app.py

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

# ============================================================
# LOAD MODEL FROM HUGGING FACE HUB
# ============================================================

model_name = "prithivMLmods/Deepfake-Detect-Siglip2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
model.eval()

# ============================================================
# PREDICTION
# ============================================================

def predict(image):
    if image is None:
        return {"Error": "Upload an image"}

    if not isinstance(image, Image.Image):
        image = Image.fromarray(image)

    image = 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).squeeze().tolist()

    labels = model.config.id2label
    result = {labels[i]: round(probs[i], 4) for i in range(len(probs))}

    print(f"Result: {result}")
    return result

# ============================================================
# INTERFACE
# ============================================================

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=2),
    title="Deepfake Detection",
    description="Upload an image to detect if it's Real or Fake"
)

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