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import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def image_classification(image):
"""Classifies an image as AI-generated, deepfake, or real."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = model.config.id2label
predictions = {labels[i]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=image_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Classification Result"),
title="AI vs Deepfake vs Real Image Classification",
description="Upload an image to determine whether it is AI-generated, a deepfake, or a real image."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
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