app.py

import gradio as gr
from transformers import pipeline
from PIL import Image

# Load the deepfake detection model
deepfake_detector = pipeline("image-classification", model="Wvolf/ViT_Deepfake_Detection")

def detect_deepfake(image):
    result = deepfake_detector(image)
    prediction = max(result, key=lambda x: x['score'])
    label = prediction['label'].lower()
    confidence = prediction['score'] * 100

    # Adjusted threshold logic
    if label == "fake" and confidence > 75:
        verdict = f"🚨 Deepfake Detected! FAKE with {confidence:.2f}% confidence."
    elif label == "real" and confidence > 75:
        verdict = f"✅ REAL image with {confidence:.2f}% confidence."
    else:
        verdict = f"⚠️ Uncertain — low confidence: {label.upper()} with {confidence:.2f}% confidence."

    return verdict

# Gradio UI — upload only (no webcam)
iface = gr.Interface(
    fn=detect_deepfake,
    inputs=gr.Image(type="pil", label="Upload an Image", sources=["upload"]),
    outputs="text",
    title="Deepfake Confidence Score Detector",
    description="Upload an image to check if it's a deepfake. Powered by Hugging Face Transformers."
)


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