app.py

import gradio as gr
from PIL import Image

def detect_deepfake(image):
    # TRANSFORM
    tensor = predict_transform(image).unsqueeze(0).to(device)

    # PREDICT
    model.eval()
    with torch.no_grad():
        outputs = model(tensor)
        probs = torch.softmax(outputs, dim=1)[0]
        pred_idx = torch.argmax(probs).item()

    labels = ["FAKE", "REAL"]
    prediction = labels[pred_idx]
    fake_prob = probs[0].item() * 100
    real_prob = probs[1].item() * 100

    result = {
        "FAKE 🔴": round(fake_prob / 100, 3),
        "REAL 🟢": round(real_prob / 100, 3)
    }

    verdict = f"⚠️ FAKE — {fake_prob:.2f}% confidence" if prediction == "FAKE" \
              else f"✅ REAL — {real_prob:.2f}% confidence"

    return verdict, result


# BUILD APP
app = gr.Interface(
    fn=detect_deepfake,
    inputs=gr.Image(type="pil", label="Upload Face Image"),
    outputs=[
        gr.Text(label="Verdict"),
        gr.Label(label="Confidence Scores")
    ],
    title="🕵️ Deepfake Detector",
    description="Upload any face image to detect if it's REAL or FAKE using EfficientNet-B4",
    examples=[
        [real_img],
        [fake_img]
    ],
    theme=gr.themes.Soft()
)

app.launch(share=True)