import gradio as gr import cv2 from mtcnn.mtcnn import MTCNN import tensorflow as tf import tensorflow_addons import numpy as np import os import zipfile local_zip = "/FINAL-EFFICIENTNETV2-B0.zip" zip_ref = zipfile.ZipFile(local_zip, 'r') zip_ref.extractall('/FINAL-EFFICIENTNETV2-B0') zip_ref.close() detector = MTCNN() model = tf.keras.models.load_model("/FINAL-EFFICIENTNETV2-B0") def deepfakespredict(input_img): face = detector.detect_faces(input_img) text ="" if len(face) > 0: x, y, width, height = face[0]['box'] x2, y2 = x + width, y + height cv2.rectangle(input_img, (x, y), (x2, y2), (0, 255, 0), 2) face_image = input_img[y:y2, x:x2] face_image2 = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB) face_image3 = cv2.resize(face_image2, (224, 224)) face_image4 = face_image3/255 pred = model.predict(np.expand_dims(face_image4, axis=0))[0] if pred[1] >= 0.6: text = "The image is fake." elif pred[0] >= 0.6: text = "The image is real." else: text = "The image might be real or fake." # if pred[1] >= 0.5: # text = "The image is fake." # else: # text = "The image is real." else: text = "Face is not detected in the image." return pred, text, input_img title="EfficientNetV2 Deepfakes Image Detector" description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. To use it, simply upload your image, or click one of the examples to load them." examples = [] demo = gr.Interface(deepfakespredict, inputs = ["image"], outputs=["text","text","image"], title=title, description=description, examples=examples ) demo.launch()