added all required files for model

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+import numpy as np
+from transformers import AutoImageProcessor, SwinForImageClassification
+from torchvision import transforms
+
+# Define device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load Swin Transformer model with original classifier (1000 classes)
+swin_processor = AutoImageProcessor.from_pretrained("microsoft/swin-large-patch4-window12-384")
+model = SwinForImageClassification.from_pretrained("microsoft/swin-large-patch4-window12-384")
+
+# Modify input channels to 4 (RGB + mask)
+original_conv = model.swin.embeddings.patch_embeddings.projection
+new_conv = torch.nn.Conv2d(
+    in_channels=4,
+    out_channels=original_conv.out_channels,
+    kernel_size=original_conv.kernel_size,
+    stride=original_conv.stride,
+    padding=original_conv.padding,
+    bias=original_conv.bias is not None
+)
+with torch.no_grad():
+    new_conv.weight[:, :3] = original_conv.weight.clone()
+    new_conv.weight[:, 3] = original_conv.weight.mean(dim=1)
+model.swin.embeddings.patch_embeddings.projection = new_conv
+
+# Load the trained state dict from best_model.pth
+model.load_state_dict(torch.load("best_model.pth", map_location=device))
+model.to(device)
+model.eval()
+
+# Define transformations for Swin Transformer input
+swin_transform = transforms.Compose([
+    transforms.Resize((384, 384)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Define label mapping for the first 7 classes
+label_to_idx = {
+    'akiec': 0, 'bcc': 1, 'bkl': 2, 'df': 3,
+    'mel': 4, 'nv': 5, 'vasc': 6
+}
+idx_to_label = {v: k for k, v in label_to_idx.items()}
+
+# Prediction function
+def predict(image):
+    # Convert numpy array to PIL Image if necessary
+    if isinstance(image, np.ndarray):
+        image = Image.fromarray(image)
+    
+    # Process image for Swin Transformer
+    swin_image = swin_transform(image).to(device)
+    
+    # Generate a dummy mask channel (all zeros)
+    mask = torch.zeros(1, 384, 384).to(device)
+    
+    # Combine image and dummy mask
+    combined = torch.cat([swin_image, mask], dim=0).unsqueeze(0)  # Add batch dimension
+    
+    # Get prediction using only the first 7 logits
+    with torch.no_grad():
+        outputs = model(combined).logits[:, :7]  # Take only the first 7 classes
+        _, pred = torch.max(outputs, 1)
+        pred_label = idx_to_label[pred.item()]
+    
+    return pred_label
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Text(),
+    title="Skin Cancer Classification",
+    description="Upload an image to classify the type of skin cancer. Supported classes: akiec, bcc, bkl, df, mel, nv, vasc."
+)
+
+# Launch the interface
+iface.launch()

best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fce599d2bca7e9e9d7e4eeb0020787f429df5584f4595cf3376407b4117e0490
+size 791125887

requirements.txt

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+gradio
+torch
+torchvision
+transformers
+segmentation-models-pytorch
+pillow
+numpy