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app.py

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+import gradio as gr
+import torch
+import torchvision.models as models
+from torchvision import transforms
+from torch import nn
+from PIL import Image
+
+transform = transforms.Compose([
+    transforms.Resize((128, 128)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+model = models.mobilenet_v3_large(pretrained=True)
+model.classifier[3] = nn.Linear(model.classifier[3].in_features, 2)
+model = model.to("cpu")
+model.load_state_dict(torch.load("cnn_model.pth", weights_only=True, map_location="cpu"))
+model.eval()
+
+label = ["nsfw", "safe"]
+
+def inference(image):
+    image = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(image)
+        output = torch.nn.functional.softmax(output, dim=1)
+
+        predicted_class = torch.argmax(output, dim=1).item()
+        score = output[0][predicted_class]
+
+    if label[predicted_class] == "nsfw":
+        output = f'Boneka ini terlalu seksi dan tidak aman dilihat anak kecil (NSFW) [{label[predicted_class]}:{score}]'
+    else:
+        output = f'Boneka ini aman (SAFE) [{label[predicted_class]}:{score}]'
+
+    return output
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            inputs = gr.Image(type="pil")
+        with gr.Column():
+            btn = gr.Button("Cek")
+            pred = gr.Text(label="Prediction")
+
+    btn.click(fn=inference, inputs=inputs, outputs=pred)
+
+        
+
+demo.queue().launch()

requirements.txt

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+gradio==3.34.0
+torchvision==0.18.1
+pillow==10.3.0
+torch==2.3.1