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import gradio as gr
from transformers import ViTImageProcessor, ViTForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/vit-mini-explicit-content" # Updated model path
model = ViTForImageClassification.from_pretrained(model_name)
processor = ViTImageProcessor.from_pretrained(model_name)
# Updated label mapping
labels = {
"0": "Anime Picture",
"1": "Enticing & Sensual",
"2": "Hentai",
"3": "Pornography",
"4": "Safe for Work"
}
def explicit_content_detection(image):
"""Predicts the type of content in the image."""
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()
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=explicit_content_detection,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="vit-mini-explicit-content",
description="Upload an image to classify whether it is anime, enticing & sensual, hentai, pornographic, or safe for work."
)
# Launch the app
if __name__ == "__main__":
iface.launch(share=True)
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