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app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Load an image classification pipeline
+classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+def classify_image(img, top_k=3):
+    if img is None:
+        return {"Error": 1.0}
+    results = classifier(img, top_k=top_k)
+    # Return as {label: score} for Gradio Label component
+    return {r["label"]: float(r["score"]) for r in results}
+
+# Gradio interface
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=[gr.Image(type="pil", label="Upload Image"), gr.Slider(1, 5, value=3, label="Top K Predictions")],
+    outputs=gr.Label(num_top_classes=5, label="Predictions"),
+    title="Image Classification App",
+    description="Upload an image and the model will predict the top objects in it."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=3.30
+transformers>=4.40
+torch>=1.13
+pillow