initial commit

app.py

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+import torch
+from torchvision.models import resnet50
+from torchvision import transforms
+from PIL import Image
+import gradio as gr
+
+# Load model
+model = resnet50(weights=None)
+model.fc = torch.nn.Linear(model.fc.in_features, 5)
+model.load_state_dict(torch.load("resnet50_dr.pth", map_location="cpu"))
+model.eval()
+
+class_names = ["No DR", "Mild", "Moderate", "Severe", "Proliferative DR"]
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+
+def predict(image):
+    image = image.convert("RGB")
+    img_tensor = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img_tensor)
+        _, predicted = torch.max(outputs, 1)
+        return class_names[predicted.item()]
+
+gr.Interface(fn=predict, inputs="image", outputs="text").launch()

requirements.txt

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+torch
+torchvision
+gradio
+Pillow

resnet50_diabretino.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8392eed4552b9f30fedc3eb221a62dfb9d64687341e91bfad5875839b4e03dd4
+size 94392002