Gradio app using Hub model

app.py

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+import gradio as gr
+import torch
+import timm
+from PIL import Image
+from torchvision import transforms
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+# Pascal VOC classes
+class_names = [
+    "aeroplane", "bicycle", "bird", "boat", "bottle",
+    "bus", "car", "cat", "chair", "cow",
+    "diningtable", "dog", "horse", "motorbike", "person",
+    "pottedplant", "sheep", "sofa", "train", "tvmonitor"
+]
+
+# 🧠 Load model from HF Hub
+REPO_ID = "fylex/swin-s3-base-pascal_test"  # 🔁 Update this
+MODEL_FILENAME = "model.safetensors"
+
+model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
+
+# Build and load model
+model = timm.create_model("swin_s3_base_224", pretrained=False, num_classes=len(class_names))
+state_dict = load_file(model_path)
+model.load_state_dict(state_dict)
+model.eval()
+
+# Preprocessing
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5]*3, [0.5]*3),
+])
+
+# Prediction function
+def predict(image):
+    img = transform(image).unsqueeze(0)
+    with torch.no_grad():
+        logits = model(img)
+        probs = torch.nn.functional.softmax(logits, dim=1)[0]
+    return {class_names[i]: float(probs[i]) for i in range(len(class_names))}
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=5),
+    title="Swin S3 Base - Pascal VOC Classifier",
+    description="A Swin Transformer model fine-tuned on Pascal VOC for multi-class image classification.",
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+torch
+timm
+gradio
+safetensors
+Pillow
+torchvision
+huggingface_hub