Added app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+
+# Load model and processor
+model = AutoModelForImageClassification.from_pretrained("shravvvv/SAG-ViT")
+processor = AutoImageProcessor.from_pretrained("shravvvv/SAG-ViT")
+
+# CIFAR-10 class labels
+class_labels = [
+    'airplane', 'automobile', 'bird', 'cat', 'deer',
+    'dog', 'frog', 'horse', 'ship', 'truck'
+]
+
+# Define prediction function
+def predict(image):
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class_idx = logits.argmax(-1).item()
+    return class_labels[predicted_class_idx]
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(type="pil"),
+    outputs=gr.outputs.Label(),
+    title="SAG-ViT Image Classifier",
+    description="Upload an image to classify it using the SAG-ViT model."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+numpy==1.26.4
+pandas==2.2.3
+matplotlib==3.7.5
+seaborn==0.12.2
+tqdm==4.66.4
+psutil==5.9.3
+pynvml==11.4.1
+scikit-learn==1.2.2
+torch==2.4.0
+torch-geometric==2.6.1
+torchvision==0.19.0
+networkx==3.3