Create app.py

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline(
+    "zero-shot-image-classification",
+    model="google/siglip2-base-patch16-224",
+    device=-1
+)
+
+def classify_image(image, candidate_labels):
+    """
+    Takes an image and a comma-separated string of candidate labels,
+    and returns the classification scores.
+    """
+    labels = [label.strip() for label in candidate_labels.split(",") if label.strip()]
+
+    results = classifier(image, candidate_labels=labels)
+    return results[0]
+
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Textbox(value="cat, dog, bird, car, airplane", label="Candidate Labels (comma separated)")
+    ],
+    outputs=gr.JSON(label="Classification Results"),
+    title="SigLIP Zero-Shot Image Classifier",
+    description="This app uses the Google SigLIP model (siglip2-base-patch16-224) for zero-shot image classification on CPU. "
+                "Enter an image and a set of candidate labels to see the prediction scores."
+)
+
+if __name__ == "__main__":
+    iface.launch()