Upload 3 files

app.py

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+import gradio as gr
+import torch
+import cnn
+from torchvision import transforms
+from PIL import Image
+
+transform = transforms.Compose([
+    transforms.Resize((128, 128)),
+    transforms.ToTensor()
+])
+
+model = cnn.CNN(2)
+model = model.to("cpu")
+model.load_state_dict(torch.load("cnn_model.pth", weights_only=True, map_location="cpu"))
+model.eval()
+
+label = ["Kucing", "Anjing"]
+
+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]
+
+    return f'Ini adalah {label[predicted_class]} dengan kecocokan sebesar {score * 100}'
+
+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()

cnn.py

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+from torch import nn
+
+class CNN(nn.Module):
+    def __init__(self, num_classes):
+        super(CNN, self).__init__()
+
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
+        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1)
+
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.bn1 = nn.BatchNorm2d(32)
+        self.bn2 = nn.BatchNorm2d(64)
+        self.bn3 = nn.BatchNorm2d(128)
+
+        self.fc1 = nn.Linear(128 * 16 * 16, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.fc3 = nn.Linear(256, 128)
+        self.fc4 = nn.Linear(128 ,num_classes)
+
+        self.dropout = nn.Dropout(0.5)
+
+    def forward(self, x):
+        x = nn.functional.relu(self.conv1(x))
+        x = self.bn1(x)
+        x = self.pool(x)
+
+        x = nn.functional.relu(self.conv2(x))
+        x = self.bn2(x)
+        x = self.pool(x)
+
+        x = nn.functional.relu(self.conv3(x))
+        x = self.bn3(x)
+        x = self.pool(x)
+
+        x = x.view(-1, 128 * 16 * 16)
+
+        x = nn.functional.relu(self.fc1(x))
+
+        x = nn.functional.relu(self.fc2(x))
+        x = self.dropout(x)
+
+        x = nn.functional.relu(self.fc3(x))
+        x = self.dropout(x)
+        x = self.fc4(x)
+        return x

cnn_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:981a38ba3f9444a5d72edef7526ac510dc593fe5c907e0bfb0a2a58ee43549a6
+size 68154689