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app.py

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+import torch
+from torchvision import models, transforms
+from PIL import Image
+import gradio as gr
+
+# Mendefinisikan nama kelas
+class_names = [
+    "calculus",
+    "caries",
+    "gingivitis",
+    "hypodontia",
+    "mouth_ulcer",
+    "tooth_discoloration"
+]
+
+# Mengatur jumlah kelas
+num_classes = len(class_names)
+
+# Membuat dan mengkonfigurasi model
+model = models.resnet50(pretrained=False)
+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):
+    processed_image = preprocess(image).unsqueeze(0)
+
+    with torch.no_grad():
+        outputs = model(processed_image)
+        _, predicted = torch.max(outputs, 1)
+        predicted_class = class_names[predicted.item()]
+
+    return predicted_class
+
+# Membuat interface Gradio
+iface = gr.Interface(
+    fn=predict_image, 
+    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()

best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b10fd8542b9770dd45be680bf393e1406d2460a0a1a4e6010ec6e0f4ca757821
+size 94399870