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import gradio as gr
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
# Load the model
model = load_model('./model.h5')
def detect_image(input_image):
img = Image.fromarray(input_image).resize((256, 256))
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
prediction = model.predict(img_array)[0][0]
probability_real = prediction * 100
probability_ai = (1 - prediction) * 100
if probability_real > probability_ai:
result = 'Input Image is Real'
confidence = probability_real
else:
result = 'Input Image is AI Generated'
confidence = probability_ai
return result, confidence
demo = gr.Interface(
fn=detect_image,
inputs=gr.Image(type="numpy", shape=(256, 256)),
outputs=[gr.Textbox(label="Result"), gr.Textbox(label="Confidence (%)")],
title="Deepfake Detection",
description="Upload an image to detect if it's real or AI generated."
)
# Deploy the interface on Gradio Hub
demo.launch(share=True)
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