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Browse files- app.py +55 -0
- best_model.pth +3 -0
app.py
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import torch
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from torchvision import models, transforms
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from PIL import Image
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import gradio as gr
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# Mendefinisikan nama kelas
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class_names = [
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"calculus",
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"caries",
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"gingivitis",
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"hypodontia",
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"mouth_ulcer",
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"tooth_discoloration"
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]
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# Mengatur jumlah kelas
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num_classes = len(class_names)
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# Membuat dan mengkonfigurasi model
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model = models.resnet50(pretrained=False)
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model.fc = torch.nn.Linear(model.fc.in_features, num_classes)
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# Memuat bobot model (sesuaikan path jika diperlukan)
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model.load_state_dict(torch.load('best_model.pth', map_location=torch.device('cpu')))
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model.eval()
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# Mengatur transformasi preprocessing
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preprocess = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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# Fungsi untuk melakukan prediksi
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def predict_image(image, model, preprocess, class_names):
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processed_image = preprocess(image).unsqueeze(0)
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with torch.no_grad():
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outputs = model(processed_image)
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_, predicted = torch.max(outputs, 1)
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predicted_class = class_names[predicted.item()]
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return predicted_class
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# Membuat interface Gradio
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iface = gr.Interface(
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fn=predict_image,
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inputs=[gr.inputs.Image(type='pil')],
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outputs=gr.outputs.Label(num_top_classes=1),
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title="Klasifikasi Gambar Medis",
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description="Upload gambar untuk memprediksi kelasnya."
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)
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# Menjalankan aplikasi Gradio
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iface.launch()
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best_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:b10fd8542b9770dd45be680bf393e1406d2460a0a1a4e6010ec6e0f4ca757821
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size 94399870
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