File size: 1,106 Bytes
37d1200 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
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()
|