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import cv2
import numpy as np
import gradio as gr
from mtcnn import MTCNN
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
# 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")
xcp_model = load_model(xcp_path)
eff_model = load_model(eff_path)
# Load face detector
detector = MTCNN()
def expand_box(x, y, w, h, scale=1.5, img_shape=None):
"""Expand face bounding box with margin."""
cx, cy = x + w // 2, y + h // 2
new_w, new_h = int(w * scale), int(h * scale)
x1 = max(0, cx - new_w // 2)
y1 = max(0, cy - new_h // 2)
x2 = min(img_shape[1], cx + new_w // 2)
y2 = min(img_shape[0], cy + new_h // 2)
return x1, y1, x2, y2
def predict(image):
faces = detector.detect_faces(image)
if not faces:
return "No face detected", image
output_image = image.copy()
results = []
for idx, face in enumerate(faces):
x, y, w, h = face['box']
# Add 20% margin while staying inside bounds
margin = 0.2
img_h, img_w = image.shape[:2]
x = max(0, int(x - w * margin))
y = max(0, int(y - h * margin))
w = int(w * (1 + 2 * margin))
h = int(h * (1 + 2 * margin))
x2 = min(img_w, x + w)
y2 = min(img_h, y + h)
face_img = image[y:y2, x:x2]
# Resize + preprocess
face_xcp = cv2.resize(face_img, (299, 299))
face_eff = cv2.resize(face_img, (224, 224))
xcp_tensor = xcp_pre(face_xcp.astype(np.float32))[np.newaxis, ...]
eff_tensor = eff_pre(face_eff.astype(np.float32))[np.newaxis, ...]
# Predictions
pred_xcp = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
pred_eff = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
avg = (pred_xcp + pred_eff) / 2
label = "Real" if avg > 0.41 else "Fake"
color = (0, 255, 0) if label == "Real" else (0, 0, 255)
# Annotate image with % instead of raw float
cv2.rectangle(output_image, (x, y), (x2, y2), color, 2)
cv2.putText(output_image, f"{label} ({avg * 100:.2f}%)", (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
results.append(
f"Face {idx+1}: {label} (Avg: {avg * 100:.2f}%, XCP: {pred_xcp * 100:.2f}%, EFF: {pred_eff * 100:.2f}%)"
)
return "\n".join(results), output_image
# Gradio Interface
interface = gr.Interface(
fn=predict,
inputs=gr.Image(type="numpy", label="Upload Image"),
outputs=[
gr.Textbox(label="Predictions"),
gr.Image(type="numpy", label="Annotated Image"),
],
title="Deepfake Detector (Multi-Face Ensemble)",
description="Detects all faces in an image and classifies each one as real or fake using Xception and EfficientNetB4 ensemble.",
)
interface.launch()
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