Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the pipeline once
+pipe = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+# Define function for Gradio
+def classify_image(img):
+    results = pipe(img)
+    return {res["label"]: float(res["score"]) for res in results}
+
+# Gradio interface
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="filepath"),   # user uploads/selects image
+    outputs=gr.Label(num_top_classes=5), # show top 5 predictions with scores
+    title="Image Classification with ViT",
+    description="Upload an image (e.g., SHrek.png) and see predicted labels."
+)
+
+# Launch
+demo.launch()