app.py

import gradio as gr
import torch
from PIL import Image
from transformers import ViTForImageClassification, AutoImageProcessor

MODEL_ID = "hudaakram/FaceGuard-20ID-ViT"

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ViTForImageClassification.from_pretrained(MODEL_ID).to(device).eval()
processor = AutoImageProcessor.from_pretrained(MODEL_ID)

# readable class names from config (id2label maps idx -> celeb_id string)
id2label = {int(k): v for k, v in model.config.id2label.items()}

def predict_top5(img: Image.Image):
    if img is None:
        return {}
    img = img.convert("RGB")
    with torch.no_grad():
        enc = processor(images=img, return_tensors="pt").to(device)
        logits = model(pixel_values=enc["pixel_values"]).logits
        probs = torch.softmax(logits, dim=-1).squeeze(0).cpu().numpy()
    top5 = probs.argsort()[-5:][::-1]  # indices of Top-5 classes
    return {f"label {i} (celeb_id {id2label[i]})": float(probs[i]) for i in top5}

desc = (
    "FaceGuard — Vision Transformer (google/vit-base-patch16-224) fine-tuned on a 20-identity subset of CelebA.\n"
    "Upload a face crop to see the Top-5 predicted identities. (IDs are CelebA celeb_id integers.)"
)

demo = gr.Interface(
    fn=predict_top5,
    inputs=gr.Image(type="pil", label="Upload a face image"),
    outputs=gr.Label(num_top_classes=5, label="Top-5 identities"),
    title="FaceGuard – ViT (20 CelebA IDs)",
    description=desc,
    allow_flagging="never",
)

if __name__ == "__main__":
    demo.launch()