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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
# Load models
xcp_model = load_model("xception_model.h5")
eff_model = load_model("efficientnet_model.h5")
# 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 faces detected", image
results = []
annotated = image.copy()
for i, face in enumerate(faces):
x, y, w, h = face['box']
x, y, w, h = max(0, x), max(0, y), w, h
x1, y1, x2, y2 = expand_box(x, y, w, h, scale=1.6, img_shape=image.shape)
face_crop = image[y1:y2, x1:x2]
# Preprocess for each model
xcp_img = cv2.resize(face_crop, (299, 299))
eff_img = cv2.resize(face_crop, (224, 224))
xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...]
eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...]
xcp_pred = xcp_model.predict(xcp_tensor, verbose=0).flatten()[0]
eff_pred = eff_model.predict(eff_tensor, verbose=0).flatten()[0]
avg_pred = (xcp_pred + eff_pred) / 2
label = "Real" if avg_pred > 0.5 else "Fake"
results.append(
f"Face {i+1}: {label} (Avg: {avg_pred:.3f}, XCP: {xcp_pred:.3f}, EFF: {eff_pred:.3f})"
)
# Draw
color = (0, 255, 0) if label == "Real" else (255, 0, 0)
cv2.rectangle(annotated, (x1, y1), (x2, y2), color, 2)
cv2.putText(
annotated,
f"{label} ({avg_pred:.2f})",
(x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX,
0.6,
color,
2,
)
return "\n".join(results), annotated
# 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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