app.py

import streamlit as st
from PIL import Image, ImageOps
import keras
from matplotlib.image import imread
import numpy as np
import tensorflow as tf
from torchvision import transforms



def predict(image):
    image1=imread(image)
    img_ar = np.asarray(image1)
    img_ar_rs = img_ar/255.0
    shape = img_ar_rs.shape
    print(shape)
    img_ar_rs1 = img_ar_rs.reshape(-1,shape[0],shape[1],shape[2])
    image = tf.image.resize(img_ar_rs1, (32, 32)) 
    print(image.shape)
    pred = model.predict(image)
    if(pred > 0.5):
        return 'AI-generated'
    else:
        return 'Real'


st.title("Predicting whether an image is real or if it is AI-generated")
model = keras.models.load_model('model.keras')
uploaded_file = st.file_uploader("Upload a real or an AI-generated image",type=['png','jpg','jpeg'])


if uploaded_file is not None:
    st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
    if st.button("Check"):
        result = predict(uploaded_file)
        st.text(result)