Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+# Load pretrained image classification pipeline
+classifier = pipeline(
+    "image-classification",
+    model="google/vit-base-patch16-224"
+)
+
+def classify_image(image):
+    results = classifier(image)
+    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 an animal image"),
+    outputs=gr.Label(num_top_classes=5),
+    title="Animal Image Classifier",
+    description="Upload an image of an animal and see the predicted class.",
+    examples=[
+        "animal_images/cat.jpg",
+        "animal_images/dog.jpg",
+        "animal_images/bird.jpg"
+    ]
+)
+
+demo.launch()