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app.py

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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()

requirements.txt

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+tensorflow>=2.9.0
+mtcnn
+opencv-python
+numpy
+pandas
+gradio
+huggingface_hub