adding app.py, model + requirements

app.py

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+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from medmnist import INFO
+from model import load_model
+from PIL import Image
+
+# Class names
+info = INFO["dermamnist"]
+class_names = list(info["label"].values())
+
+# Load model
+model = load_model()
+
+# Transforms (match training)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5], std=[0.5])
+])
+
+# Prediction function
+def predict(image):
+    image = image.convert("RGB")
+    input_tensor = transform(image).unsqueeze(0)  # Add batch dim
+
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        probs = torch.softmax(outputs, dim=1).squeeze().numpy()
+
+    return {class_names[i]: float(probs[i]) for i in range(len(class_names))}
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Skin Disease Classifier",
+    description="Upload a skin image and the model will predict potential skin cancer(melanoma), tumor or moles using EfficientNet-B2 fine-tuned on DermMNIST."
+)
+
+if __name__ == "__main__":
+    demo.launch()

model/effnetb2_dermamnist.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e75206d42c74874ac260b367abb4e02c73381ab7f16cc0e11bdd323faabf17c
+size 31302714

requirements.txt

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+torch
+torchvision
+gradio
+medmnist
+scikit-learn
+matplotlib
+