first commit

app.py

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+from transformers import pipeline
+import gradio as gr
+
+# Load your model from the Hugging Face Hub
+model_name = "bsvaz/landmark-classification-vit"  # Replace with your model's path on the Hub
+classifier = pipeline("image-classification", model=model_name)
+
+# Define the prediction function
+def classify_image(image):
+    results = classifier(image)
+    return {result["label"]: result["score"] for result in results}
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil"),
+    outputs="label",
+    title="Image Classification",
+    description="Upload an image to classify it."
+)
+
+iface.launch()

requirements.txt

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+transformers 
+gradio
+torch