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| import os | |
| import cv2 | |
| import numpy as np | |
| import gradio as gr | |
| import tensorflow as tf | |
| from mtcnn import MTCNN | |
| from huggingface_hub import hf_hub_download | |
| 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 from Hugging Face Hub | |
| # --------------------------------------------------------- | |
| 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) | |
| # --------------------------------------------------------- | |
| # Face Detection | |
| # --------------------------------------------------------- | |
| detector = MTCNN() | |
| def extract_faces(image): | |
| faces = detector.detect_faces(image) | |
| if not faces: | |
| return [] | |
| results = [] | |
| for i, face in enumerate(faces): | |
| x, y, w, h = face['box'] | |
| x, y = max(0, x), max(0, y) | |
| cropped = image[y:y+h, x:x+w] | |
| if cropped.shape[0] >= 60 and cropped.shape[1] >= 60: | |
| results.append((cropped, (x, y, w, h))) | |
| return results | |
| # --------------------------------------------------------- | |
| # Inference Function | |
| # --------------------------------------------------------- | |
| def predict_faces(image): | |
| faces = extract_faces(image) | |
| if not faces: | |
| return "No faces detected", None | |
| annotated = image.copy() | |
| results = [] | |
| for i, (face, (x, y, w, h)) in enumerate(faces): | |
| # Preprocess | |
| xcp_img = cv2.resize(face, (299, 299)) | |
| eff_img = cv2.resize(face, (224, 224)) | |
| xcp_tensor = xcp_pre(xcp_img.astype(np.float32))[np.newaxis, ...] | |
| eff_tensor = eff_pre(eff_img.astype(np.float32))[np.newaxis, ...] | |
| # Predict | |
| 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" | |
| confidence = f"{avg_pred:.2f}" | |
| # Annotate | |
| color = (0, 255, 0) if label == "Real" else (0, 0, 255) | |
| cv2.rectangle(annotated, (x, y), (x + w, y + h), color, 2) | |
| cv2.putText(annotated, f"{label} ({confidence})", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2) | |
| results.append(f"Face {i+1}: {label} (Avg: {avg_pred:.3f}, XCP: {xcp_pred:.3f}, EFF: {eff_pred:.3f})") | |
| return "\n".join(results), annotated | |
| # --------------------------------------------------------- | |
| # Gradio Interface | |
| # --------------------------------------------------------- | |
| interface = gr.Interface( | |
| fn=predict_faces, | |
| 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="This model detects all faces in an image and classifies each one as real or fake using Xception and EfficientNetB4 ensemble." | |
| ) | |
| interface.launch() | |