app.py

import cv2
from tensorflow.keras.models import load_model
import gradio as gr
import tensorflow as tf
import cv2
import numpy as np
from tensorflow.keras.models import load_model





# Load the pre-trained model
new_model = load_model('imageclassifier.h5')

def classify_image(img):
    # Resize the image
    resize = tf.image.resize(img, (256, 256))
    
    # Preprocess the image and make prediction
    yhat = new_model.predict(np.expand_dims(resize / 255, 0))

    # Return the prediction result
    return "Real" if yhat > 0.5 else "Fake"

# Create a Gradio interface
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(),
    outputs="text",
    live=True,
      
)


# Launch the Gradio interface
iface.launch()