app.py

#import tensorflow_hub as hub
import gradio as gr
import tensorflow as tf

# Load the model
model = tf.keras.models.load_model('keras_model.h5')

# Preprocess the image
def preprocess_image(image):
    resized_image = tf.image.resize(image, [224, 224])
    normalized_image = resized_image / 255.0
    return normalized_image

# Function to detect fake images using the model
def detect_fake_image(image):
    processed_image = preprocess_image(image)
    prediction = model.predict(tf.expand_dims(processed_image, 0))
    percentage = prediction[0][0] * 100
    if percentage > 50:
        result = f"Real with {percentage}% confidence"
    else:
        result = f"Fake with {100 - percentage}% confidence"
    return result

# Gradio Interface
iface = gr.Interface(
    fn=detect_fake_image,
    inputs="image",
    outputs="text",
    title="Fake Image Detector"
)

# Launch the interface
iface.launch()