app.py

import gradio as gr
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing import image
import numpy as np
import io
import os
from PIL import Image
def main(img):
    model = keras.models.load_model('./ai_real_classifier.h5')

    # Save the uploaded image to a temporary file
    img_path = 'temp_image.jpg'
    Image.fromarray(img).save(img_path)

    img = image.load_img(img_path, target_size=(150, 150, 3))
    img_array = image.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array /= 255.0

    # Make predictions
    predictions = model.predict(img_array)

    confidence = 0.001

    # Interpret the predictions
    if predictions[0][0] > confidence:
        class_label = 'real'
    else:
        class_label = 'AI Generated'

    # Clean up the temporary image file
    os.remove(img_path)

    return "The image is classified as " + str(class_label) + " \n\n | Please note that this model is only a demonstration of how the A.I.R.S architecture works, we are working on better and more accurate models. If you would like to support us through a donation, you can visit freecs.org/donate"

demo = gr.Interface(fn=main, inputs="image", outputs="text", title="Artificial Image Recognition System", description="This model recognize whether an image is real or AI-generated. With the A.I.R.S architecture we aim to solve all the Deep Fake related problems. If you would like to support us through a donation, you can visit [freecs.org/donate](http://freecs.org/donate). Created by [gr](http://gr.freecs.org)  ")

demo.launch()