files

app.py

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+import torch
+from torch import nn
+from torchvision import transforms
+from torchvision.models import resnet50, ResNet50_Weights
+import gradio as gr
+
+title = "Cancer Detection"
+description = "Image classification with histopathologic images"
+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.pretrain_model = resnet50(weights=ResNet50_Weights.DEFAULT)
+        self.pretrain_model.eval()
+        for param in self.pretrain_model.parameters():
+            param.requires_grad = False       
+        self.pretrain_model.fc = nn.Sequential(
+            nn.Linear(self.pretrain_model.fc.in_features, 1024),
+            nn.ReLU(),
+            nn.Dropout(),
+            nn.Linear(1024,2)
+        )
+    def forward(self, input):
+        output=self.pretrain_model(input)
+        return output
+        
+model = ImageClassifier()
+model.load_state_dict(torch.load('comet-torch-model.pth'))
+
+def predict(inp):
+    image_transform = transforms.Compose([ transforms.Resize(size=(224,224)), transforms.ToTensor()])
+    labels = ['normal', 'cancer']
+    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=['image-1.jpg', 'image-2.jpg']).launch()

model-data_comet-torch-model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:470a1b9450a673fe0aaab6d15987ca23800fbfbdd22e8c9cf21d2555b91b1756
+size 102753277

requirements.txt

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+torch
+torchvision