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import os
import numpy as np
import cv2
from mtcnn import MTCNN
from PIL import Image
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 tensorflow.keras.preprocessing.image import img_to_array
from huggingface_hub import hf_hub_download
# Load models
xcp_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector_final", filename="xception_model.h5")
eff_path = hf_hub_download(repo_id="Zeyadd-Mostaffa/deepfake-image-detector_final", filename="efficientnet_model.h5")
model_xcp = load_model(xcp_path)
model_eff = load_model(eff_path)
# Face detector
detector = MTCNN()
# Prediction function
def predict_image(image_path):
img = cv2.imread(image_path)
if img is None:
return {"error": "Image could not be loaded"}
results = []
faces = detector.detect_faces(img)
# === Single or no face ===
if len(faces) <= 1:
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_xcp = xcp_pre(np.expand_dims(cv2.resize(img_rgb, (299, 299)), axis=0))
img_eff = eff_pre(np.expand_dims(cv2.resize(img_rgb, (224, 224)), axis=0))
xcp_pred = model_xcp.predict(img_xcp)[0][0]
eff_pred = model_eff.predict(img_eff)[0][0]
final_score = (xcp_pred + eff_pred) / 2
label = "REAL" if final_score > 0.5 else "FAKE"
results.append({"face_id": "whole image", "label": label, "score": round(float(final_score), 3)})
else:
for idx, face in enumerate(faces):
x, y, w, h = face['box']
x, y = max(0, x), max(0, y)
cropped = img[y:y+h, x:x+w]
if cropped.shape[0] < 60 or cropped.shape[1] < 60:
continue
face_rgb = cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB)
img_xcp = xcp_pre(np.expand_dims(cv2.resize(face_rgb, (299, 299)), axis=0))
img_eff = eff_pre(np.expand_dims(cv2.resize(face_rgb, (224, 224)), axis=0))
xcp_pred = model_xcp.predict(img_xcp)[0][0]
eff_pred = model_eff.predict(img_eff)[0][0]
final_score = (xcp_pred + eff_pred) / 2
label = "REAL" if final_score > 0.5 else "FAKE"
results.append({"face_id": f"face_{idx+1}", "label": label, "score": round(float(final_score), 3)})
return results