflow.keras.applications.densenet import preprocess_input
+from tensorflow.keras.applications.vgg16 import preprocess_input
 
 # 1. Inisialisasi Aplikasi
 app = FastAPI(title="Ashoka Hipospadia Classifier API")
@@ -12,7 +12,7 @@ app = FastAPI(title="Ashoka Hipospadia Classifier API")
 # 2. Load Model
 print("Sedang memuat model...")
 try:
-    model = tf.keras.models.load_model('cnn_kfold_best_model_v2.h5')
+    model = tf.keras.models.load_model('model_vgg16_final.h5')
     print("Model berhasil dimuat!")
 except Exception as e:
     print(f"Error memuat model: {e}")
@@ -24,10 +24,10 @@ class_names = ['normal', 'buried']
 # 3. Fungsi Preprocessing
 def prepare_image(image_bytes):
     """
-    Preprocessing gambar untuk model ResNet50
+    Preprocessing gambar untuk model VGG16
     - Konversi ke RGB (3 channel)
     - Resize ke 224x224
-    - Preprocessing ResNet50
+    - Preprocessing VGG16
     """
     try:
         img = Image.open(io.BytesIO(image_bytes))
@@ -35,7 +35,7 @@ def prepare_image(image_bytes):
         # Paksa ubah ke RGB agar PNG transparan tidak error
         img = img.convert("RGB")
         
-        # Resize ke ukuran input model (224x224 untuk ResNet50)
+        # Resize ke ukuran input model (224x224 untuk VGG16)
         img = img.resize((224, 224))
         
         # Convert ke numpy array
@@ -44,7 +44,7 @@ def prepare_image(image_bytes):
         # Tambah batch dimension
         img_array = np.expand_dims(img_array, axis=0)
         
-        # Preprocessing ResNet50 (HARUS sama dengan training!)
+        # Preprocessing VGG16 (HARUS sama dengan training!)
         img_array = preprocess_input(img_array)
         
         return img_array
@@ -104,7 +104,7 @@ def home():
     """Endpoint root untuk testing API"""
     return {
         "message": "Ashoka Hipospadia Classifier API Online! 🚀\n",
-        "model": "DenseNet Binary Classification\n",
+        "model": "VGG16 Binary Classification\n",
         "classes": class_names
     }
 

cnn_kfold_best_model_v2.h5 → model_vgg16.h5

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8a40cfa4dc21f325ef160b7be42ee5a4533088d1cb1fa17baf0cba72ff3bd634
-size 249086792
+oid sha256:fcdb25632467509be0a2ffa43e7c9df0d3021b746d582157decbd0b5e0b01d68
+size 60534752