added files

app.py

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+import torch
+from torch import nn
+from torchvision import transforms
+import gradio as gr
+
+title = "PyTorch Cat vs Dog"
+description = "Classifying cats and dogs with Pytorch"
+article = "<p style='text-align: center'><a href='https://github.com/TirendazAcademy'>Github Repo</a></p>"
+
+# The model architecture
+class ImageClassifier(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv_layer_1 = nn.Sequential(
+          nn.Conv2d(3, 64, 3, padding=1),
+          nn.ReLU(),
+          nn.BatchNorm2d(64),
+          nn.MaxPool2d(2))
+        self.conv_layer_2 = nn.Sequential(
+          nn.Conv2d(64, 512, 3, padding=1),
+          nn.ReLU(),
+          nn.BatchNorm2d(512),
+          nn.MaxPool2d(2))
+        self.conv_layer_3 = nn.Sequential(
+          nn.Conv2d(512, 512, kernel_size=3, padding=1),
+          nn.ReLU(),
+          nn.BatchNorm2d(512),
+          nn.MaxPool2d(2)) 
+        self.classifier = nn.Sequential(
+          nn.Flatten(),
+           nn.Linear(in_features=512*3*3, out_features=2)
+        )
+    def forward(self, x: torch.Tensor):
+        x = self.conv_layer_1(x)
+        x = self.conv_layer_2(x)
+        x = self.conv_layer_3(x)
+        x = self.conv_layer_3(x)
+        x = self.conv_layer_3(x)
+        x = self.conv_layer_3(x)
+        x = self.classifier(x)
+        return x
+
+model = ImageClassifier()
+model.load_state_dict(torch.load('image_classifier.pth'))
+
+def predict(inp):
+    image_transform = transforms.Compose([ transforms.Resize(size=(224,224)), transforms.ToTensor()])
+    labels = ['cat', 'dog']
+    inp = image_transform(inp).unsqueeze(dim=0)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(inp))
+        confidences = {labels[i]: float(prediction.squeeze()[i]) for i in range(len(labels))}    
+    return confidences
+    
+gr.Interface(fn=predict, 
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=2),
+             title=title,
+             description=description,
+             article=article,
+             examples=['cat.jpg', 'dog.jpg']).launch()

image_classifier.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:472e52a33ecd629811329db47e8e95c7ccffbff0a36f9678571eea3faa172782
+size 10689971

requirements.txt

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+torch==1.10.0
+torchvision==0.11.0