app.py

import cv2
import numpy as np 
import gradio as gr
from tensorflow.keras.utils import img_to_array
from tensorflow.keras.models import load_model
import os

model = load_model(r'deepfake_detection_model.h5')

def predict_image(img):
    
    x = img_to_array(img)

    x = cv2.resize(x, (256, 256), interpolation=cv2.INTER_AREA)
    
    x /= 255.0

    x = np.expand_dims(x, axis=0)

    prediction = np.argmax(model.predict(x), axis=1)
    
    if prediction == 0:
        return 'Fake Image'
    else:
        return 'Real Image'


# Define the Gradio Interface with the desired title and description

description_html = """

<p>Upload a face image to check if it's real or morphed with deepfake</p>
"""

# Define example images and their true labels for users to choose from

custom_css = """
div {background-color: whitesmoke;}
"""

gr.Interface(
    fn=predict_image,
    inputs='image',
    outputs='text',
    title="Deepfake Image Detection",
    description=description_html,
    allow_flagging='never'
).launch()