Spaces:
Sleeping
Sleeping
File size: 2,427 Bytes
835e1c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
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() |