Create app.py

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the image classification pipeline
+classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
+
+def predict(image):
+    if image is None:
+        return None
+    predictions = classifier(image)
+    return {p["label"]: p["score"] for p in predictions}
+
+# Updated examples list to use cat.jpeg instead of cat.png
+examples = [
+    "animal_images/hippo.png",
+    "animal_images/jaguar.png",
+    "animal_images/toucan.png",
+    "animal_images/sloth.png",
+    "animal_images/cat.jpeg",  # Changed to jpeg
+    "animal_images/frog.png",
+    "animal_images/turtle.png"
+]
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(
+        type="pil", 
+        label="Input Image", 
+        interactive=True,
+        height=None
+    ),
+    outputs=gr.Label(num_top_classes=3, label="Predictions"),
+    examples=examples,
+    title="Animal Classifier",
+    description="Upload an image of an animal."
+)
+
+if __name__ == "__main__":
+    demo.launch()