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import torch
from PIL import Image
import requests
import gradio as gr
from transformers import AlignProcessor, AlignModel
processor = AlignProcessor.from_pretrained("kakaobrain/align-base")
model = AlignModel.from_pretrained("kakaobrain/align-base")
def get_image_alignment_probabilities(image, is_url):
candidate_labels = ["advertisement", "not an advertisement"]
# Load image from URL or locally
if is_url:
image = Image.open(requests.get(image, stream=True).raw).convert("RGB")
else:
image = Image.open(image).convert("RGB")
# Process inputs
inputs = processor(text=candidate_labels, images=image, return_tensors="pt")
# Compute outputs
with torch.no_grad():
outputs = model(**inputs)
# Extract logits per image
logits_per_image = outputs.logits_per_image
# Compute label probabilities using softmax
probs = logits_per_image.softmax(dim=1)
return {label: prob.item() for label, prob in zip(candidate_labels, probs[0])}
iface = gr.Interface(fn=get_image_alignment_probabilities,
inputs=[gr.Image(type='filepath', label="Upload Image"), "checkbox"],
outputs="label")
iface.launch()
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