app.py

import gradio as gr
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
import numpy as np
from keras.models import load_model
import cv2 as cv

# Load the trained model
model = load_model('FaceAuthenticator.keras')

# Load the pre-trained face detection model with error handling
face_cascade = cv.CascadeClassifier('hass_face.xml')

# Define a function to preprocess the input image
def preprocess_image(image_path):
    img = cv.imread(image_path)
    img = cv.resize(img, (224, 224))
    img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
    img_array = np.expand_dims(img, axis=0)
    img_array = preprocess_input(img_array)
    return img_array

# Define a function to classify the input image
def classify_image(image_data):
    try:
        # Save the uploaded image temporarily
        temp_image_path = "temp_image.jpg"
        image_data.save(temp_image_path)

        # Preprocess the image
        img_array = preprocess_image(temp_image_path)

        # Convert the image to grayscale
        gray_image = cv.cvtColor(cv.imread(temp_image_path), cv.COLOR_BGR2GRAY)

        # Detect faces in the image
        faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

        # Check if any faces were detected
        if len(faces) == 0:
            return "No faces detected in the input image."
        else:
            # Make predictions
            prediction = model.predict(img_array)

            # Return the prediction
            if prediction[0][0] > 0.5:
                return "The image is classified as real."
            else:
                return "The image is classified as fake."
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Create the Gradio interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil", label="Upload Image"),
    outputs=gr.Textbox(label="Prediction"),
    title="DeepFake Detection for Facial images",
    description="Upload an Facial image and the model will classify it as real or fake.",
    theme="default",

)

# Launch the Gradio app
demo.launch()