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import os
import cv2
import numpy as np
import gradio as gr
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.xception import preprocess_input as xcp_pre
from tensorflow.keras.applications.efficientnet import preprocess_input as eff_pre
from huggingface_hub import hf_hub_download
from mtcnn import MTCNN
# Download and load models
xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="xception_model.h5")
eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector", filename="efficientnet_model.h5")
xcp_model = load_model(xcp_path)
eff_model = load_model(eff_path)
# Face detector
detector = MTCNN()
def extract_face(image):
faces = detector.detect_faces(image)
if not faces:
return None
x, y, w, h = faces[0]['box']
x, y = max(0, x), max(0, y)
return image[y:y+h, x:x+w]
def predict(image):
face = extract_face(image)
if face is None:
return "No face detected", None
# Xception
xcp_img = cv2.resize(face, (299, 299))
xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
xcp_pred = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
# EfficientNet
eff_img = cv2.resize(face, (224, 224))
eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]
eff_pred = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
# Ensemble
avg_pred = (xcp_pred + eff_pred) / 2
label = "Real" if avg_pred > 0.5 else "Fake"
# Log probabilities
print(f"Xception: {xcp_pred:.4f}, EfficientNetB4: {eff_pred:.4f}, Ensemble Avg: {avg_pred:.4f}")
# Return label with confidence
result = f"{label} (Avg: {avg_pred:.3f}, XCP: {xcp_pred:.3f}, EFF: {eff_pred:.3f})"
return result, face
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy", label="Upload Image"),
outputs=[
gr.Label(label="Prediction"),
gr.Image(type="numpy", label="Detected Face")
],
title="Deepfake Image Detector (Ensemble: Xception + EfficientNetB4)",
description="Upload an image. The model detects the face, classifies it as real or fake using an ensemble of Xception and EfficientNetB4."
)
interface.launch()
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