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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 | |
| cv2.rectangle(output_image, (x, y), (x2, y2), color, 2) | |
| cv2.putText(output_image, f"{label} ({avg:.2f})", (x, y - 10), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2) | |
| results.append(f"Face {idx+1}: {label} (Avg: {avg:.3f}, XCP: {pred_xcp:.3f}, EFF: {pred_eff:.3f})") | |
| return "\n".join(results), output_image | |
| # Gradio Interface | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="filepath", label="Upload Image"), # ✅ EDITED | |
| 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.", | |
| api_name="/predict" # ✅ ADDED | |
| ) | |
| interface.launch() | |