app.py

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

# New model details
model_name = "prithivMLmods/deepfake-detector-model-v1"
processor = AutoImageProcessor.from_pretrained(model_name)
model = SiglipForImageClassification.from_pretrained(model_name)

def detect_deepfake(image):
    image = Image.fromarray(image).convert("RGB")
    inputs = processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
        probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
    id2label = { "0": "Fake", "1": "Real" }
    return { id2label[str(i)]: float(probs[i]) for i in range(2) }

demo = gr.Interface(
    fn=detect_deepfake,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(num_top_classes=2),
    title="Deepfake Detector",
    description="Upload an image; this model predicts Real vs Fake."
)

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