Update app.py

app.py

@@ -3,7 +3,6 @@ from torchvision import models, transforms
 from PIL import Image
 import gradio as gr
 
-# Mendefinisikan nama kelas
 class_names = [
     "calculus",
     "caries",
@@ -13,26 +12,21 @@ class_names = [
     "tooth_discoloration"
 ]
 
-# Mengatur jumlah kelas
 num_classes = len(class_names)
 
-# Membuat dan mengkonfigurasi model
-model = models.resnet50(pretrained=False)
+model = models.resnet50(weights=None)
 model.fc = torch.nn.Linear(model.fc.in_features, num_classes)
 
-# Memuat bobot model (sesuaikan path jika diperlukan)
 model.load_state_dict(torch.load('best_model.pth', map_location=torch.device('cpu')))
 model.eval()
 
-# Mengatur transformasi preprocessing
 preprocess = transforms.Compose([
     transforms.Resize((224, 224)),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
 ])
 
-# Fungsi untuk melakukan prediksi
-def predict_image(image, model, preprocess, class_names):
+def predict_image(image, model=model, preprocess=preprocess, class_names=class_names):
     processed_image = preprocess(image).unsqueeze(0)
 
     with torch.no_grad():
@@ -42,14 +36,12 @@ def predict_image(image, model, preprocess, class_names):
 
     return predicted_class
 
-# Membuat interface Gradio
 iface = gr.Interface(
     fn=predict_image, 
-    inputs=[gr.inputs.Image(type='pil')],
+    inputs=gr.inputs.Image(type='pil'),
     outputs=gr.outputs.Label(num_top_classes=1),
     title="Klasifikasi Gambar Medis",
     description="Upload gambar untuk memprediksi kelasnya."
 )
 
-# Menjalankan aplikasi Gradio
 iface.launch()