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('fake_real_face_classification_model.keras')

# Load the pre-trained face detection model with error handling
face_cascade = cv.CascadeClassifier('img_for_deepfake_detection\\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_path):
    # Preprocess the image
    img_array = preprocess_image(image_path)

    # Convert the image to grayscale
    gray_image = cv.cvtColor(cv.imread(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."

# Create the Gradio interface
demo = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="file", label="Upload Image"),
    outputs=gr.Textbox(label="Prediction"),
    title="DeepFake Image Detection",
    description="Upload an image and the model will classify it as real or fake.",
    theme="default",
    layout="vertical",
    css="""
        .gradio-container {
            font-family: 'Roboto', sans-serif;
        }
        .gradio-input, .gradio-output {
            border: 1px solid #ccc;
            border-radius: 4px;
            padding: 10px;
            font-size: 16px;
        }
        .gradio-button {
            background-color: #4CAF50;
            color: white;
            border: none;
            border-radius: 4px;
            padding: 10px 20px;
            font-size: 16px;
            cursor: pointer;
        }
    """
)

# Launch the Gradio app
demo.launch()